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2705 commits
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devin-ai-integration[bot]
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d81472f196
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fix(model_prices): registry audit 2026-10-17, Perplexity decider v1.2 rows, Azure Mistral and Vertex Gemini deprecation dates (#47114)
* fix(model_prices): registry audit 2026-10-17, Azure Mistral and Vertex Gemini deprecation dates Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * feat(model_prices): add Perplexity pplx-decider v1.2 and flash v1.2 rows with latest aliases Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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litellm-stampbot
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dd8b2853a0
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feat(deepinfra): add DeepSeek-V4.1-Flash and DeepSeek-V4.1-Flash-1017 to the cost map (#47132)
* feat(deepinfra): add DeepSeek-V4.1-Flash and DeepSeek-V4.1-Flash-1017 to the cost map * fix(deepinfra): price DeepSeek-V4.1-Flash rows at the current 30% promo rate --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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litellm-stampbot
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3ade8288da
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feat(pricing): backfill github_copilot prices from the Copilot pricing table (#47085)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
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2c5c9b8d84
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fix(bedrock): use native structured output on Haiku 5.5 and Sonnet 5.5, and fix Fable 5 and Sonnet 4.6 schema 400s (#46870)
* fix(bedrock): use native structured output on every Claude model whose Bedrock profiles accept it Bedrock accepts outputConfig structured output on every reachable profile of Claude Opus 4.7, 4.8, 5, 5.5, Sonnet 5, 5.5, Haiku 5.5, and Fable 5.1, so json_schema now goes out natively there instead of as a forced tool, which kept Bedrock from returning adaptive thinking. Fable 5 rejects outputConfig, so its keys go back to the tool path. GovCloud keys keep their flags, and the drift tests let GovCloud lag its commercial base but never lead it. * fix(bedrock): clean Claude schemas for native structured output and keep recursive schemas on the old path Bedrock native output rejects minimum/maximum, minItems, uniqueItems, oneOf, additionalProperties true, and recursive or unresolved $ref, all of which the forced tool accepted. Claude schemas on the Bedrock native paths now go through the cleaner the Anthropic native path already uses, and a schema with a recursive or unresolved $ref keeps the forced tool, inline, or drop path it had. * fix(bedrock): keep older installs on the json tool with a Bedrock-only native output key Older installs fetch the model map from main, so flipping supports_native_structured_output moved them onto native output with their old schema handling. The new supports_bedrock_native_structured_output key is ignored by older code. Schemas native output cannot hold (recursive refs, open objects such as dict fields) keep the json tool on the new key, models on the old key keep their current behavior for those shapes, and the Anthropic API, Vertex AI, and Azure keep their current behavior. * chore(cost-map): regenerate the model prices schema for the Bedrock native output key * fix(bedrock): keep native output to profiles every account accepts and route schemas Bedrock rejects to the json tool Only Opus 5.5, Sonnet 5.5, Haiku 5.5 and Fable 5.1 keep the Bedrock native output key, since Opus 4.8 and Sonnet 5 answer 400 on some accounts and Opus 5 garbles escaped characters. Lookaround patterns and string formats outside Bedrock's list now keep the json tool, objects typed as a union with null are closed, and the cleaner keeps minItems of 0 or 1. * fix(bedrock): keep Opus 5.5 and Fable 5.1 on the json tool, since native output stops their adaptive thinking * fix(bedrock): keep shared $defs in native schemas and send refused schemas to the json tool on every native model Native output now keeps $defs instead of inlining each use, so a submodel reused across a schema counts once against Bedrock's optional and union limits. Schemas native output cannot hold use the json tool on Converse and Invoke chat for both native keys, and minItems is only kept when it is an integer 0 or 1 * fix(bedrock): keep the json tool unless thinking is requested and bound native output schemas The Bedrock native output key now applies only when the call asks for thinking, so calls without thinking keep the json tool as before. Schemas past Bedrock's documented union and optional counts, or past a grammar size estimate fitted to live probes, keep the json tool on both map keys. Open objects keep the json tool on the Anthropic API, Vertex AI and Azure AI, where native output returned them empty. The $ref check no longer expands $defs, so doubling definitions no longer stall the request. * fix(bedrock): resolve root-path $defs refs so nested Pydantic models keep native output on Invoke * test(bedrock): cover native structured output and json tool routing through the proxy Integration cells drive the proxy against a scripted Bedrock peer that enforces Bedrock's structured output schema rules, covering thinking with native output on Converse, Converse streaming, Invoke, /v1/responses and /v1/messages, the deployment opt-in, Fable 5, schema cleaning on every native entry, open objects, and malformed schemas. Vertex, Anthropic and Azure AI cells cover open objects on native-capable models and the cleaner's minItems and nullable object handling. The unit table now covers reasoning_effort none and disabled thinking * fix(bedrock): keep native output for numeric formats and wide old-key schemas, and keep thinking in json mode Check string formats only on nodes that can be strings, apply the 64-field cap only to the Bedrock-only key, carry thinking blocks into the json mode message, and send malformed json tool schemas as object schemas. * fix(anthropic): keep native output for dict fields unless the json tool can be forced * fix(anthropic): keep native output for dict fields when the caller sends tools * test(bedrock): close the dangerous-tool-use test schema, since Bedrock native output requires closed objects * test(anthropic): cover forced and unforced open-object routing, json mode thinking, numeric formats and wide schemas Add proxy cells for open-object schemas that keep native output when the json tool cannot be forced (a model that refuses forced tools on the Anthropic API and Vertex AI, thinking, caller tools), for the thinking block a json mode answer keeps on Vertex AI and Bedrock Invoke, and for integer and number formats and 65-field schemas on each Bedrock map key. Point the forced open-object cells at the Anthropic API and Azure AI only, since no Vertex AI model in the cost map takes both native output and a forced tool * fix(bedrock): keep the native output opt-in on the deployment that sets it, and route effort none open objects to native output * test(bedrock): cover deployment-scoped native output opt-in, register_model and effort none open objects |
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joshua-berri
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6d0d0bb68b
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feat: add Nace Drex decisions provider (#46523)
* feat: add Nace Drex decisions provider * fix: expose Nace in dashboard provider metadata * ci: include Nace tests in the provider shard * fix: complete Nace provider catalog and preserve OpenRouter tests * test: mark Nace test directories as packages * fix: share Drex score parsing across decision providers --------- Co-authored-by: Joshua Valluru <326636767+joshua-berri@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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b6ca57b36d
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fix(bedrock): set supports_none_reasoning_effort on gpt-6-sol and gpt-6-luna (#47000)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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ryan-crabbe-berri
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9cf09c18fe
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fix(providers): route Azure Responses past dated api versions, count Grok reasoning tokens on azure_ai, refresh provider sets after register_model, fix gpt-6.1-sol Azure rows (#46377)
* fix(azure): add responses_api_version so the Responses route stops using the deployment chat api_version * fix(azure_ai): fold Grok reasoning tokens into completion_tokens like the xai provider * fix(utils): refresh provider model sets after register_model * fix(cost-map): align azure gpt-6.1-sol reasoning effort flags with the openai row * test(e2e): cover Azure Responses on a deployment pinned to a dated chat api_version * fix(utils): keep router deployment ids out of provider model sets register_model now refreshes the provider sets, so the per-request and router registrations keyed by a deployment id put those opaque ids into wildcard model listings. Those callers now pass add_to_provider_model_sets=False. * fix(azure): carry responses_api_version through the chat to Responses bridge completion() dropped responses_api_version from the litellm_params it hands the Responses bridge, so azure/responses/<model> on a dated chat api_version still built the 404ing /openai/responses URL. Also cover the auto-router marker never forwarding the new param. * fix(azure): stop listing a wrong set of reasoning efforts when none is rejected The message said only low, medium and high are supported, which is wrong for models that also take xhigh such as gpt-6.1-sol. * test(azure): use the latest grok and gpt-5.4 rows in the new azure tests * fix(proxy): list each wildcard-expanded model once A provider set can hold a model both bare and provider-prefixed, which now happens after any priced request registers its openai/<model> key, so the openai/* listing showed that model twice. * fix(azure): route Responses past dated GA api_versions by default A deployment pinned to a dated GA api_version such as 2024-10-21 for chat now sends Responses calls (create, get, delete, cancel, input_items and the azure/responses/ bridge) to the default Responses version, because those dated versions have no Responses route on Azure and 404. responses_api_version still overrides it, -preview, v1, latest and preview versions are kept, an api-version in the api_base query still wins, and chat keeps api_version as before * chore(azure_ai): mark the grok chunk_parser dict as an override contract * fix(azure): rewrite only the route's /openai segment when moving a call to v1 The v1 rewrite in the Azure URL builder replaced every "/openai" substring in the path, so a gateway api_base like https://apim.example.com/openai-gw/team turned into /openai/v1-gw/team/openai/v1/responses. Dated GA Responses calls now land on the v1 route by default, which made this reachable for them too, so the rewrite now only touches the last /openai path segment. The Responses version resolver no longer duplicates the builder's default for an unset api_version. Tests cover the gateway api_base case, the env AZURE_API_BASE query-string precedence, and the e2e suite now has one test for the dated GA default and one where only responses_api_version can make the call work * test(integration): cover Azure Responses api_version, Grok reasoning spend, wildcard listing and reasoning_effort none Wire tests pin the Azure route and query for chat, Responses and the azure/responses bridge on a dated GA api_version, and check responses_api_version never reaches the request body. Cost cases check azure_ai Grok folds reasoning tokens into completion_tokens and spend, streaming and not. A config-driven proxy checks /v1/models next to an openai/* wildcard lists each id once and never a deployment id. Azure gpt-6.1-sol rejects reasoning_effort none before reaching upstream and drops it with drop_params * fix(utils): list ai21 chat and vertex code models under their provider wildcard register_model now files ai21 chat models into ai21_chat_models, which models_by_provider never read, so a registered ai21 chat model dropped out of ai21/* listings. models_by_provider now reads ai21_chat_models and the vertex code chat and code text sets too. * fix(main): keep caller-chosen model names out of provider wildcard listings --------- Co-authored-by: yuneng-jiang <yuneng@berri.ai> |
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litellm-stampbot
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1c317e3e61
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feat(cost-map): add baseten and nebius rows for Reflection Beam 1.0 (#46991)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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87f959798d
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fix(bedrock): backfill Priority and Batch prices from the AWS offer index (#46987)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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78372b440d
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feat(cost-map): add together_ai reflection/Beam-1.0-501B-A23B (#46964)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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Rick
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f81e657903
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feat(volcengine): add Doubao-Seedance video generation via the OpenAI /v1/videos surface (#46156)
* feat(volcengine): add Doubao-Seedance video generation via the OpenAI /v1/videos surface
New VolcEngineVideoConfig maps litellm's OpenAI Videos API onto Volcengine
Ark's contents/generations task API: create hits
/api/v3/contents/generations/tasks and returns a cgt-... task id, status and
content poll /api/v3/contents/generations/tasks/{id}, list maps the same
collection (limit -> page_size, Ark's filter.* through extra_query), delete
maps DELETE on the task URL, and edit/extension reuse create with a
reference_video content item since Ark models both as ordinary generation
tasks. Ark's own edit and extension require ratio adaptive (edit also
duration -1), so those are pinned unless the caller overrides them. Remix has
no Ark equivalent and raises.
The body is flat apart from a typed content array: the prompt is a text item
and every image/video/audio input is a url item carrying a role, which is the
only way to express reference-to-video, edit and extension jobs, so a
caller-supplied content array wins outright. input_reference becomes a
first_frame item, base64 encoded into a data URI for file inputs.
OpenAI params translate per Ark's contract: seconds becomes duration and size
yields both a ratio and a named resolution tier, with the ratio snapped onto
the set Ark accepts (854x480 reduces to 427:240, which Ark rejects) and the
tier taken from the shortest side. Emitting only the ratio leaves the request
on the model default and bills at that default's rate.
Billing reads the delivered duration and resolution rather than the requested
ones, because duration -1 and ratio adaptive both let Ark deliver something
else. Ark bills per output token (tokens = width * height * 24 * seconds /
1024), so the cost map carries per-second rates by resolution tier, derived
from the console's CNY per million token list rates (2.5 70, 2.0 46, 2.0-fast
37, 2.0-mini 23 without a video input) at the repo's 7.0 CNY/USD convention.
Requests that do carry a video input bill at the lower rate instead, which
litellm's per-second tiers cannot express, so those are overestimated.
usage.total_tokens rides along for invoice reconciliation.
Ark echoes the delivered geometry only as a resolution tier plus a ratio, and
the 2.0 family's rendered frame does not follow the tier name: its 16:9 "480p"
canvas is 864x496, not the 854x480 the tier implies, and its other ratios
differ from the tier's nominal shortest side too. A size reconstructed from
the tier alone therefore reports pixels the model never produces and scales
the 480p rate against the wrong pixel count, so both the reported size and
the 2.0 480p rates come from the measured frame (a per-family table, sourced
from the Seedance 2.0 Mini model card and re-verified against a live gateway).
The create response reports the requested duration and resolution, since only
the create, edit and remix calls are costed and Ark's create response carries
no usage block, so without it every video logs zero spend. A caller that
leaves seconds off is not free either: Ark runs its own default length, which
is 5s for the 2.0 family, so that is what the create response bills, while
the 2.5 family's default is -1 (Ark picks) and stays unbilled until the
status response. The create response also echoes the requested seconds and
the frame the job will be rendered at.
Editing and extending need the source video as a URL: Ark's task query is a
GET while litellm's edit prefetch hook only POSTs, so a task id cannot be
resolved into one. The model comes from an explicit extra_body.model, else
the encoded video id, else the model the router already resolved, with
litellm's provider prefix dropped.
The credential's api_base is the Anthropic-compat surface
(/api/compatible), so the base is cut at the first /api/ marker rather than
by a /api/v3 suffix before the video paths append their own version.
The cost-map schema test's exception list gains the 2.0 4K tier, whose
$1.28/second rate exceeds its $1 sanity bound, alongside the existing
runwayml/seedance2 entry.
* fix(volcengine): answer 4xx when a Seedance video is downloaded before it finishes
Downloading a task that is still queued or running raised a bare ValueError, which the
video content handler synthesizes into a 500. The proxy then retried the same poll twice
and reported the caller's own early download as a gateway fault, and the router went
looking for a model group fallback for a job that was simply unfinished
The download path now raises VolcEngineVideoError carrying the status the OpenAI videos
API documents for content that is not ready (404), 400 for a task the provider reported
as failed, 404 for an expired task record, and 502 for a succeeded task with no artifact.
None of them are 5xx, so the router stops retrying them
Regression tests drive avideo_content and assert the caller sees NotFoundError or
BadRequestError instead of an internal server error
* fix(volcengine): clear the type-discipline and override findings on the Seedance video path
The module annotated four mutable collections and carried five `# mutable-ok` markers on
lines that suppressed nothing, so the type-discipline gate counted LIT001 x4 and LIT013
x5 against the PR. The content-item and task-dump helpers hand back read-only views of
what they build, so they now say Mapping. VideoObject.error is declared as a dict by the
type it feeds, so that one marker moves onto the annotation it actually suppresses
transform_video_status_retrieve_response also dropped the client keyword the base class
declares, which basedpyright reported as an incompatible override
* fix(volcengine): bill every Seedance video at create and keep its polls on the deployment that made it
Ark bills a task when it finishes while litellm bills it at create, so a request
whose cost is not known then is rejected: Ark-picked length (duration -1), frames,
a video input, and with it edit and extension. An omitted length is sent as 5s,
since the 2.5 family otherwise lets Ark pick it and logged $0.
Video ids now carry the requested and the served model group, read from
litellm_metadata, so polls and downloads go back to the deployment that created
the task. The routing and input_reference helpers move out of the DashScope
config into a shared module both providers use.
A download before the render finishes answers 400, unreadable or unmapped task
bodies answer 502, malformed seconds, size, parameters and Ark fields raise
unless drop_params is set, an unpriced resolution tier is refused, and the
rendered frame table moves into the cost map.
Co-Authored-By: AaronHowell <111272993+AaronHowell@users.noreply.github.com>
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
* fix(volcengine): reject a text item in content so the guarded prompt is the only text sent
Input guardrails scan data["prompt"], so a caller could pass a harmless prompt
for scanning and a different, unscanned text item in content, which this branch
kept and the guarded prompt was dropped. The prompt now always leads the content
array and a second text item answers 400.
Co-Authored-By: AaronHowell <111272993+AaronHowell@users.noreply.github.com>
Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>
* fix(volcengine): route video to VOLCENGINE_API_BASE when the deployment sets no api_base
* fix(volcengine): let a model with no tier prices keep its resolution instead of rejecting every tier
* fix(volcengine): answer 400 for malformed or unknown create fields, send a 720p default tier, and stop the list cursor
* fix(volcengine): map size to the closest tier by pixel count, reject draft, remix and list cursors, map user to safety_identifier, read ARK_API_BASE
* fix(volcengine): drop unsupported create values under drop_params, take OpenAI's object input_reference, default to 4 seconds, price endpoint ids by base_model
* fix(videos): unwrap nested file tuples without recursion
* fix(volcengine): price Seedance tiers at Ark's own per-resolution token rates
Ark prices 1080p and 4k per token above and below the 480p/720p rate, so the
single 46 CNY per million tokens the rows were derived from billed 2.0 1080p
and 2.5 1080p about 10% low and 2.0 4k 77% high. Ark's per-resolution table
and its own 16:9 examples agree: 1080p 2.48 CNY/s, 4k 5.05 CNY/s for 2.0 and
3.74 CNY/s for 2.5 1080p, which is 51, 26 and 77 CNY per million tokens.
Rewrites the tier test to check each rate against the rendered pixels at that
tier's own price instead of assuming every tier scales from the base rate,
and drops the 4k cost exception that only existed for the old rate.
Co-Authored-By: Bytechoreographer <noreply@bytechoreographer.com>
Co-Authored-By: AaronHowell <111272993+AaronHowell@users.noreply.github.com>
* fix(volcengine): unpack Ark parameters sent through extra_body
* fix(volcengine): ignore the extra_body keys litellm maps itself
The shared request builder merges the caller's raw extra_body back over the
mapped params, so a key this adapter maps itself comes back a second time as a
field of its own, and the unknown field check turned the supported request
into a 400.
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Ayush Shekhar
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7930c0e434
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feat(router): mid-stream fallback continuation for chat completions (#41127)
* feat(router): continue chat streams on fallback after a mid-stream break A chat-completions stream that breaks after content has been delivered was re-raised and the fallback deployment never ran (#40404), while the Responses-API path already continues via a prefilled assistant turn. Add the same for chat completions behind `enable_mid_stream_fallback_continuation` (opt-in, async-only), so a post-content break re-enters the fallback chain with the partial text as an assistant prefill instead of surfacing the error. Continuation is only attempted when it is safe: the stream emitted plain assistant text (no tool/function calls, thinking blocks, reasoning items, audio or images), the request is not constrained output (response_format or a forced tool_choice) and not merge-reasoning mode, and the fallback target's model supports assistant prefill. The target check runs as a deployment pre-call filter, so a chain with no prefill-capable deployment empties and the original error is surfaced rather than a duplicated or rejected request being sent. Plain reasoning_content is treated as out-of-band and does not block a continuation, matching the Responses-API path; Anthropic thinking is excluded because its signed thinking blocks cannot ride a text-only prefill. CustomStreamWrapper now latches whether a disqualifying delta was streamed and carries it on MidStreamFallbackError so the router can decide without re-scanning chunks. Default behavior is unchanged. * fix(router): harden mid-stream continuation eligibility and prefill folding Address review of the mid-stream continuation: - keep response_format={"type": "text"} eligible; only json_object / json_schema and other structured formats decline, since "text" is the unconstrained default - fold a new partial into an existing trailing assistant prefill instead of appending a second one, so a nested mid-stream break stays a single prefill turn even on providers that do not merge consecutive assistant messages - split the deployment prefill-capability lookup into two readable steps - cover the merge_reasoning decline branch, the text response_format pass, and the prefill-folding path with tests * fix(router): address review of mid-stream continuation - register the continuation deployment filter as a process-global singleton instead of tracking it per router, so discarding one router no longer removes the type-deduplicated filter that other live routers still depend on - disqualify reasoning_content: it reaches the caller as visible reasoning that a text-only prefill cannot carry, so a reasoning-capable fallback would re-derive it and produce an incoherent response - move MID_STREAM_CONTINUATION_KWARG to constants and lazy-import the filter class in the router, breaking the module-level import cycle CodeQL flagged - trim the added docstrings to the repository comment policy * fix(router): keep mid-stream continuation within the type-discipline budget The LIT* type-discipline gate counts mutable-collection use the ruff pass does not see. Annotate the reads with read-only Sequence/Mapping views, compare response_format against a frozen MappingProxyType sentinel, and mark the genuinely mutable litellm message and deployment-list boundaries with mutable-ok reasons, so LIT001 and LIT002 stay under their limits. * fix(router): clear basedpyright operator error and the import-cycle alert - narrow the accumulated stream delta content to str before appending, so the shared helper no longer trips reportOperatorIssue (str += object) - keep the continuation marker in the pre-call-check module and lazy-import it at the two router call sites instead of routing it through constants, so the router has no module-level dependency that closes an import cycle * fix(router): make the mid-stream continuation marker unforgeable The deployment filter is process-global and read the continuation marker as a truthy value from request_kwargs. Since the proxy can forward arbitrary request-body fields into the router, a client could send `"_mid_stream_continuation": true` on a normal request to filter a mixed model group down to prefill-capable deployments and steer their prompt onto that provider. Set a private sentinel object internally and check it by type instead of truthiness: a JSON request body cannot construct one, so a forged flag is ignored. Adds a regression test that forged client values (true, "true", 1, a dict) leave the deployment list untouched. * feat(router): honor per-deployment model_info for continuation prefill capability The deployment filter only read supports_assistant_prefill from the cost map, so a model configured with `model_info: {supports_assistant_prefill: true}` on the deployment (the usual way to declare a capability for a model that is not in the cost map, or to override it) was wrongly skipped. Check the deployment's model_info first (True or False both authoritative), then fall back to the cost map. Adds tests for the override in both directions. * fix(router): bound mid-stream continuation to the caller's output allowance A continuation handed the fallback the full max_tokens / max_completion_tokens even though part of the answer had already streamed, so the total could exceed the caller's limit and repeated interruptions could re-grant the allowance on every hop, amplifying spend. Reduce each configured ceiling by the tokens already emitted before re-entry, and decline the continuation (surface the original error) when the allowance is exhausted. The reductions compound across hops because the trimmed ceiling is what the next hop sees. Also add the missing explanatory comment on the model_info fall-through except. * refactor(router): extract mid-stream decline to stay within the complexity budget The output-ceiling branch pushed stream_with_fallbacks past the C901 limit. Both decline paths (ineligible after content, and exhausted allowance) re-raised the original provider error the same way, so pull that into _raise_original_mid_stream_error. It removes the duplicated branching, drops the function back under the complexity ceiling, and reads clearer. * fix(cost-map): mark Claude Sonnet 4.6 as not supporting assistant prefill The Anthropic API and Bedrock both reject an assistant-last message on claude-sonnet-4-6 with "This model does not support assistant message prefill", so the continuation filter must not route a mid-stream continuation to it. Flip the flag on every claude-sonnet-4-6 entry and on databricks' opus-4-6 entry, whose anthropic sibling already says false. The prefill check tests now register synthetic cost-map entries instead of pinning real models, which broke when claude-3-opus left the map. * refactor(router): type the streaming fallback kwargs and drop narration comments Removes an unused mutable-ok marker (LIT013) and the explanatory comments Greptile flagged, and types initial_kwargs on both streaming fallback iterators as dict[str, Any] like their Responses siblings so the new continuation helpers take a typed mapping and the basedpyright budget stays at or below main. * fix(router): run post_call_rules over the joined text of a continued stream Each provider stream wrapper validates only the fragment it produced, so a rule that trips on the primary's prefix plus the continuation's text never fired. The router now feeds litellm.post_call_rules the emitted text plus every continuation chunk before yielding it. Also drops a narrating test-helper docstring. * refactor(router): read a deployment's declared prefill support without an empty except * test(router): cover _raise_original_mid_stream_error directly for the router coverage gate * fix(router): fold the continuation prefill into a trailing assistant turn A request that already ends in an assistant message took the partial output as a second assistant turn, which prefill providers reject. The partial now extends that turn in place, with prefix set, so the continuation request never ends in two assistant messages. * fix(router): keep content blocks on a trailing assistant turn when extending it with the partial output A trailing assistant message whose content is a list of blocks was stringified before the partial output was appended, so the fallback received the Python repr of the blocks as its prefill. The partial now lands as one more text block, and a content shape that cannot be extended surfaces the original stream error instead of a garbled continuation. * fix(router): continue the trailing text block in place when extending a prefill with content blocks Anthropic strips trailing whitespace per content block, so appending the partial output as a new text block after an assistant turn that ends in "Sure, " would send the prefill as "Sure,here". The continuation now extends the last text block itself and only opens a new text block when the trailing block is not text (an image, say), so the prefill reads exactly as the text the caller already received * test(router): type the prefill check's deployment helper * fix(router): keep a continued stream on the primary's identity and widen what can continue Continuation chunks now keep the primary stream's id and created time and drop the fallback's second role delta. Requests with n > 1 no longer continue. Prefill support reads model_info.base_model and litellm_params.base_model before the deployment model. enable_mid_stream_fallback_continuation is runtime-updatable through update_settings and registers the prefill filter. The bridge rebuild of MidStreamFallbackError keeps the inner stream's disqualifier. A fallback that repeats the prefill is not doubled in the continuation input. * fix(router): decline mid-stream continuation after a content filter, merged reasoning, or a plain trailing assistant turn Also count the partial stream's completion tokens in the continued stream's usage, accept string values for enable_mid_stream_fallback_continuation, and list the new router helpers in the router coverage check * fix(router): drop re-emitted whitespace at the continuation join and sum both legs' usage consistently * fix(router): keep the full prefill so a nested break keeps the separator * fix(router): bill both calls of a continued stream and fix Bedrock Converse, Databricks, and Vertex DeepSeek prefill routes * test(router): integration cells for mid-stream fallback continuation Covers the continued stream the caller gets, the prefill turn and reduced max_tokens the fallback receives, the prefill-capability filter, the runtime toggle, repeated requests through the response cache, and the spend row of a continued stream * fix(router): log a continued stream under the caller's id, price each cut on its own prompt, and keep prefill flags per deployment * fix(router): price and log each cut leg before its continuation reuses the logging object Price the cut stream's partial and let its failure log finish (bounded) before the fallback overwrites a shared proxy logging object, and skip continuation when an output guardrail is registered or a request fallback carries its own messages. * test(router): integration cells for the output-guardrail and request-fallback declines and the cut call's failure log Cover a registered post_call guardrail and a request fallback carrying its own messages keeping the original error, an input-only guardrail leaving continuation on, and the cut call's failure event naming its own deployment and price. * fix(router): skip mid-stream continuation when a request fallback overrides request params A request-body fallback entry that sets anything besides model (stream, messages, max_tokens) changes the request the prefill continuation relies on, so a stream: false entry ended the answer with no error. * fix(router): skip mid-stream continuation for a fallback group target that overrides request params The override check read only top-level fallback entries, so a target inside a group's list, such as {"primary": [{"model": "backup", "stream": false}]} in the request, the config, or a key's router settings, still ended the answer with no error. * fix(router): check only the request's own fallback chain for request param overrides Scanning every group's chain turned continuation off for all requests when one unrelated group's target overrode a param. Resolve the chain the router will use for this request, the same way the fallback path does. * fix(router): keep the original error when request fallbacks are malformed The request-param override check resolved a standard-shape chain outside the router's own error handling, so an empty entry or a non-list chain raised StopIteration or TypeError and replaced the original mid-stream error. Skip empty entries and treat a non-list chain as no chain. * fix(router): read deployment-configured constraints, decline deployment-id fallbacks without prefill, strip only the last Bedrock text block * fix(router): read a deployment's tool_choice ahead of router defaults in the continuation check, and restore the renamed prefill test names * fix(router): filter continuation deployments on every routing strategy, read extra_body and fallback deployments' own params The deployment filter now also runs on the sync selection path that usage-based-routing v1 and custom strategies use, one predicate reads n, response_format, tool_choice and merge_reasoning_content_in_choices with extra_body laid over them, fallback deployments whose own config constrains the output are skipped, and continuation declines when the cut deployment's id is not in the router (per-request credential deployments) * fix(router): decline a cut mid-echo, reduce extra_body output limits, and check post_call_rules once on nested continuations --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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e74e4e5693
|
fix(vertex-ai): drop cached flex price from gemini 3 preview rows (#46826)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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123e2071bf
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feat(wandb): add NVIDIA Nemotron 3.5 Super VL to the cost map (#46795)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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464bb5a1ca
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chore(pricing): add github_copilot deprecation dates for claude-opus-5 and gpt-5.6-terra (#46792)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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6c9c3a9098
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feat(cost-map): add baseten Muse Spark Open and Nemotron 3.5 Super VL (#46794)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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d387cdc816
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fix(vertex-ai): end free period for deepseek-v4.1-flash-maas (#46793)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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Mateo Wang
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7090588bdf
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feat(gemini)!: auto-continue gemini-4-argon long decodes with continuationToken (#46240)
* feat(gemini): continue MAX_TOKENS decodes with continuationToken across chat, streaming, and interactions * fix(gemini): continue long decode on CONTINUATION and cancel interactions at /cancel * feat(gemini): add public gemini-4-argon cost-map rows for AI Studio and Vertex AI Add gemini/gemini-4-argon and vertex_ai/gemini-4-argon alongside the existing early-access row, carrying the same limits and capabilities. The AI Studio row mirrors the early-access entry; the Vertex row differs only in provider and pricing source. Prices follow once Google publishes them. Extend the mapped metadata test to cover all three ids, that each routes to its provider, that its backup copy matches, and that the public rows carry the same limits as the early-access row. * feat(gemini): raise gemini-4-argon to the 1M long decode cap and report initial_prompt_tokens across hops * chore(prices): build on the merged gemini-4-argon rows and only raise the long decode cap * fix(gemini): follow continuation hops in a loop instead of recursion The unary hop collectors and the stream iterator called themselves once per continuation request, which the recursion check in CI rejects and which would hit the interpreter's call depth on a very long output. They now loop, and a test follows more hops than the interpreter allows nested calls * fix(gemini): bill Google Search grounding from every continuation request Google sends search grounding once, on the request that ran the search, and does not repeat it on a continuation request. The merged response kept only the last request's grounding, so usage.web_search_requests and the per-search fee dropped every earlier search. Unary merges and the stream iterator now carry the grounding of every request in the chain * fix(gemini): continue long gemini-4-argon answers on streams and Vertex AI A continued stream stopped after the first chunk of its second request. The stream wrapper asks the iterator for a fresh start before every chunk, and that reset it to the first request's finished stream. The iterator now reads from the newest request's stream On Vertex AI the continuation request went to the v1 API version, which rejects continuationToken with a 400. A request that carries the token now goes to v1beta1, which accepts it, including a token that v1 issued On a stream, initial_prompt_tokens came from a usage chunk in the middle of the stream, which reports a smaller prompt count than the last chunk. It is now the count the first request's stream ends with, the same number a unary call reports continuation_token is no longer forwarded on the Interactions API, which answers 400 for the field. It is dropped there as it was before this branch * chore(gemini): name the type errors the dual-mode stream iterator trips The stream iterator holds a sync or an async line iterator in one untyped attribute. Pointing it at a continuation request's stream gave the type checker a concrete union for it, so six existing lines that only ever run in one of the two modes started to report errors This adds a rule-named suppression with a reason on each of those lines and changes no code. Splitting the class into a sync and an async iterator would remove them and is left for a follow-up * fix(gemini): continue only when Google ends a request with CONTINUATION, and stop streaming mid-answer tokens A long decode now sends the next request only when Google's finish reason is CONTINUATION, so an answer cut at the caller's own max_tokens returns in one request with its continuation_token instead of drawing a rejected extra request A request for more than one choice is never continued, since Google rejects a continuation with more than one candidate Chat-format streams no longer carry the tokens Google sends mid-answer. Only a stream that ends cut off carries a continuation_token, on its last content chunk The response cache key now includes continuation_token, so a resend with a token is not served the earlier segment * fix(gemini): keep one response id across a continued answer A continued chat stream changed its chunk id each time the next request to Google started, and a unary answer came back under the last request's id. Every chunk and the unary answer now carry the first request's id, including the chunk that ends a cut-off stream A request whose n or max_tokens arrives as a string is sent once and never continued, where it used to fail with a 500 on the comparison. An empty continuation_token no longer changes the cache key * test(gemini): add integration cells for long decode continuation Covers continued answers on chat, /v1/messages and /v1/responses, streamed and not, on the Gemini API and Vertex AI against a scripted Google, plus concurrency, outage and worker restart runs Also moves the Google stand-in cancel route to /cancel, the URL this PR now calls for Interactions cancel, and checks the cancel test posts there exactly once * fix(vertex_ai): fetch the access token again before each continuation request A long Gemini 4 answer on Vertex AI can run for over half an hour of follow-up requests, and every one of them reused the bearer token fetched before the first request. Once that token expired Google answered 401 and the caller got a cut-off answer with finish_reason length Each Google request in a chain now asks for the token again, which is a cache hit until the token nears expiry, on the streamed and unary paths, sync and async. A refresh that fails counts as a failed follow-up, so the caller still gets the answer so far with its continuation token. A caller's own Authorization in extra_headers still wins on every request, and the Gemini API key path is unchanged * fix(vertex_ai): start each continuation stream with an empty parser buffer When a Google stream ended on an unfinished frame, its leftover text stayed in the stream parser and every frame of the next continuation stream was appended to it. None of them could be decoded, so the whole continuation was billed and dropped without an error Each continuation stream now starts with a fresh buffer, and a test covers a stream that ends on an unfinished frame, sync and async * fix(vertex_ai): treat a continuation stream with no readable frame as a failed follow-up A continuation stream that answered 200 and then sent Google's error frame first, sent a body that is not JSON, or dropped before its first frame ended the caller's stream with `stop` or an error and no token, so a cut answer looked complete or was lost. It now ends the way a refused follow-up does, with the answer so far, `finish_reason` length and that request's token Every continuation stream request now also gets the caller's per-request timeout, which #46349 added to the first one, so a follow-up that never answers ends at that timeout the same way * fix(vertex_ai): keep every follow-up's citations and URL context in a continued answer A non-streamed answer that took several Google requests kept citationMetadata and urlContextMetadata from the last request only, and left every later request's offsets counting from that request's own text. So a URL read on the first request was missing from the answer, citations from earlier requests were dropped, and a later request's citations and search supports pointed at the wrong part of the joined answer The joined answer now keeps every request's citations (Vertex AI's `citations` and AI Studio's `citationSources`) and URL context, and shifts each later request's citation and search support offsets by the UTF-8 bytes of the answer text sent before it, thought parts excluded, since Google counts these offsets in bytes * fix(vertex_ai): answer when a candidate sends null citationMetadata or urlContextMetadata A Gemini candidate whose citationMetadata or urlContextMetadata was JSON null made the call fail on every Gemini model, non-streamed with a 422 or 500 and streamed with an error event, because the per-request metadata helper handed the null back as is and the caller tried to extend a list with it. A continued answer also failed when a follow-up's citation or search support came with a null startIndex A null field is now kept as one entry on a single request, as it was before this branch, a follow-up's null URL context adds nothing to a continued answer, and a null startIndex counts from the start of that request's text. Tests cover missing, null and empty fields on one request and on a continued answer, streamed and not * fix(vertex_ai): pass through spans with no end index and point a continued stream's citations into the whole answer A later request's citation or search support with no endIndex made the whole non-streamed answer fail with a 500, so a span Google sends without an end index now comes back as Google sent it, the way one request for the whole answer returns it. A span with an end index but no start index still counts from 0 A continued stream handed callers each later request's citation and search support offsets counted from that request's own text. The stream now adds the answer bytes of every earlier request, so the offsets point into the whole answer like the non-streamed path and one request for the whole answer Adds the audit's wire tests for both, which fail 24 cells before this change * test(vertex_ai): cover a later request whose cited span starts at its first byte with no start index Google leaves out a zero startIndex, so the second request of a continued answer can cite its own opening with only an endIndex. These wire cells check that span lands where one request for the whole answer puts it, on Gemini and Vertex AI, chat and Responses, unary and streamed * fix(vertex_ai): log a continued answer's raw response as one object A non-streamed answer joined from several Google requests logged its raw response as a JSON list of every request's body. The OpenTelemetry logger reads that raw response as one Gemini body to set its `llm.gemini.*` attributes, so on a continued answer it hit a validation error and set none of them It now logs the merged body, which carries only the last request's token, and a single request still logs Google's body unchanged. The new unit test fails with the change reverted Also adds integration cells for a cut chat stream's token chunk, an answer joined from 41 requests, a caller that gave up on a non-streamed call, counts and caps that are not numbers or are sent twice, `n` sent as true, a partial answer cached after a refused continuation, the token in another provider's cache key, and the sync SDK path with its Vertex token refetch * test(integration): check a continued answer logs one merged raw response A non-streamed Gemini answer that took several Google requests now hands logging one merged response, so the OpenTelemetry span carries the llm.gemini attributes. These 12 cells fail on the merge base and on the commit before that fix, and pass on the branch * test(vertex_ai): cover a follow-up tool call and its thought signature in long decode * test(gemini): pin that a one-request answer logs Google's body unchanged |
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Fede Kamelhar
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68759a239f
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fix(oci): drop non-function tools under drop_params and gate reasoning_effort on declared levels (#40732)
* fix(oci): skip non-function tools and gate reasoning_effort on models that reject it Fixes #31449. Two OCI GENERIC request-building defects that made Grok-on-OCI unusable for tool-using coding-agent clients (Codex over the Responses API, Claude Code over the Anthropic Messages API): 1. `adapt_tool_definition_to_oci_standard` raised `OCIError("OCI only supports function tools")` for any tool whose type is not "function", aborting the whole request when a client mixed a built-in/server tool in with its function tools. Non-function tools are now skipped with a warning; if none remain, `tools`/`toolChoice` are omitted from the request instead of sending an empty array. 2. The GENERIC param map forwarded `reasoning_effort` to every non-Cohere model, but OCI rejects it for all served xAI Grok models (`Model grok-4.20 does not support parameter reasoningEffort`) and for OpenAI's non-reasoning models (`Unrecognized request argument supplied: reasoning_effort` on gpt-4o / gpt-4.1); gpt-5, the o-series, gpt-oss-* and Meta/Google models accept it. `_model_supports_reasoning_effort` encodes that, `get_supported_openai_params` stops advertising the param for those models, and `map_openai_params` follows the standard unsupported-param contract (dropped under drop_params, clear error otherwise) so `drop_params` / `additional_drop_params` now work as documented instead of the 400 surfacing from the service. Behaviour verified against OCI GenAI in us-chicago-1 (2026-09-11) per model. The two existing reasoning_effort normalisation tests move from a Grok model to openai.gpt-5, which is the case they exercise. 21 new tests; the OCI suite is 406 passed. * fix(oci): drive reasoning_effort gating from the model catalog and drop tool_choice for skipped tools Review follow-ups on #40732: - `_model_supports_reasoning_effort` now reads the `supports_reasoning` flag of the model's `oci/` catalog entry through `get_model_info` instead of hardcoding xAI / gpt-oss branches. Catalogued models without the flag (xAI Grok, Meta Llama) no longer receive `reasoningEffort`; models absent from the catalog keep the historical pass-through. - Catalog: flag `oci/google.gemini-2.5-{flash,pro,flash-lite}` as reasoning models and add `oci/openai.gpt-oss-120b` / `oci/openai.gpt-oss-20b` (pricing from Oracle's public price list, 128K context per OCI docs). - `_model_uses_max_completion_tokens` routes on the `openai.*` prefix only. OCI-hosted Gemini accepts `maxCompletionTokens` but ignores it (the output cap silently disappears) and Llama rejects it with 400, so the catalog's reasoning flag must not drive that switch. - After tool adaptation, a `FUNCTION` tool_choice naming a tool that was skipped (built-in / server tools) is dropped with a warning instead of being sent to OCI, which rejects the unmatched choice. Verified live against OCI GenAI (us-chicago-1): Grok with mixed tools plus a forced built-in now completes and still calls the function tool; Gemini and gpt-oss forward reasoningEffort with maxTokens honoured; Llama drops it under drop_params; gpt-5 keeps maxCompletionTokens. * style(oci): use an immutable set for the tool-name lookup Keeps the type-discipline gate (LIT002, mutable collections) within its budget; the lookup is read-only. * fix(oci): fail closed when a forced tool_choice names a tool OCI will not receive Review follow-ups on #40732: - When tool_choice forces a function that is not among the adapted tools (a skipped built-in or a misspelled name), send `toolChoice: NONE` instead of removing the choice. Falling back to the service default would let the model call any other supplied function even though the caller forced a specific one, which an agent processing untrusted content must not do. - Move the #31449 regression tests into the mapped test module (`test_oci_chat_transformation.py`) instead of a separate file. Verified live against OCI GenAI: Grok and Llama with a forced skipped built-in now complete with no tool call; a forced kept function is still honoured. * test(oci): point reasoning_effort comments at the in-module gating tests The referenced module was folded into this file. * fix(oci): drop non-function tools only under drop_params and gate reasoning_effort on declared levels Non-function tools now follow the unsupported-param contract: a 400 naming drop_params without it, and dropped with a warning under it, with a forced dropped tool turning tool calls off. reasoning_effort is gated on the model's reasoning_effort_levels catalog entry, so OCI Llama keeps forwarding it and the Grok 4.20 models OCI serves, now in the catalog with their Oracle prices, stop sending a param the service rejects * fix(oci): gate reasoning_effort on oci/xai.grok-4.20, which rejects it * fix(oci): disable tool calls when tool_choice forces a dropped custom tool by name * fix(oci): drop tool_choice that forces a dropped tool and keep a kept function's name * fix(oci): keep Gemini 2.5 Flash Lite unflagged for reasoning so released proxies keep its max_tokens cap * fix(oci): answer 400 when a request depends on a dropped tool and leave Gemini max_tokens routing to its own PR Under drop_params, non-function tools still drop when function tools remain and tool_choice does not force a dropped tool. A request whose every tool is dropped, or whose tool_choice forces one, now answers 400 instead of a tool-less reply the model can fill with invented results, or a swap to another tool. The maxTokens routing change for OCI Gemini goes back to base, since it is outside this fix and an open PR carries it. --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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67fab6737b
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fix(azure): set azure_ai/claude-opus-5-5-fast deprecation_date from list API (#46760)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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ad83e3d3c8
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fix(vertex-ai): price deepseek-v4.1-flash-maas at zero during free period (#46753)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
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5bd6c0af5c
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feat(model_prices): add missing OpenAI, Together, Fireworks and Bedrock registry entries (#46742)
* feat(model_prices): add missing OpenAI, Cohere, Together, Fireworks and Bedrock registry entries Add the requested model registry entries. Co-authored-by: Azusa <m3171470760@gmail.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(model_prices): drop unpriced Cohere entries Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Azusa <m3171470760@gmail.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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54ff82dd14
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feat(vertex-ai): add deepseek-v4.1-flash-maas (#46729)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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a741f348bc
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fix(bedrock): add Priority and Flex prices to the gpt-oss-120b and gpt-oss-20b rows (#46728)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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f14475e311
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feat(bedrock): add the jp.amazon.nova-2-lite-v1:0 geo inference profile (#46721)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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b211ac865c
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feat(cost-map): add together_ai NVIDIA-Nemotron-3.5-Super-VL (#46693)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
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53cd1d4a98
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fix(model_prices): registry audit 2026-10-15, OpenAI GPT-4 series shutdown dates, Azure gpt-chat-latest retirement, xAI voice text input, Sail DeepSeek V4.1 Flash prices (#46675) | ||
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Taylor Hawkes
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fd84e97f49
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feat(providers): add Inference APIs as OpenAI-compatible provider (#41669)
* feat(providers): add Inference APIs as OpenAI-compatible provider Registers https://api.inferenceapis.com/v1 as a JSON-configured provider (chat completions and Responses API). Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * fix(providers): list Inference APIs in Add Model and the provider docs index, add request-level tests - provider_create_fields.json entry so the Add Model dropdown test passes - provider_endpoints_support.json (+ backup) entry so the provider docs check passes - tests now capture the outgoing HTTP request (URL, auth header, body) instead of only inspecting registrations - drop a redundant comment Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> * test(providers): inject a recording HTTP session instead of patching httpx.Client Request-level tests now set litellm.client_session to an httpx.Client with a MockTransport, so nothing is monkeypatched on a class. Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011qUBY2P8qTTuJCUeMzTCD4 * chore(providers): drop redundant comment on the inferenceapis entry Co-Authored-By: Claude Fable 5.1 <noreply@anthropic.com> Claude-Session: https://claude.ai/code/session_011qUBY2P8qTTuJCUeMzTCD4 * feat(inferenceapis): add cost map rows for the 12 chat models and test the responses route Prices and context windows come from https://inferenceapis.com/pricing and GET /v1/models. Moves the provider test to tests/unit and adds the interactions flag the endpoint matrix now carries. * feat(inferenceapis): price the vendor's chat model aliases and mark /v1/messages supported * fix(inferenceapis): serve /v1/messages natively and keep audio routes off the provider * fix(json providers): send provider-specific params in extra_body and report context overflow as ContextWindowExceededError * fix(exception mapping): match the Inference APIs overflow phrase instead of any context_length_exceeded substring * fix(exception mapping): map context overflow to ContextWindowExceededError for Inference APIs only Providers without their own mapper keep reporting an overflow as BadRequestError, since the router raises a ContextWindowExceededError instead of trying generic fallbacks when context_window_fallbacks has no entry for the group. Adds the integration cells for the provider, its fallbacks, and the extra_body and overflow families. * test(inferenceapis): hold upstream replies across the worker kill in the chaos cell A reply the killed worker sent before SIGKILL can lose its queued spend row, which the product never promised to keep. Holding the upstream until after the kill means only the surviving worker answers, so every 200 must log exactly once. * test(inferenceapis): make the chaos cell kill the quieter worker and always release held replies The survivor now must hold at least one in-flight request, so the burst check cannot pass as 0 == 0, and the held vendor is released on exit so a failed assertion cannot stall the wire server teardown --------- Co-authored-by: TaylorHawkes <857858+TaylorHawkes@users.noreply.github.com> Co-authored-by: Claude Fable 5.1 <noreply@anthropic.com> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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|
Rick
|
71e508b65d
|
feat(minimax): add MiniMax-H3 and H3-Max video generation via /v1/videos (#43705)
* feat(minimax): add MiniMax-H3 and H3-Max video generation via /v1/videos MinimaxVideoConfig maps litellm's OpenAI Videos API onto MiniMax's V2 task API. Create, status, list, content and delete hit /v2/video_generation and /v2/query/video_generation. Remix is not exposed: MiniMax's /v2/video_regeneration only upscales a finished 768P task to 2K and ignores any prompt, so it raises instead of returning a video that silently drops the edit OpenAI params translate to MiniMax's contract: seconds becomes duration, size reduces via gcd to a valid ratio, and text-to-video requests default to ratio 16:9, resolution 768P and duration 5 when unset. input_reference becomes a first_frame content item, base64 encoded into a data URI for file inputs, and multimodal reference media passes through a content array in extra_body. Caller content is validated strictly as image, video or audio items, and the scanned prompt is always the one text item sent, so text can't reach MiniMax past the guardrails; anything else in content returns a 400. api_base shares MINIMAX_API_BASE with the chat config (a trailing /v1 or /v2 is stripped because the video API carries its own version). The list endpoint pages by number with no cursor or sort, so after and an ascending order are rejected with a 400 rather than ignored, and page_num passes through extra_query. Responses validate into frozen pydantic models at the boundary, video ids are provider-encoded so status and content route back to MiniMax, and create reports duration_seconds and video_resolution in usage for cost tracking End-to-end tests drive the real avideo_generation() entrypoint through an httpx MockTransport and assert on the encoded request body, which is what catches a mapper that drops input_reference before the request is built Both model_prices maps gain minimax/MiniMax-H3 and minimax/MiniMax-H3-Max, priced per output second by resolution tier from MiniMax's pay-as-you-go list (H3 $0.08 at 768P and $0.13 at 2K, H3-Max $0.05 at 480P and $0.08 at 768P). MiniMax also bills input images beyond a per-model free allowance (5 for H3, 2 for H3-Max), so each entry carries input_cost_per_image and provider_specific_entry.minimax_free_input_images, and the create response reports the full charge as provider_reported_cost_usd. Reference video input is rejected with a 400, because MiniMax bills its duration only after the create call that litellm bills Co-authored-by: AaronHowell <237895480@qq.com> * fix(minimax): fail closed on invalid video params and malformed task responses Addresses review on #43705: - A create, status or delete response without a task id (or a task) is a 502 instead of a queued object with an empty id - An unmapped task status is a 502 instead of defaulting to queued - seconds/duration must be a positive whole number and size a positive WIDTHxHEIGHT or W:H, otherwise 400; a 0x0 size no longer divides by zero and an unparseable duration no longer falls back to the billed default - characters and image are rejected with UnsupportedParamsError unless drop_params is set, instead of being dropped silently - Downloading a task that is not ready or failed is a 400 that keeps its message through the handler, instead of a ValueError surfaced as a retried 500 - The video download goes through safe_get / async_safe_get Co-authored-by: AaronHowell <237895480@qq.com> * test(minimax): cover the video download path and invalid-param branches The download now goes through safe_get, so the happy path and the finished-task-without-a-url branch are asserted, alongside the non-object parameters rejection and the remix response stub. Generated with [Claude Code](https://claude.ai/code) via [Happy](https://happy.engineering) Co-Authored-By: Claude <noreply@anthropic.com> Co-Authored-By: Happy <yesreply@happy.engineering> * fix(minimax): return 400 for video remix and clear new lint gate counts * fix(minimax): send file-tuple images and keep input_reference next to content media * fix(minimax): answer 404 when an H3 video is downloaded before it finishes A download that lands while the task is still queued or running answered 400. That is a client error, which was the intent, but 404 is what the OpenAI videos API documents for content that is not ready, and it is the status the sibling DashScope and Volcengine video paths now answer, so a client polling all three reads one shape A failed task keeps its 400, since that is not a timing mistake the caller can retry away. The download test drives avideo_content and asserts the caller sees NotFoundError rather than an internal server error * fix(minimax): answer 400 for unsupported video params, early downloads and unknown ids A create param MiniMax cannot honor (seed, n, quality and the like) was dropped without a word. It now answers 400 unless drop_params is on, like other providers A download before the video is ready and a status call for an unknown task id now answer 400. The router cools a deployment down on a 404 and retries a 5xx, and neither is a deployment fault. DashScope on main answers 400 for the same case Input images past the free allowance are now billed through the shared video cost path, so a deployment with custom pricing bills them too. Status usage no longer carries MiniMax's token counts, list sets has_more to false since MiniMax pages by number, and the price map stops listing video as an input modality * fix(minimax): keep the polled video id, honor deployment drop settings, and match chat's key order Status re-encodes the id with the polled id's model when MiniMax omits it, drop_params and additional_drop_params never reach MiniMax and listed keys are dropped, MINIMAX_API_KEY wins over litellm.api_key, create sets created_at, and a content variant other than the video answers 400 * fix(minimax): list a task without a model under the routed group MiniMax leaves `model` off some listed tasks. The caller polls the ids a list hands back, and the proxy resolves a poll by the model in the id, so an id encoded with an empty model no longer routes back to the deployment that served the task. Fall back to the group the router was asked for. * fix(minimax): honor deployment drop keys inside parameters and bill images at the map rate when a deployment prices only seconds A field the deployment lists in additional_drop_params no longer reaches MiniMax when it arrives inside parameters, and a deployment that sets only its per-second price now bills reference images past the free allowance at the cost map's per-image rate instead of $0 * fix(minimax): keep deployment-dropped fields out of the body when they arrive through extra_body * fix(minimax): answer 400 for a video resolution with no price unless drop_params is on * fix(minimax): send the default resolution when a deployment drops resolution --------- Co-authored-by: AaronHowell <237895480@qq.com> Co-authored-by: Claude <noreply@anthropic.com> Co-authored-by: Happy <yesreply@happy.engineering> Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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Mateo Wang
|
3fec5075fa
|
feat(typesafe): support Jev 1.14 images, box questions, and long-context headers (#46596)
* feat(typesafe): support Jev 1.14 images, box questions, and long-context headers * fix(cost): drop the typesafe/jev-1.14.0 entry main already prices * fix(typesafe): send images only with the images header and box questions only to models that answer them TypeSafe takes assets and input_image parts only when the request carries TypeSafe-Danger-Zone-Images: true, so without it they follow drop_params like any other provider that cannot take images. Box support now comes from a supports_box_questions cost-map flag set on jev-1.14.0 and jev-preview, so jev-1.13.0 and jev-latest refuse or drop a box question before the call. A malformed provider_specific_header answers 400 instead of 500. * fix(typesafe): allow null box confidence and validate asset references and base64 * fix(typesafe): gate box questions on the images header, drop unreadable assets under drop_params, forward opt-in headers on the pass-through --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mateo Wang
|
d6e6c6f26c
|
fix(responses): map Anthropic tool search to OpenAI server tool search (#46441)
* fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models * fix(responses): define the tool_search_call output item locally for older openai SDKs * fix(responses): gate tool search on the Anthropic request builder and validate deferred tool fields Bedrock Converse builds its own request and has no tool search, so the bridge now drops the tool_search tool there with a warning instead of sending a tool the transport loses, while Bedrock invoke, Anthropic, and Vertex keep it. A replayed tool_search_call with no stored result is dropped from the Anthropic request so a previous_response_id follow-up no longer answers 400. Only srvtoolu_ ids whose name is one of Anthropic's search tools count as search calls, so a caller function named like one stays a function call. A non-boolean defer_loading or a non-list allowed_callers now answers 400 instead of 500 * fix(responses): keep an answered search call on replay and send tools undeferred when no search tool goes out A replayed Claude tool search call that the caller answered with a tool message kept its tool_result in the user turn while its tool_use was dropped from the assistant turn, so Anthropic answered 400 on the OpenAI-SDK tool loop. The search call is now dropped only when no tool message answers it. The Responses bridge now strips defer_loading from every tool whenever no tool search tool is sent, whether the caller sent none or the route cannot run it, since a deferred tool with no search tool can never be loaded. Namespace members had started carrying the flag to every bridge provider, which providers that reject unknown tool keys answered 422. The audit round-1 integration cells for the bridge wire and chaos paths ride in this commit. * fix(responses): gate only the OpenAI tool_search shape and keep native Anthropic search tools intact A native tool_search_tool_regex_20251119 or tool_search_tool_bm25_20251119 tool the caller sends through /v1/responses passes through the bridge on every route as before, and the other tools keep defer_loading whenever any Anthropic search tool goes out. The route gate now drops the OpenAI tool_search item before translation instead of filtering the translated regex tool, so bm25 counts as a search tool. answered_tool_call_ids walks the history with islice instead of a slice copy. The tool_search_call output item drops its created_by field, which OpenAI rejects on a replayed input item and the streaming item never carried * fix(responses): drop malformed tool flags under drop_params and keep stored search calls off non-Anthropic follow-ups With drop_params on, a non-boolean defer_loading or a non-list allowed_callers on a Responses API tool (or a namespace member) is dropped before the bridge instead of answering 400, so only a request without drop_params gets the 400. A custom tool now carries defer_loading to the chat tool like a function tool does A previous_response_id follow-up routed to a provider that does not build an Anthropic request no longer replays the stored Claude search call (srvtoolu_ id) as a client tool call. Mistral answered that history 400 "Unexpected tool call id". An assistant turn that held only the search call is dropped there. The Anthropic route keeps the call, since its factory pairs it with the stored result The tool_search_call output model is renamed OutputToolSearchCall so the proxy's OpenAPI schema keeps the OpenAI SDK's ResponseToolSearchCall name. answered_tool_call_ids reads the history by index, so the lookup is linear in the history length. The streaming done event reads the response it is given, which is the same object it used to read from the iterator * fix(responses): validate function allowed_callers, send one search tool, and keep replays clean off Anthropic A function tool whose allowed_callers is not a list of strings now answers 400 instead of 500. The guardrail scan reads tools without malformed flags, so the route decides between a 400 and a drop under drop_params. When the caller sends a native Anthropic search tool beside tool_search, only the native one goes out. Databricks Claude no longer counts as an Anthropic request route. A previous_response_id follow-up off Anthropic also drops a search call the client replayed as a function_call with a srvtoolu_ id, and its output. The answered tool id scan in the Anthropic message factory runs once per assistant run instead of once per message. A null created_by on tool_search_call and tool_search_output output items is left out of the serialized response, so a cache hit returns the same keys as the first call. * fix(guardrails): keep OpenAI tool_search out of the Responses guardrail tool scan The scan translated tool_search into Anthropic's regex search tool on every route, so a guardrail returning only function tools removed tool_search from native OpenAI requests and OpenAI answered 400 for the deferred tools * fix(anthropic): drop answered tool search replays beside client calls, place system before dropped search turns, flag tool search on azure_ai * fix(anthropic): keep answered tool search replays paired with their answers * fix(anthropic): place mid-conversation system messages in one pass and scan tool_search_call in output guardrails * fix(guardrails): send empty incomplete tool_search_call arguments when a rewrite is not a JSON object * fix(responses): keep the caller's defer_loading on Claude routes when no server tool_search is sent * fix(guardrails): deliver streamed rewrites when OpenAI's tool_search_call has no call_id * fix(responses): answer 400 to a tool_search execution that is not server or client OpenAI's own Responses API answers 400 invalid_value to it. With drop_params the malformed execution is dropped like the other malformed tool flags, which leaves a server search * test(responses): cover the 400 and drop_params paths for a malformed tool_search execution * fix(responses): map Anthropic tool search to OpenAI server tool search Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): sync generated tool search API types Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): type tool search mapping predicates Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): validate tool-search mappings Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(responses): use the openai SDK ToolSearchToolParam for the mapped tool search * fix(responses): keep deferred chat tools beside a tool search on the responses bridge Restore the hand-rolled OpenAIToolSearchToolParam, since openai 2.20.0 (the supported floor) has no ToolSearchToolParam, and carry defer_loading from chat function tools onto the Responses request when a tool search tool is present, which OpenAI requires for tool_search * fix(responses): leave the bridge's defer_loading carry to its own PR #46439 owns keeping defer_loading on chat function tools sent through the Responses bridge, so this PR keeps only the tool search mapping and the openai 2.20.0 compatible tool type * fix(openai): keep tool search mapping to the OpenAI API and drop it where the model or history cannot take it * fix(openai): narrow the tool search drop to Anthropic's search tool, drop it when nothing is deferred, and run the wire tests against the OpenAI host * fix(openai): pass malformed tools to OpenAI as sent and keep a caller's own tool_search under drop_params * perf(openai): scan Responses history for Claude search calls only when Anthropic's search tool is sent * test(responses): cover the Anthropic tool search mapping to OpenAI Responses across routes, settings, and chaos * test(integration): define the fallback cell's deployments in the gateway config so both workers load them * test(integration): keep the worker-kill burst held past the replacement wait * fix(responses bridge): count an Anthropic search tool as hosted tool search on the chat bridge main's #46439 refuses defer_loading when no tool_search tool is in the request, and the chat bridge only counted {"type": "tool_search"}, so the Anthropic bm25 or regex tool this PR maps to OpenAI's tool search was refused before the mapping ran Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * test(integration): expect the chat bridge to map the search tool now that defer_loading survives --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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2211fa5f3f
|
chore(prices): add xAI grok-voice-3.0, grok-voice-2.0 and grok-voice-latest realtime rows (#46602)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
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314f17986a
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fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models (#45882)
* fix(responses): translate OpenAI tool_search to Anthropic tool search for Claude models * fix(responses): define the tool_search_call output item locally for older openai SDKs * fix(responses): gate tool search on the Anthropic request builder and validate deferred tool fields Bedrock Converse builds its own request and has no tool search, so the bridge now drops the tool_search tool there with a warning instead of sending a tool the transport loses, while Bedrock invoke, Anthropic, and Vertex keep it. A replayed tool_search_call with no stored result is dropped from the Anthropic request so a previous_response_id follow-up no longer answers 400. Only srvtoolu_ ids whose name is one of Anthropic's search tools count as search calls, so a caller function named like one stays a function call. A non-boolean defer_loading or a non-list allowed_callers now answers 400 instead of 500 * fix(responses): keep an answered search call on replay and send tools undeferred when no search tool goes out A replayed Claude tool search call that the caller answered with a tool message kept its tool_result in the user turn while its tool_use was dropped from the assistant turn, so Anthropic answered 400 on the OpenAI-SDK tool loop. The search call is now dropped only when no tool message answers it. The Responses bridge now strips defer_loading from every tool whenever no tool search tool is sent, whether the caller sent none or the route cannot run it, since a deferred tool with no search tool can never be loaded. Namespace members had started carrying the flag to every bridge provider, which providers that reject unknown tool keys answered 422. The audit round-1 integration cells for the bridge wire and chaos paths ride in this commit. * fix(responses): gate only the OpenAI tool_search shape and keep native Anthropic search tools intact A native tool_search_tool_regex_20251119 or tool_search_tool_bm25_20251119 tool the caller sends through /v1/responses passes through the bridge on every route as before, and the other tools keep defer_loading whenever any Anthropic search tool goes out. The route gate now drops the OpenAI tool_search item before translation instead of filtering the translated regex tool, so bm25 counts as a search tool. answered_tool_call_ids walks the history with islice instead of a slice copy. The tool_search_call output item drops its created_by field, which OpenAI rejects on a replayed input item and the streaming item never carried * fix(responses): drop malformed tool flags under drop_params and keep stored search calls off non-Anthropic follow-ups With drop_params on, a non-boolean defer_loading or a non-list allowed_callers on a Responses API tool (or a namespace member) is dropped before the bridge instead of answering 400, so only a request without drop_params gets the 400. A custom tool now carries defer_loading to the chat tool like a function tool does A previous_response_id follow-up routed to a provider that does not build an Anthropic request no longer replays the stored Claude search call (srvtoolu_ id) as a client tool call. Mistral answered that history 400 "Unexpected tool call id". An assistant turn that held only the search call is dropped there. The Anthropic route keeps the call, since its factory pairs it with the stored result The tool_search_call output model is renamed OutputToolSearchCall so the proxy's OpenAPI schema keeps the OpenAI SDK's ResponseToolSearchCall name. answered_tool_call_ids reads the history by index, so the lookup is linear in the history length. The streaming done event reads the response it is given, which is the same object it used to read from the iterator * fix(responses): validate function allowed_callers, send one search tool, and keep replays clean off Anthropic A function tool whose allowed_callers is not a list of strings now answers 400 instead of 500. The guardrail scan reads tools without malformed flags, so the route decides between a 400 and a drop under drop_params. When the caller sends a native Anthropic search tool beside tool_search, only the native one goes out. Databricks Claude no longer counts as an Anthropic request route. A previous_response_id follow-up off Anthropic also drops a search call the client replayed as a function_call with a srvtoolu_ id, and its output. The answered tool id scan in the Anthropic message factory runs once per assistant run instead of once per message. A null created_by on tool_search_call and tool_search_output output items is left out of the serialized response, so a cache hit returns the same keys as the first call. * fix(guardrails): keep OpenAI tool_search out of the Responses guardrail tool scan The scan translated tool_search into Anthropic's regex search tool on every route, so a guardrail returning only function tools removed tool_search from native OpenAI requests and OpenAI answered 400 for the deferred tools * fix(anthropic): drop answered tool search replays beside client calls, place system before dropped search turns, flag tool search on azure_ai * fix(anthropic): keep answered tool search replays paired with their answers * fix(anthropic): place mid-conversation system messages in one pass and scan tool_search_call in output guardrails * fix(guardrails): send empty incomplete tool_search_call arguments when a rewrite is not a JSON object * fix(responses): keep the caller's defer_loading on Claude routes when no server tool_search is sent * fix(guardrails): deliver streamed rewrites when OpenAI's tool_search_call has no call_id * fix(responses): answer 400 to a tool_search execution that is not server or client OpenAI's own Responses API answers 400 invalid_value to it. With drop_params the malformed execution is dropped like the other malformed tool flags, which leaves a server search * test(responses): cover the 400 and drop_params paths for a malformed tool_search execution * test(responses): cover a Claude search call replayed to Bedrock Converse with cache marks --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> Co-authored-by: mateo <mateo@berri.ai> Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Mike Hyams
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9d72bae336
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fix(cost): price typesafe/jev-1.14.0 (#46594)
* fix(cost): price typesafe/jev-1.14.0 TypeSafe released jev-1.14.0 on 2026-10-14 at the same price as jev-1.13.0 ($0.042 per 1M input tokens, output free; https://docs.typesafe.ai/models). Without an entry, calls through the TypeSafe pass-through log a cost of 0: TypeSafePassthroughLoggingHandler prices from litellm.model_cost["typesafe/<model>"], and a deployment's model_info price is registered under its model id only. Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com> * fix(cost): add max_input_tokens to the typesafe jev rows --------- Co-authored-by: Mike Hyams <241361536+hyamie@users.noreply.github.com> Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com> Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
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4428c4c78f
|
feat(cost-map): add scx-ai DeepSeek-V4.1-flash, GLM-5.3 and Kimi-K3 (#46496)
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: bhuvan2134686 <bhuvankakkar@gmail.com> |
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litellm-stampbot
|
4d5291885f
|
fix(cost): price AgentCore web search at $7 per 1,000 queries (#46571)
* fix(cost): price AgentCore web search at $7 per 1,000 queries * feat(search): honor input_cost_per_query set on a search tool (#46560) * feat(search): honor input_cost_per_query set on a search tool * feat(search): record the search tool price on proxy-created logging objects * chore(search): type the search tool price lookups and shard tests/unit/search * chore(openapi): regenerate snapshot and ui types for search tool input_cost_per_query --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: Kerry Lu <klu@berri.ai> |
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berriai-litellm-provider-info-sync[bot]
|
01cec8f938
|
fix(vertex-ai): add flex prices to gemini-nano-banana-2.1 (#46512)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> Co-authored-by: Kerry Lu <klu@berri.ai> |
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berriai-litellm-provider-info-sync[bot]
|
0d67cccf03
|
fix(gemini): add the changelog shutdown date to gemini-2.0-flash-exp-image-generation (#46489)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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Rick
|
4f3cb48dc1
|
feat(dashscope): add Wan 3.0, Wan 2.7 and HappyHorse video generation via /v1/videos (#43706)
* feat(dashscope): add Wan 3.0, Wan 2.7 and HappyHorse video generation via /v1/videos
DashScopeVideoConfig maps litellm's OpenAI Videos API onto Alibaba Model
Studio's async task API: create hits
/api/v1/services/aigc/video-generation/video-synthesis and status and content
poll /api/v1/tasks/{task_id}. It is registered for dashscope and for the
qwencloud and qwen_ai_platform brand aliases, following the same
get_dashscope_family_*_config pattern image generation uses. Unlike the
Sora-shaped providers the request body is nested, so prompt and media go under
input and everything else goes under parameters. X-DashScope-Async is always
sent, because DashScope rejects the call outright without it
OpenAI params translate to DashScope's contract. seconds becomes duration, and
size yields both a ratio and a named resolution tier (1280x720 gives 16:9 plus
720P), since sending only the ratio leaves the request on the default 1080P and
silently bills at that rate. input_reference becomes a single media entry,
base64 encoded into a data URI for file inputs. Which media type a model takes
(first_frame or, on the reference-to-video models, reference_image) is read
from provider_specific_entry.dashscope_video_reference_input via
get_model_info rather than hardcoded; models without it predate the media
array and get the flat img_url field. A caller-supplied media array always
wins, as that is the only way to express multi-role combinations
The create call is the only billed call, so it bills what DashScope will
deliver: the requested duration and tier, or the documented 5 second and 1080P
defaults when they are omitted. Smart duration (duration -1) lets the service
pick the length after that call, so it is rejected rather than recorded at
zero, with a 400. Task creation and task lookup both report failures as a 200 carrying
top-level code and message with no output, so that shape is raised instead of
surfacing as a queued video with an empty id. Status responses re-encode the
deployment model from the polled id, so follow-up polls and downloads route to
the same deployment. Timestamps parse as UTC+8. DashScope publishes no list,
delete or remix endpoint, so those raise rather than guess at a URL
The end-to-end tests drive the real avideo_generation() entrypoint through an
httpx MockTransport and assert on the JSON body httpx actually encoded. That is
what catches a mapper that drops input_reference before the request is built,
or a body fragment json cannot serialize, neither of which a test calling the
transform directly would see
Both model_prices maps gain wan3.0-video, wan3.0-video-prime, wan2.7-t2v,
wan2.7-i2v, wan2.7-r2v and the HappyHorse 1.0 and 1.1 t2v, i2v and r2v models,
for dashscope and the qwencloud and qwen_ai_platform aliases, priced per output
second by resolution tier from Model Studio's USD rate cards. dashscope and
qwen_ai_platform default to the Beijing host and use its rate card; qwencloud
defaults to the international host and uses Singapore's. The three
*_API_BASE_VIDEO overrides are documented in BerriAI/litellm-docs#1920
Co-authored-by: AaronHowell <237895480@qq.com>
* fix(dashscope): fail loudly on video responses that cannot be read
Addresses the review on the video generation PR. A body that is not the
documented task shape, an output block missing task_id or task_status, an
undocumented task_status and a missing output all raise DashScopeVideoError
now, instead of defaulting into a queued video with an empty id that callers
poll forever. A JSONDecodeError from a gateway response no longer escapes as
a generic connection error.
task_id and task_status are required on _TaskOutput, and a missing output
raises even when the body carries no code or message.
Download failures move into the 4xx range, since a 5xx has the proxy retry
them: 409 while the task is still running, 400 for a task that failed, 404
for an id that expired. The status a provider reported is kept when it
reported one, so an in-body failure that arrives on a 200 is billed as the
400 it is rather than as a retryable connection error.
characters and image are only dropped when drop_params is set; the params
that are rebuilt into the body or consumed by the caller (model, prompt,
user, extra_*) stay silently skipped, since the proxy stamps user from the
caller's end-user header.
Co-authored-by: AaronHowell <237895480@qq.com>
* fix(dashscope): clear the lint gates after merging main
* fix(dashscope): accept file tuples and paths as video input_reference
video_generation() types input_reference as FileTypes, which includes
(filename, content[, content_type[, headers]]) tuples and pathlib paths,
but the DashScope encoder only read bytes and file objects, so those
image-to-video calls raised before reaching DashScope. Bare strings stay
URLs and are never opened as paths.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(dashscope): snap size to a supported ratio and read file tuples and paths
* test(dashscope): assert whole mapped values and bill against hand-written rates
The tier pricing test read its expected rate from the same map the cost
calculator uses, so a wrong rate passed. Billing is now checked against
fixed rates driven through the status response usage, while the map test
only asserts every supported tier has its own rate. Size mapping tests
compare the full mapped dict, and the single-test wiring class is a
plain function.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(videos): download content through the caller's client and stop retrying dead tasks
Two Cursor Bugbot findings on the DashScope video PR.
The DashScope artifact lives on OSS, so transform_video_content_response makes a
second request. It built its own cached client, discarding an injected client and
its ssl_verify. Thread the client through the content response the way
transform_video_status_retrieve_response already does, and use it in the three
providers that fetch from a second URL (DashScope, RunwayML, fal.ai). The
remaining providers accept it and read the response body, matching how they
already accept an unused client on status retrieve.
A FAILED or CANCELED task reported without a code or message fell past the
in-flight check into the 502 branch, so the proxy retried a task that can never
produce a video. Raise 400 for a terminal status instead.
* fix(dashscope): answer 404, not 409, when a Wan video is downloaded before it finishes
A download that lands while the task is still PENDING or RUNNING answered 409. That
reads as a client error, which was the intent, but litellm's retry predicate counts 409
as retryable, so the proxy polled DashScope twice more before giving up and then counted
the caller's own early download against the model's error rate
404 is what the OpenAI videos API documents for content that is not ready, and it is the
one status in that family the retry predicate leaves alone. The rest of the mapping is
unchanged: 400 for a task the provider reported as failed, 404 for an expired task id,
502 when a succeeded task carries no artifact
The download tests now drive avideo_content and assert the caller sees NotFoundError
rather than an internal server error
* fix(dashscope): route a video poll back to the model group that created the task
The create response encoded the upstream model name into the returned id. Both the
dashscope and qwen_ai_platform prefixes serve the same model names from their own hosts,
so a proxy with a deployment per region had deployments that only the model group told
apart. Encoding the bare model name resolved a poll to whichever deployment the router
lists first, which sent it to the other region's host with that region's key and a task
id that region never issued. That is the reroute the earlier proof records as the
dashscope and qwen_ai_platform poll ids coming back tagged qwencloud
The id now carries the model group the create was routed to, which is what the proxy
resolves a poll back to, and falls back to the model name for a direct SDK call that has
no router
The regression test drives a real router with one deployment per region, under a
different prefix and under the same one, and asserts the poll carries the key of the
region that created the task
* fix(dashscope): read the routed model group from litellm_metadata
The router stamps the model group it picked into litellm_metadata. metadata only
carries a copy when the caller sent no metadata of its own, and every proxy request
sends some, so a proxied create still encoded the bare model name and the poll
resolved to whichever region's deployment the router lists first
The regression test now also creates the video with caller metadata, which fails
without this change
* fix(dashscope): reject unbillable video requests and keep fal and runway on their own clients
* fix(dashscope): show the caller why a video task is gone or not ready instead of the bare upstream body
* fix(dashscope): check video request values before the billed create and honor per-call drop_params
Type check documented DashScope parameters so a bad seed answers 400 instead of a billed failed task,
pass the call's and deployment's drop_params into the video mapping, answer 400 for an expired task id so
the deployment is not cooled down, read DASHSCOPE_API_BASE for video, keep polling SUSPENDED tasks, and
refuse to map size onto models litellm has no price for.
* fix(dashscope): carry the requested model name in video ids so alias and wildcard keys can poll
A key scoped to an alias or to a name a wildcard serves could create a
video and then got a 403 on the poll and the download, because the id
carried the group that served the call (wan3, qwencloud/*) and the key
was never given that name. The id now carries the name the router was
asked for on that hop, and the router resolves an alias, routing group
or wildcard name in an id back to itself when no deployment name or
provider model matches it first
* fix(dashscope): keep a routing group's video polls on the member that created the task
A routing group spans several model groups, so an id carrying the group's name
load-balanced its polls, and some left for another region's host and key with a
task id that region never issued. The id now carries the member that served the
task when the request named a routing group, and keeps the requested name for a
model group, an alias, or a wildcard match.
* fix(dashscope): carry the requested and served model in video ids so group-scoped keys poll in the creating region
* fix(proxy): pin a video's served group only after key and team aliases resolve
* test(integration): audit DashScope video create, poll, download, routing and chaos paths
* fix(proxy): keep catch-all video polls on their provider and count served polls against the requested group
* test(routing): expect a forged served group to be dropped from the polled video id
* test(integration): cover rate-limited routing group polls and catch-all polls of DashScope videos
---------
Co-authored-by: AaronHowell <237895480@qq.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
|
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devin-ai-integration[bot]
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79abdb66e6
|
fix(cost-map): remove azure_ai/claude-fable-5-5, which Azure does not list (#46428)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
||
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devin-ai-integration[bot]
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2f3ccbe7c7
|
feat(decisions)!: send images to Cloudflare Clef through its images field (#45734)
* feat(decisions): send images to Cloudflare Clef OpenAI-format input_image parts and the System One images field now reach Cloudflare clef and clef-flash in its images field, with remote URLs downloaded and inlined first. Providers that cannot take images drop them under drop_params and answer 400 otherwise. * fix(decisions): follow drop_params for images and use the call timeout for image downloads * fix(decisions): drop a malformed images entry on its own under drop_params * fix(decisions): refuse images next to input whatever drop_params says * fix(decisions): bound clef image fetches, honor drop_params for images, and log caller URLs Refuse more than 4 images for Cloudflare clef before any download, keep the caller's timeout as one deadline across image fetch retries, accept images next to input under drop_params, validate image strings and objects, and pass the caller's image URLs (not inlined bytes) to logging callbacks. * fix(decisions): check input_image URLs and clef image sizes * fix(decisions): accept data URL media type parameters and check clef image types * fix(decisions): count clef images after dropping unsupported types and flag clef vision --------- Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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426ab45f2d
|
fix(cost-map): set together_ai Qwen3.7-Max to published price and restore 2026-09-15 removal dates (#46281)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
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eacc143dca
|
feat(pricing): add github_copilot/gemini-4-argon (#46280)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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devin-ai-integration[bot]
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829bd6a8cc
|
fix(model-prices): add TokenHub rows, fix TokenHub DeepSeek-V4-Pro price, Bedrock Claude 3.5 Sonnet and Fireworks context windows, Together dates (#46206)
* fix(model-prices): add TokenHub DeepSeek, GLM and Kimi rows, correct TokenHub DeepSeek-V4-Pro price, update Together removal dates Co-authored-by: halewang <halewang@tencent.com> Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(model-prices): correct bare Bedrock Claude 3.5 Sonnet context window Align bare Sonnet entries with their regional 200K context metadata. Co-authored-by: OrangerXu <OrangerXu@users.noreply.github.com> Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> * fix(model-prices): correct Fireworks serverless context windows Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> --------- Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com> Co-authored-by: halewang <halewang@tencent.com> Co-authored-by: OrangerXu <OrangerXu@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
|
cb94567ed7
|
feat(vertex-ai): add gemini-4-argon-cyber-permissive (#46249)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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Kerry Lu
|
61841887f3
|
fix(pricing): drop duplicate and bare gemini-4-argon rows (#46246)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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Kerry Lu
|
59ef56f636
|
feat(pricing): add vertex gemini-4-argon (#46237)
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
|
d7fca3dad0
|
fix(gemini): take gemini-4-argon max output tokens from the model docs (#46236)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
|
3e16bf9518
|
feat(vertex-ai): add gemini-4-argon (#46228)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |
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berriai-litellm-provider-info-sync[bot]
|
9dc6d9a3ee
|
chore(prices): add gemini/gemini-4-argon from the Gemini pricing page (#46227)
Price-Sync: litellm-providers Co-authored-by: berriai-litellm-provider-info-sync[bot] <328147090+berriai-litellm-provider-info-sync[bot]@users.noreply.github.com> |