* fix(gemini)!: honor supports_sampling_params on the Gemini and Vertex path and flag Gemini 3.6 Flash, 3.5 Flash-Lite and later
Google ignores temperature, top_p and top_k on Gemini 3.6 Flash, Gemini 3.5
Flash-Lite and every later Gemini release, and says a later generation will
return 400 for them. The model map now carries supports_sampling_params: false
on those rows, and the Gemini and Vertex transformation reads the flag the way
the Anthropic and Bedrock paths already do, dropping the params under
drop_params and otherwise raising the client-side 400 that names drop_params
BREAKING CHANGE: a request that sends temperature, top_p or top_k to a flagged
Gemini model without drop_params now gets a 400 instead of a 200 whose
sampling params Google silently ignored
Fixes#47015
* fix(gemini): gate the passed-through top_k where the request body is built and keep allowed_openai_params working
top_k is a provider-specific kwarg that never reaches the OpenAI param mapping,
so the flag has to be applied while the Gemini request body is built, the same
boundary Anthropic uses. The mapping now skips a flagged sampling param instead
of raising, because the shared unsupported-param check already returns the 400
that names drop_params, and a param the caller lists in allowed_openai_params
is forwarded verbatim as that 400 promises
* fix(gemini): filter top_k out of the generation config instead of mutating optional_params
* test(gemini): type the sampling-flag twin test and keep its locals out of the loop body
* fix(vertex_ai): reject or drop sampling params on batch rows for flagged Gemini models
The batch JSONL wrapper calls map_openai_params directly and skips get_optional_params,
so a flagged model's temperature, top_p and top_k vanished from the uploaded row with no
400. Batch rows now get the same contract as a real-time call: a 400 naming drop_params,
or the knobs dropped when litellm.drop_params is on
* fix(vertex_ai): ignore null sampling knobs on batch rows for flagged Gemini models
A batch row carrying temperature: null, top_p: null or top_k: null means no
override, the same as on a real-time call, so the row is no longer refused
with a 400 and the null knobs are simply not forwarded
* feat(extraction): add Clairic extraction types and Cloudflare provider config
* feat(extraction): add the /v1/extraction route, router wiring, and Clairic billing
* fix(extraction): refuse or drop stream, pass unknown keys, run guardrails, keep the requested model name
Clairic does not stream, so stream=true answers 400 and is dropped and billed under drop_params. Keys LiteLLM does not know pass through to Cloudflare. Guardrails scan state, field instructions and text answers. Frozen decision and extraction responses now carry the requested model name. List fields must name their item type. /health/test_connection and the Add Model page accept the extraction mode.
* fix(extraction): keep upstream model names on decisions, scan images, keep typed values, refuse OpenAI params
Scope the frozen-response model stamp to extraction so /v1/decisions and /v1/systemone return the upstream name as on main. Pass request images to guardrails as data URLs and drop answer spans when a response guardrail rewrites state_text. Field spec values keep their JSON type, so a wrong type answers 400 instead of being coerced. OpenAI generation params answer 400 unless drop_params. The Model Hub shows a /v1/extraction snippet for extraction models.
* fix(extraction): send only Clairic's request fields, count extraction tokens, keep typed answers typed, drop spans a request guardrail made stale, and honor --max_tokens
* fix(extraction): scan number answers in response guardrails, rewrite guardrail fields in linear time, and leave --max_tokens as on main
* fix(extraction): scan float answers as whole numbers so fraction digits never trip a response guardrail
* fix(extraction): round only mean and median for response guardrails so fractional values keep their decimal text
* fix(extraction): count state and fields for budget reservations and scan estimate quantiles in response guardrails
* fix(extraction): send each repeated text to guardrails once and keep the context-window budget reservation
* test(extraction): type request_data in the guardrail test helpers
* feat(proxy): opt-in free-tier allowance for Google Search grounding spend
* fix(proxy): declare the search free tier counter's service target
* test(cost_map): allow search_free_requests in the cost map schema
* fix(proxy): skip cache hits and never free more than the billed cost in the search free tier
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(cost-map): add volcengine doubao-seed-2-1-pro-260915 and doubao-seed-2-1-lite-260915
Pro row absorbed from #47372, lite row added from the same Volcengine Ark pricing and model list docs
Co-authored-by: donghongfei <1247543758@qq.com>
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(cost-map): cover volcengine doubao-seed-2-1-pro-260915 row
Co-authored-by: donghongfei <1247543758@qq.com>
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(cost-map): add undated azure/eu and azure/us rows for o1, o3, o3-mini, o4-mini, gpt-4o, gpt-4o-mini
Absorbed from #47499
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: donghongfei <1247543758@qq.com>
* feat(dashscope): price Qwen implicit context-cache reads separately
* fix(dashscope): normalize implicit cache price strings
* fix(dashscope): price explicit cache hits through an explicit_ override key
Keep cache_read_input_token_cost as the default (implicit) Qwen cache-read
rate, which the cost map already holds, and add
explicit_cache_read_input_token_cost for hits DashScope tags with
cache_type "ephemeral". Untyped and unknown cache types keep the base rate,
so older releases reading the remote cost map keep billing unchanged.
Add explicit cache-read and cache-creation rates to the qwen3.7-max rows.
* fix(types): accept explicit_cache_read_input_token_cost as a constructor keyword
* fix(dashscope): inherit the explicit cache price on custom deployments and keep tier rates all-or-nothing
Deployments that set only input and output prices now inherit
explicit_cache_read_input_token_cost from the cost map, so explicit hits bill
at the explicit rate. A pricing tier without its own explicit rate bills its
own cache_read rate instead of the model-level explicit one, matching how an
off-peak block falls back.
The DashScope calculator now delegates the explicit rate to the shared
resolver, and the register_model warning is back to main's wording.
* fix(dashscope): keep a deployment's own cache-read price for explicit hits and ignore a non-string cache_type
* fix(dashscope): keep cache_type through Responses usage and let a registered cache-read price cover explicit hits
---------
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* feat(search): add DeepSeek as a /v1/search provider
Adds DeepSeekSearchConfig for POST https://api.deepseek.com/v1/tools/search, registers it under search_provider=deepseek, prices deepseek/search at 0.00075 per query and marks search support in provider_endpoints_support.json
* fix(search): reject DeepSeek queries over the 128 character limit before the call
* test(search): escape regex metacharacters in DeepSeek query limit match
---------
Co-authored-by: devin-ai-integration[bot] <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(openai): drop unsupported reasoning effort for gpt-6.1-sol
gpt-6.1-sol accepts low, medium, high, xhigh, and max. none and minimal were still forwarded, because chat completions ignored an explicit supports_none_reasoning_effort=false and the Responses mapper never applied that contract.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(openai): mark the stripped reasoning object as outgoing JSON
Co-authored-by: Cursor <cursoragent@cursor.com>
* Fix Azure responses reading the OpenAI effort map
gpt-6-astra on Azure accepts reasoning.effort none. The responses gate was checking the OpenAI entry, which rejects it.
Signed-off-by: Paul El Khoury <82139421+pOpO1-9@users.noreply.github.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(openai): gate every reasoning_effort level from the cost map on chat and Responses
One gate reads the model's cost-map row for whatever effort level a request
asks for, on Chat Completions and the Responses API, for OpenAI and Azure.
xhigh stays opt-in on chat (Azure also keeps none opt-in); every other level
is refused only when its supports_<level>_reasoning_effort flag is false. A
refused level is dropped under drop_params or answered with a local 400, and
Responses keeps the rest of the reasoning object when it drops effort. The
guards that follow judge temperature by the level the provider will see.
Tests move into the mapped test files and derive the expected outcome for
each level from the map row, so a new GPT-6 model needs only its row.
* test(openai): pin gpt-6.1-sol's refused effort levels and prove the row flags alone drive the gate
* fix(cost-map): azure gpt-6-astra refuses none and honors max on Responses, as live calls show
* test(reasoning_effort): pin the astra max and none flips through the adapter and Foundry paths
* fix(reasoning_effort): keep max out of the route gates and correct the gpt-5.3-codex rows
The same model honors max on Responses and refuses it on Chat Completions, so one row flag
cannot gate it on either route. The gates now skip max, the Azure gpt-6-astra rows keep max
off so the /v1/messages adapter degrades to xhigh and /model_group/info stops short of it as
on main, and chat still forwards max for the Responses bridge.
The gpt-5.3-codex rows said none and xhigh off and minimal on; the live model answers the
other way round (2026-10-01), and an opt-out gate would have dropped the levels Codex CLI sends.
* fix(reasoning_effort): bridge function tools to Responses when the chat gate drops none
* docs(reasoning_effort): correct the max advertisement note
* fix(azure_ai): read the Foundry row on the Responses effort gate and turn none off for Azure gpt-6.1-sol
* fix(azure): gate a prefixed Foundry deployment's Responses effort off its row
litellm.responses(model="azure_ai/<deployment>", api_base=<Foundry host>) is
re-routed to the azure provider with the azure_ai/ prefix intact, so the Azure
Responses config saw a prefixed name, skipped it as another provider's
pass-through, and forwarded none and minimal to Azure even under drop_params.
The Azure gpt-5 resolver also read no row at all for a prefixed name.
The Azure Responses gate now detects the family the way the Azure chat gate
does, and an azure_ai/ name on the Azure OpenAI configs reads its azure_ai/ row
when the map has one, else the azure/ twin, the rule the Foundry chat config
already applies to the same deployment.
* fix(cost-map): turn none off on azure/eu/gpt-6-astra like its sibling rows
* docs(reasoning_effort): say how each gate reads none and shorten the module docstring
* fix(azure_ai): call the public effort-level check from the Foundry Responses gate
* test(reasoning_effort): fold the Foundry Responses tests into the mapped file and type the row helpers
---------
Signed-off-by: Paul El Khoury <82139421+pOpO1-9@users.noreply.github.com>
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
* fix(baseten): list current Model APIs instead of retired 2023 deployment IDs
`baseten/*` wildcard expansion and `/v1/models` read a hardcoded set of
three 2023 deployment IDs (Falcon 7B / WizardLM / MosaicML) that no longer
exist, while the model cost map already carries current Baseten entries.
- Populate `baseten_models` from the model cost map, like fireworks_ai.
- Add `BasetenConfig.get_models()` against the Model API `/v1/models`
endpoint and register it in `ProviderConfigManager.get_provider_model_info`,
so `check_provider_endpoint: true` returns the live catalog.
- Remove the retired deployment IDs from `constants.py`.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* fix(baseten): mark retired Model APIs deprecated and skip them in listings
15 Baseten entries in the model cost map are no longer servable: the Model
API returns 410 "model version ... has been deprecated" (DeepSeek-V3.1
returns 400 deactivated), and none appear in `inference.baseten.co/v1/models`.
- Set `deprecation_date` on those entries (kept for spend tracking).
- Skip entries past their `deprecation_date` when building `baseten_models`,
so `baseten/*` expansion only lists models that still serve.
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
* test(baseten): assert the custom api_base model listing on the returned models
The custom api_base test only inspected the patched client's call args, which trips the
TQ002 mock-echo gate. Serve the custom URL with respx and assert on the models that come
back instead, so a wrong host or a broken parse both fail.
* fix(baseten): drop newly retired models on cost map reload and fake discovery at the HTTP boundary
A cost map refresh only ever added to the Baseten set, so a model that gained a past
deprecation_date after boot stayed listed under baseten/* until restart. Discard it on reload.
Tests now serve the catalog with respx instead of patching our own HTTP client, the
isinstance registration test is gone since the get_valid_models test already proves
discovery end to end, and new test locals carry Final.
---------
Co-authored-by: AlexKer <AlexKer@users.noreply.github.com>
Co-authored-by: Claude Opus 5.5 <noreply@anthropic.com>
Co-authored-by: ryan-crabbe-berri <ryan@berri.ai>
* feat(count_tokens): configurable token_count_framing and token_count_fallback_model, count source in the response
Count endpoints now say where each number came from in litellm_count_source
(provider, fallback_model, local_estimate) and name the fallback model in
litellm_count_fallback_model. When the provider answers 400 or 404 for a model
it cannot count, the proxy counts the same request with token_count_fallback_model.
token_count_framing adds a per-model constant to local estimates. Both settings
ship in the cost map for Bedrock Claude Sonnet/Haiku 5.5, Bedrock Kimi K3, and
Databricks Kimi K3 and DeepSeek V4 Flash, and can be overridden in model_info.
* test(count_tokens): classify the new cost map keys in the schema generator and expect count_source in exact-shape tests
* test(count_tokens): e2e matrix against real inference and integration wire tests for count source, fallback and framing
* fix(count_tokens): keep the provider error when local counting is disabled, name the fallback error, keep settings in cooldown
Moves the cost map lookup off the event loop
* ci(cost map): bound token_count_framing to non-negative and token_count_fallback_model to non-empty
* fix(count_tokens): keep a failed fallback's own status, ship fallbacks only where they count, add system.ai framing
A fallback model that answers 404 returns the requested model's error unchanged, and any other fallback
failure answers with its own status and message. The eu, apac, au and jp Sonnet and Haiku 5.5 entries
no longer ship the Opus 5.5 fallback, which AWS serves only in us-east-1. A configured empty
token_count_fallback_model turns the shipped fallback off. Config framing accepts whole-number floats and
rejects negatives. The Databricks system.ai Kimi K3 and DeepSeek V4 Flash names carry the same framing as
their serving endpoint names, and the e2e matrix runs on Bedrock Haiku 5.5 too.
* test(databricks): list the system.ai Kimi K3 and DeepSeek V4 Flash names with their published DBU rates
* fix(count_tokens): skip a token count fallback model for an hour after it answers 404 on that deployment
A us. or global. Sonnet 5.5 deployment outside us-east-1 paid two extra Bedrock
calls and about 200 ms on every count, since Opus 5.5 cannot count there either.
The Kimi K3 e2e also runs the large system prompt shape now.
* test(count_tokens): use the shared integration port allocator in the fallback wire test
* fix(cost map): keep the Databricks system.ai entries to framing only, so their spend stays as today
* test(count_tokens): cover the fallback 404 skip, inference profile base_model, shipped framing, and deployment-scoped token count settings
Adds integration cells for a fallback model that answers 404, an application inference profile ARN deployment whose base_model carries the shipped fallback, and the shipped Kimi K3 framing with token_count_framing 0 turning it off. Adds unit tests that keep token count settings off the shared backend cost map key and that store a token count setting on PATCH only when the operator changed it
* fix(count_tokens): keep the counter's own 400 and the provider's error when a framed estimate cannot read the body
A 400 the OpenAI or Gemini counter returns before calling the provider is the caller's answer, so it no longer tries the fallback model or a framed estimate. When the framed local estimate cannot read a content block, the client gets the provider's error instead of a 500. Adds /audit round 2's integration tests for the fallback leg's converted body, the Anthropic envelope on fallback failures, and both framing cases.
* fix(count_tokens): answer the provider's error whenever a framed estimate raises
A tool schema the local estimate cannot format, such as a boolean subschema or an array with no items, raised AttributeError or KeyError and answered a 500 on a framed deployment whose provider had already answered 400. Any estimate failure now answers the provider's own error.
* fix(count_tokens): load the custom tokenizer inside the guarded framed estimate
A framed deployment whose custom_tokenizer could not load answered a 500 or 503 where the provider had already answered 400, because the tokenizer was selected outside the estimate's error handling. Selecting it inside answers the provider's error, logs a warning with the estimate's error, and still raises the Rust extension's fork-guard errors.
* fix(count_tokens): look up framing under the deployment's custom_llm_provider
* fix(count_tokens): share one tokenizer download per worker and bound the wait after a provider error
A framed deployment whose custom_tokenizer is not cached locally made every
count wait out the Hugging Face hub's whole retry cycle when the hub could not
be reached, and a burst of those loads held the default thread limiter that
every count's cost map lookup also needs. Counts on a worker now share one
download per minute, a failed download is reused for the rest of that minute,
and after a provider error a count waits at most 5 s before answering the
provider's error. The swallowed-estimate warning logs only the error type.
* fix(count_tokens): keep one tokenizer download per key running and load the local route per request
* test(count_tokens): type the scripted counter and wait on events instead of wall time where possible
* fix(count_tokens): run framed tokenizer loads on their own capped threads and answer a tokenizer panic with the provider error
Framed loads used anyio's default 40 threads, so a hub that drops packets let about 40 hanging loads stall every count on the worker. Loads now run on a per-worker pool of 4, and a count that finds 4 loads running answers the provider's error at once. Any finished load is reused for a minute after it finishes, so a load that fell back to tiktoken is not retried on every count. A pyo3 PanicException from the tokenizer, while loading or counting, now answers the provider's error instead of a plain 500
* feat(proxy): count tokens with token_count_model in its pinned region
token_count_model {model, region} replaces token_count_fallback_model. Bedrock Sonnet and Haiku 5.5 count with Opus 5.5 in us-east-1, or Melbourne for au. profiles, and a failure names the region to enable
* fix(count_tokens): use the renamed count model constant in the e2e test and list token_count_model with the object keys
* fix(proxy): keep the upstream error envelope when adding the region hint
* test(proxy): annotate the token count setting echo test
* ci(cost map): require non-blank token_count_model strings and test the region rules
* fix(count_tokens): answer the token_count_model's error even when framing is set
* fix(count_tokens): keep the provider error for Google contents instead of a framed estimate
* fix(count_tokens): estimate Google contents the route translated to messages
* fix(count_tokens): keep the provider error for Google contents with non-text parts
* test(proxy): drive tokenizer wait and cost map tests by events and a skippable loop clock
* fix(count_tokens): check Google text-only parts without a KeysView comparison basedpyright flags
* feat(proxy): count Bedrock Kimi K3 tokens exactly with its pinned tokenizer
* test(bedrock): keep the Kimi K3 image check in-process
* fix(count_tokens): join the region hint to an upstream message that already ends with a period
Also pins that a blank token_count_model in config falls through to the cost map default
* fix(count_tokens): count Gemini tool-call turns and Responses tools, and stop tokenizer retries from stalling counts
The Google countTokens route estimates contents with functionCall, functionResponse and thoughtSignature parts again, and still answers the provider error for media, thought and unknown parts
The Kimi K3 exact count renders Responses API and Anthropic tools through the chat tool translation the Vertex count already used, now shared from the prompt factory, and falls back to the estimate when the Converse body cannot carry a tool
A failed Kimi K3 tokenizer load is retried in the background without making counts wait, and a load still running after the wait logs a warning
* fix(count_tokens): count Kimi K3 tools the way Bedrock inference sends them
A flat Responses API function or custom tool becomes the chat tool the
Responses route sends to inference, and every other tool goes to the
Converse body as is, so hosted tools Bedrock drops are left out of the
count instead of being counted or turning it into an estimate.
* test(count_tokens): annotate new token counter test locals with Final
* fix(count_tokens): keep shipped count regions inside the deployment's geography
A plain Bedrock Sonnet or Haiku 5.5 deployment in a non-US region no longer sends its count prompt to us-east-1. Global count models and configured token_count_model keep their pinned region. Also expect litellm_count_source in the Vertex Llama count test main added
* fix(count_tokens): resolve count settings off the event loop
* fix(bedrock): send native structured output first on Opus 4.8 and Sonnet 5 and fall back to the json tool when Bedrock rejects it
* fix(bedrock): retry with the json tool when Bedrock refuses the native output schema
Count a 400 under output_config.format.schema: as a native output rejection, so a schema Bedrock cannot compile (like a pattern with a nested {n,m} group) gets the json tool retry instead of reaching the caller
* fix(bedrock): make the native output retry send what an opted-out deployment sends
The retry after a native output rejection now reruns the same optional param
mapping completion() ran, with the deployment model_info opted out of
supports_bedrock_native_structured_output, so tool_choice, effort and the json
tool schema match an opted-out deployment instead of a hand-built tool. Each
config keeps only the predicate that decides when native output came from the
opt-in key.
Bedrock batch records for Anthropic models map with the opt-in turned off,
since a batch job has no retry. Models on supports_native_structured_output
keep native output in batch
* fix(bedrock): build the messages retry from the caller's request and keep other errors unchanged
* fix(bedrock): give sync converse stream errors the upstream body as text
* fix(bedrock): let a malformed caller outputConfig reach Bedrock instead of raising
A Converse json_schema request whose caller outputConfig failed litellm's
shape check raised a pydantic error before the request was sent, so the
caller got a 500 where the merge base passed Bedrock's 400 through. The
opt-in check now treats that outputConfig as carrying no native output.
Also opts the vertex wire test's pinned Opus 4.8 json tool deployment out of
native output, and extends the Bedrock native output integration tests.
* fix(bedrock): send a refused caller outputConfig once and give opt-out advice only when native output is unsupported
A caller can send its own outputConfig on Converse. When Bedrock refused that
schema on an opt-in deployment, the retry still carried the caller's textFormat,
so Bedrock got the same request twice and the warning told the operator to turn
off an opt-in that was not the cause. The retry is now skipped when the opted-out
request would still carry a native textFormat, so Bedrock's 400 reaches the
caller after one call as on the merge base. The opt-out advice is logged only for
the account-level "Extra inputs are not permitted" rejection.
* fix(bedrock): keep the caller's continuation message on the native output retry
The Converse transform pops user_continue_message from litellm_params on the first attempt, so a retry built from those params afterwards dropped it. Both Bedrock chat routes now hand the fallback a copy taken before the first attempt.
* test(bedrock): pin additional_drop_params on the /v1/messages native output retry
* fix(bedrock): give the Converse native output retry the fake stream timeout
* feat(openinfer): add OpenInfer as OpenAI-compatible provider
Register OpenInfer in the JSON provider registry with catalog models,
proxy create fields, and unit coverage so requests route via openinfer/
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(openinfer): assert catalog invariants instead of vendor rates
Pinning per-token prices made CI fail when the vendor list changed. Keep
the backup agreement and positivity checks our code owns.
Co-authored-by: Cursor <cursoragent@cursor.com>
* fix(openinfer): add openinfer to the Rust OpenAI-compatible provider mirror
* fix(openinfer): align catalog with OpenInfer's live /v1/models
Replace the four catalog rows with the two models OpenInfer serves and
publishes a price for (@oi/GLM-5.3-Flash, @oi/Qwen3.5-27B), using its
listed per-token rates and context windows, and price-check them in tests
* fix(openinfer): use OpenInfer's native Responses and Messages APIs, allow tools on unpriced models, price Qwen3.5-9B
Declare /v1/responses and /v1/messages for openinfer, since OpenInfer serves both natively. Let JSON providers declare provider-wide capabilities in providers.json, read through get_provider_info, so tool calls reach OpenInfer for models without a cost map row. Send max_completion_tokens as is, set output caps to each model's context window, mark the three rows as reasoning models, add Qwen3.5-9B at its listed rate, and wire openinfer into models_by_provider so the openinfer/* wildcard lists its models.
* fix(openinfer): let a model's own capability row win over provider defaults in get_model_info
Provider defaults now only fill capability fields the cost-map row leaves unset, so a row or deployment that turns function calling off stays off. Also marks openinfer_models as Final.
---------
Co-authored-by: Cursor <cursoragent@cursor.com>
Co-authored-by: mateo-berri <277851410+mateo-berri@users.noreply.github.com>
* fix(router): stop a chat-mode Mantle deployment from rewriting the shared Responses routing row
A deployment with an explicit custom_llm_provider and model_info.mode: chat
used to overwrite the shared litellm.model_cost row, moving every other
deployment and plain SDK calls onto Chat Completions until restart, because
the downgrade guard read a double-prefixed key and never fired. The guard now
reads the real key, and a deployment's own mode decides its routing in
responses_api_bridge_check, except on chatgpt and github_copilot where Chat
Completions is dead for responses-mode models.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* test(responses): keep the bridge-check mode tests in the sharded bridge flag file
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): let a deployment mode pin replace only the shared-row mode so the bridge capability rules still run
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): keep Responses-only backends bridged under a deployment chat pin and satisfy the type gate
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): give the bridge casts a reason and drop the bare noqa so the type discipline gate passes
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(responses): keep a responses row with no supported_endpoints bridged under a deployment chat pin
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* fix(router): read the downgrade guard's mode from the row registration writes
A chat-mode openai/<name> deployment merged into the bare <name> row while the guard read
openai/<name>, so the guard never fired and the shared responses row turned to chat. An explicit
responses/ prefix now also outranks a deployment's chat pin.
* fix(router): let an explicit responses/ prefix outrank a registered chat row
The router now registers a chat-pinned openai/responses/<model> deployment under its own name, so the
lookup found mode chat and the request went to Chat Completions as responses/<model>.
* fix(router): keep the shared row's mode for every deployment pin and honor the pin on /v1/messages
A deployment's mode pin now never rewrites the shared cost-map row in either direction, including the responses/-stripped twin key, so siblings and SDK calls keep routing by the registered mode. The backend key formula goes back to main's, which keeps capability fields off rows that registration never wrote at base. The /v1/messages adapter and handler read the deployment's model_info, so a chat-pinned deployment gets a plain reasoning effort on chat completions.
* fix(router): keep copilot responses opt-in, make unmapped pins order-free, send Mantle auto summary as auto
A responses pin on a github_copilot model or a model missing from the cost map still marks the shared row, since that is how those models opt into the Responses API. A chat pin never marks a row, so registration order no longer matters. Mantle's OpenAI path gets summary auto in place of the auto summary detailed. The Azure o3-pro and o3-deep-research rows drop Chat Completions, which Azure does not serve for them.
* fix(router): skip a non-string model_info.mode when keeping the registered mode
* fix(router): keep chat-pinned Mantle tool calls and summaries on the Responses API
A Mantle row that also serves the Responses API now follows the GPT-5 tool and
reasoning summary bridge rules, since Mantle Chat Completions rejects function tools
on gpt-5.4+ unless reasoning is off and rejects the summary aliases. Mantle chat
gets a dict reasoning_effort as its effort level.
The opt-in auto summary asks each Responses config for its level, auto on Mantle's
OpenAI path, so a client's own detailed summary follows drop_params as at base.
No mode replaces a registered responses mode, and chatgpt and github_copilot rows
take any pin until they are responses, which restores the Copilot chat opt-out.
* fix(responses): ask the target for its auto summary level on the chat bridge and reasoning_effort
* test(integration): run the Mantle region-prefixed v1 base case again
This PR routes that deployment by its own chat pin, so the case passes and no longer needs the skip
* fix(bedrock_mantle): keep web search and reasoning summaries on the Responses API under a chat pin
A chat-pinned Mantle GPT-5 deployment now sends web_search_options and a dict reasoning_effort carrying a summary to the Responses API, which serves them. Mantle Chat Completions drops n above 1 and a reasoning summary under drop_params and answers 400 without it. The /v1/messages adapter probes the bridge with only the client's own summary, so an auto-injected one never moves the request
* fix(bedrock_mantle): check n and reasoning summaries only on the Chat Completions request
* fix(router): keep a non-string deployment mode off the shared routing row
A deployment whose model_info.mode was an int, a list, or an object still
reached the shared cost-map row, so an unpinned sibling on the same backend
stopped routing to the Responses API. The shared registration now drops a
mode that is not a string, and the deployment keeps its own raw value.
Adds integration cells for per-deployment routing across the OpenAI and
Anthropic SDKs, raw httpx, wildcard routes, malformed modes, an upstream
outage, config changes under traffic, and a worker kill plus restart.
* test(integration): find each routing cell's spend row by the response it logged
A bridged call logs its row under the wrapped Responses id, which the caller never sees on a streamed Messages answer, so look the row up by the answer's own ids and the upstream ids for its marker. Int and list modes 500 their own model group on main too, so those cells check only the sibling, and the summary cell sends the auto level Mantle's Responses path accepts
* fix(bedrock_mantle): run extra_body fields through the chat checks
A chat-pinned Mantle request that nests n or reasoning_effort in
extra_body now gets the same 400, drop, and effort-level handling as
the top-level fields. The bridge test injects a MockTransport client
instead of patching HTTPHandler.post.
* test(router): cover nested reasoning_effort routing, messages thinking summaries and per-target auto summary levels
* fix(router): probe a pinned deployment's own API on /health and keep a requested summary beside an extra_body effort
* fix(router): keep a custom-priced deployment's mode pin off the shared backend row on each request
* fix(router): run per-request custom pricing through the shared mode guard so a health probe keeps the row's mode
* fix(router): drop the debug log that wrote a request's model name from keep_registered_mode
* test(router): cover custom-priced chat pins, their health probes and summary aliases beside an extra_body effort
* fix(proxy): pass only the mode pin to a /health probe so a failing probe never cools its deployment down
* test(router): cover failing health probes leaving their deployment out of cooldown, with and without background checks
* test(router): kill the worker that holds burst calls, so the worker kill cell never depends on how the kernel spread them
* fix(router): route a fine-tune the same whichever deployment registers first
Main's register_model now seeds an unmapped fine-tune's row from its base
model's built-in row, mode included, on the first registration. The mode
guard read only the live row, so a responses pin marked the shared row only
when it registered before its siblings. The guard now reads the row the
registration merges onto, the live row or else the row it would seed.
---------
Co-authored-by: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
* 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>
* 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
* 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>
