Backfill 21 serverless chat models, the multilingual-e5 embedding model, and
Llama-Guard-4-12B from the live Together catalog with per-token pricing and
capability flags. Mark 25 delisted together_ai entries with their documented
deprecation_date and point superseded models at a live successor via metadata.
Reprice Llama-3.3-70B-Instruct-Turbo to Together's current rate.
Six defects in the RunwayML video provider:
- transform_video_create_request hardcoded /image_to_video, so text-to-video 400'd and video-to-video was unreachable; the endpoint is now selected from the inputs present (promptVideo/videoUri, promptImage, or text only)
- get_error_class raised instead of returning, turning a provider 4xx into a proxy 500 APIConnectionError; it now returns a RunwayMLError
- VideoObject.progress was typed int while Runway sends a 0..1 float, 500'ing status polls while RUNNING; it is now scaled to a 0..100 percent
- custom per-deployment pricing stored under litellm_metadata was ignored for video; the deployment model_info lookup now checks both metadata keys
- stale cost-map entries (gen3a_turbo, gen4_aleph) were removed and current models added, with output_cost_per_second_480p/_4k tier keys plumbed through the model-info and router types
- video cost now falls back to Runway's estimatedCost from the create response when no custom pricing is configured, and custom pricing always wins over it
Fixes#36483
Databricks documents databricks-claude-fable-5 as accepting text only,
where every sibling Claude endpoint accepts text and image. An earlier
commit flipped the flag to true on the reasoning that fable-5 was the
only entry of its generation declaring false, which had it backwards:
it is the only one because the endpoint really does refuse images.
Advertising vision here would surface the model in capability filters
and hand the caller a provider-side rejection.
databricks-claude-fable-5 was the only fable-5 entry in the registry
declaring supports_vision false, and the only one of the five new
entries to do so.
Only the five new models were pinned against the published DBU table,
so the 26 cache literals added to pre-existing entries were checked by
nothing independent. Extend the table to all 33 entries carrying cache
rates and assert both cache fields against it for the 31 that take the
published rates, leaving the two older-vintage gemini-2-5 entries to
their existing guard.
Also widen the cache-declaration guard to both cache fields, and
replace the single-model equals-input assertion with one that covers
all 14 entries publishing no cache rates.
Cache rates were derived as ratios of the dollar input rate (1.25x write,
0.1x read) while input and output derive from the published DBU table
times $0.070. Databricks publishes cache write and cache read DBU per
model, and those are not exact multiples of the input DBU, so the two
rules disagreed by up to 0.1 percent.
Rewrites 43 cache literals across 31 entries to published_cache_DBU x
$0.070. Skips databricks-gemini-2-5-pro and databricks-gemini-2-5-flash,
whose input and output rates predate the current table by a 1.25x
increase; their cache rates stay tied to their own input rate so each
entry remains internally consistent.
Replaces the ratio assertions with a test pinning the absolute published
DBU figures for the five new models, and adds a test pinning the
older-vintage exception. Corrects the metadata note on the five new
entries, which claimed the reference-only *_dbu_cost_per_token fields
drive cost calculation.
The figures came out of a float product of the DBU rate and the dollar
rate, so the map carried representation tails such as
2.9999900000000006e-07 where the derivation only means 2.99999e-07.
Trim the 44 values this change adds or edits to the shortest literal that
round-trips to the same figure, leaving every pre-existing value alone.
The largest move is under 1e-15 relative, so no billed amount changes.
The shared cost calculator treats a missing cache rate as free, so routing
Databricks through it billed cached tokens at zero on the 14 entries that
publish no cache pricing. On a 10,000 token prompt with 8,000 cache reads
that is $0.0010000 against the correct $0.0050001, a fivefold undercharge.
Those entries now declare cache rates equal to their input rate, which is
what a model with no caching discount should charge, and a test pins every
priced Databricks entry to declaring cache rates so no future entry can
regress into it.
Also repoints the provider-neutral generalization test off an id the new
Opus 5 entry now shadows, adds backup-to-main parity tests for the five new
entries, pins that Databricks Claude is never auto-injected with cache
control despite reporting caching support, and trims the Sonnet 5 pricing
note, which is served on an unauthenticated route.
The introductory DBU rates run through 2026-08-31 and pricing carries no
expiry date, so a static introductory entry would undercharge by a third
from September 1 and let spend outrun enforced budgets. Ship the standard
rates, which match Sonnet 4.5 and 4.6, and keep the introductory numbers
in the entry notes.
Also give the new cost calculator tests full type annotations.
Databricks cost calculation multiplied every prompt token by the input rate, so
a cache read cost the same as an uncached token. Route it through
generic_cost_per_token, which already understands cache reads and cache writes,
and add the cache rates the registry was missing.
Adds Claude Opus 4.7, Opus 4.8, Opus 5, Sonnet 5 and Fable 5 on Databricks.
* fix(pricing): add undated azure aliases for gpt-audio-mini and gpt-realtime-mini
Azure deployments are commonly created against the undated model name,
and the cost-tracking docs say to set base_model to azure/<model> — but
only the dated -2025-10-06 entries existed for these two models (the
openai provider has undated aliases for both). base_model:
azure/gpt-audio-mini therefore resolved to nothing and, depending on the
fallback path, text tokens billed at $0 while audio tokens billed fine.
Mirror the -2025-10-06 entries as undated aliases, exactly like the
undated openai entries mirror their newest dated variant.
Fixes#33170
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* test(pricing): assert undated azure audio aliases exactly mirror their dated entries
Review follow-up: COST_FIELDS missed realtime-specific cost keys
(cache_creation_input_audio_token_cost, cache_read_input_token_cost,
input_cost_per_image). Full-entry equality catches drift on every field.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(pricing): mirror updated mode=realtime on the undated gpt-realtime-mini alias
Upstream changed the dated entry's mode from chat to realtime after this
branch was cut; the undated alias must stay a byte-for-byte mirror.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
* fix(pricing): mirror the new deprecation_date onto the undated gpt-audio-mini alias
* test(pricing): use shared local_model_cost_map fixture so get_model_info's lru_cache never crosses maps
---------
Co-authored-by: Mihidum Hettiyahandi <55163074+mihidumh@users.noreply.github.com>
Co-authored-by: Claude Fable 5 <noreply@anthropic.com>
OpenAI's model page for gpt-5.6 serves the GPT-5.6 Sol page and states
that the gpt-5.6 alias routes requests to GPT-5.6 Sol, so the alias bills
at Sol's rates. The registry entry was left on the pre-cut rates while
gpt-5.6-sol took the cut, overbilling gpt-5.6 callers by 25 percent on
input and 50 percent on output.
All 23 cost fields on gpt-5.6 now match gpt-5.6-sol, and a regression
test pins the two entries together so they cannot drift again.
The three lite-image keys landed on the deploy branch separately while this
branch was open, so merging left every key defined twice in both price maps.
The merge is clean as text and the file still parses, but JSON keeps the last
occurrence of a repeated key, so the first copy's supported_endpoints,
supported_modalities and supports_system_messages were being dropped without
any error.
Each key is now one entry, placed next to its gemini-3.1-flash-image sibling
rather than at the end of the file.
supports_reasoning goes to false on all three, matching every other Gemini
image model. Leaving it off is not neutral: _supports_factory falls through to
the vertex_ai provider config, which answers true, and reasoning_effort then
gets forwarded to an image endpoint that rejects it. That was fixed for the
rest of the family in 75dd70a678 and these entries had drifted back.
Also fills in what the entries were missing against Google's published
pricing: the Vertex implicit cache read rate, batch rates on the Vertex
routes, and the pdf/video input flags.
The two overlapping test files are folded into one, and the price map suite
grows a duplicate-key guard so the next clean-but-lossy merge fails loudly.
GPT-5.6 Sol, Terra and Luna reached the bedrock-runtime data plane on
2026-08-17, separately from the existing bedrock-mantle path. On runtime
they are served only through cross-region inference profiles, so
bedrock/us.openai.gpt-5.6-* had no cost map entry and fell through to the
Invoke route, which rewrites the token cap to max_tokens and is rejected
as unsupported_parameter on both /v1/chat/completions and /v1/responses.
Register the Geo and Global profiles as bedrock_converse so routing
reaches Converse, which AWS documents and serves for these models, and
price each profile from its own published rate table. No bare key: the
control plane reports inferenceTypesSupported INFERENCE_PROFILE with no
on-demand throughput, so a bare id is not invocable.
Declare the published cache-read and cache-write rates. Bedrock rejects
an explicit cachePoint block for these models, so supports_prompt_caching
stays off, but it caches long prefixes implicitly and reports the cache
tokens in usage either way. Without the cost fields a cache-read turn
bills only its uncached tokens: measured against live Bedrock, a
15609-token cached prefix came to $0.000176 instead of $0.00876095.
Clients that resend a long prefix every turn are the worst affected.
Reasoning stays unadvertised. Converse rejects the Anthropic-shaped
thinking block LiteLLM sends for reasoning_effort; the shape these models
accept is additionalModelRequestFields {"reasoning": {"effort": ...}},
which needs a transform change tracked by #34105. Advertising it without
that change is what made the earlier attempt in #37307 fail.
Applies the review suggestions. The cost map now carries the rates published on
https://scx.ai/pricing, GLM-5.2 at 0.61 in, 0.22 cached, 1.98 out and
Qwen3.8-Max at 1.65 in, 0.21 cached, 4.99 out per million tokens, and cites that
page as the source rather than a third party gateway. The provider link is
corrected to https://docs.litellm.ai/docs/providers/scx_ai to match the page
that shipped as scx_ai.md. Both the primary files and their backup mirrors are
updated.
Resolves the three conflicts against the JSON provider registry refactor. The
hardcoded api.scx.ai base-url branch in get_llm_provider_logic.py is dropped in
favour of the generic JSONProviderRegistry.get_by_base_url lookup, which reads
the same base_url and api_key_env from providers.json and additionally honours
an explicitly passed api_key. constants.py and types/utils.py keep both the
cognition and scx-ai entries added on either side.
The cost map shipped cognition/swe-1.7 at $2.50 in / $12.50 out per million with
$1.00 cache reads. Those are the Lightning numbers. Cognition's own model list at
https://docs.devin.ai/desktop/models has uid swe-1-7 at $0.50 / $2.50 with $0.20
cache reads, and uid swe-1-7-lightning at $2.50 / $12.50 with $1.00 cache reads,
so every swe-1.7 call has been costed at 5x since the entry landed.
swe-1.7 now carries the standard rates and the Lightning tier gets its own entry,
in both cost map copies. The source field on both moves to the desktop models page,
which is the one that lists both tiers.
The swe-1.7 rates were briefly lowered to the standard tier. The docs page
records the API-served swe-1.7 as the Cerebras-served Lightning tier, so put
the matching rates back rather than have the cost map and the docs disagree.
Cognition also answers /v1/responses through the chat-completions bridge, the
same as every other provider in the JSON registry, so the endpoints support
matrix should say so instead of under-declaring it.
The swe-1.7 rates were carried over from the closed prior attempt and
match SWE-1.7 Lightning, 5x the SWE-1.7 Max and Medium rates the vendor
publishes. swe-1.6 was already on the standard tier, so the two entries
disagreed with each other. Both now read 0.5 in, 2.5 out, 0.2 cached per
million tokens.
Also drops the redundant registry comment in constants.py.
Cognition serves an OpenAI-compatible /v1/chat/completions endpoint, so it has been onboarded as
custom_llm_provider: openai. That books its traffic as OpenAI, which means OpenAI-specific cost
discounts and provider-level reporting apply to it.
Registers cognition through the JSON provider registry: a providers.json entry with
COGNITION_API_KEY and COGNITION_API_BASE, LlmProviders.COGNITION, the constants.py provider lists,
cost map entries for swe-1.6 and swe-1.7, the provider endpoints matrix, the dashboard provider
fields, and tests. JSON providers can now also be resolved from their base url alone, so an
api_base pointing at a known provider no longer falls through to an unresolved provider.
The edit model is reached through the image generation path with fal's
image_urls param; /v1/images/edits is not wired for fal_ai and errors.
Point supported_endpoints at /v1/images/generations and say so in the
entry notes.
OpenAI documents computer_use as a supported tool for Daybreak Blue and its
default snapshot gpt-5.6-sol, but neither entry carried supports_computer_use.
Sibling gpt-5.6-cyber and daybreak-red-latest already set it, so /model/info
and the capability gates reported blue as unable to use computer tools.
The gap came in with the source PR rather than the consolidation: #37029 sets
the flag on cyber and red only. Pinned by a new metadata test covering the
daybreak family and the blue alias agreeing with its snapshot.
Route fal.ai's openai/gpt-image-2 endpoints through a dedicated transformation that maps OpenAI image params (n, size, quality, output_format) into fal's schema, and register the model in the cost map.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
