Gate the reasoning_effort translation on the cost-map flag or the model name containing
claude, so unmapped Claude serving endpoints keep translating. Flag the newer Claude
entries that were missing it. Expose supports_anthropic_thinking_payload as a public
helper next to the other supports_* wrappers instead of importing the private factory.
Drop the adaptive-only guard, since the adaptive flags only ever match Claude ids, and
add regression tests for an unmapped Claude endpoint and an adaptive Claude model
Replace the hand-rolled Meta realtime handler with a MetaRealtimeConfig
that plugs into the shared realtime handler and RealTimeStreaming relay.
Clients keep speaking the OpenAI realtime wire: session.update,
input_audio_buffer.append/commit and the OpenAI transcription events.
Unsupported transcription settings are logged and dropped, matching the
Gemini realtime precedent, and the Meta-specific session.mode, keywords,
language_bias, DIARIZATION and speaker extensions are removed.
Drop the MODEL_API_KEY env var in favor of the standard META_API_KEY,
remove the private-logging flag so spend logs record the transcript the
same way other realtime models do, and add per-second pricing for
muse-voice-transcribe-1.0.
The relay now sends raw bytes from transform_realtime_request straight to
the backend after pace_backend_send, and transcription sessions never
trigger response.create.
LiteLLM sends the Bedrock Mantle GPT rows through Bedrock's Responses endpoint, which refuses minimal on gpt-5.4 and gpt-5.5 like every other Bedrock GPT row. The earlier commit measured the raw chat endpoint, which accepts it, and dropped the flag by mistake. The ladder test now matches what the proxy path returns
Live calls to Bedrock Mantle and Converse on 2026-09-11: the gpt-5.6 luna, sol, and terra rows and gpt-6-astra return 200 on reasoning_effort=max, gpt-6-astra returns 400 on none, and Mantle gpt-5.4 and gpt-5.5 return 200 on minimal. The commercial Bedrock rows now carry exactly those flags, and the schema test asserts the measured ladder per row instead of a blanket mirror of the direct OpenAI rows
W&B's serverless catalog grows faster than the registry names it, so a model
they ship today resolves as non-reasoning here until someone edits the cost map,
and the caller's reasoning_effort is dropped or rejected.
Add a wandb-reasoning-baseline capability rule to fallback_generalizations so any
wandb/ id the map has not described defaults to supports_reasoning. Rules lose to
exact entries, so mapped non-reasoning models such as
wandb/meta-llama/Llama-3.1-8B-Instruct are unaffected.
The rule carries no mode and no pricing, so cost stays on the standard unpriced
behavior and the deployment does not read as catalog-mapped to the router's
reasoning-effort resolver.
Claude-Session: https://claude.ai/code/session_01A6SkwJdfZUmkzfUkrEkqX8
The converse reasoning gate only matched openai.gpt-5, so gpt-6-astra fell through to
Anthropic's thinking block and Bedrock rejected the call with 400 Unknown parameter:
'thinking'. Match any openai.gpt-<digit> model at the three gate sites instead.
Nova 2 lite and pro accept forced tool_choice on Converse (verified live on
us.amazon.nova-2-lite-v1:0), so the nine Nova 2 registry keys now advertise
supports_tool_choice. The invoke dispatcher also forwards json_mode to Nova like it
already does for Anthropic and TwelveLabs.
litellm's azure_ai config rejects reasoning_effort for gpt-chat-latest and Azure documents a fixed reasoning level for it, so the entry no longer advertises reasoning_effort_levels. The catalog metadata tests compare cost_per_token and the whisper transcription cost with the entry the calculator read instead of with list-price literals, the pattern #40195 removed
Direct litellm.cost_per_token callers that name a Model Router deployment as
the model get the routing fee again, as they did before this branch, and the
fee is still charged exactly once on every completion_cost path. The
grok-4-20 entries bill cached prompt tokens at the input rate, since Azure has
no cached-input meter for them, and the model_router twin carries the same
limits and retirement date as model-router. The catalog test now exercises
the cost calculator and map relations instead of pinning map fields.
The router fee was folded into azure_ai.cost_per_token and then added again
by the additional_costs hook, so every routed request paid it twice. The hook
now owns the fee, the entry named by the deployment supplies the price, and a
response priced as the router entry itself is not charged again
model-router, gpt-chat-latest and cohere-command-a carry the limits from the
Foundry models page, and model-router and grok-4-20-* carry their retirement
dates. The router tests now run at the completion_cost level with a Logging
object, which is the path the proxy takes, and fail at the merge base
AWS prices Marengo 2.7 and 3.0 text and image embeddings per request, never per
token, and their responses carry no token count. The old transform estimated
prompt tokens from the vector length, which billed a text request at 128 tokens
times the per-token rate (0.00896 instead of 0.00007). Marengo responses now
report zero tokens with query_count and image_count derived from the request
batch, and all six Marengo cost-map entries price per request (with the video
and audio per-second and per-image rates on the base entries). query_count is a
new prompt_tokens_details field wired to input_cost_per_query in the cost
calculator.
Add cost map entries for azure_ai/gpt-chat-latest, codex-mini, whisper,
model-router, cohere-command-a, grok-4-20-reasoning, and
grok-4-20-non-reasoning, priced from the live Azure AI Foundry and Azure
OpenAI pricing pages and the Azure Retail Prices API.
Skip the model router flat fee when the response model is the router
entry itself, since the generic cost already priced that fee. Before,
azure_ai/model_router charged it twice.
Resolves LIT-3157
