The Together catalog exposes only context_length, so the sync was recording
every chat model's context window as its output ceiling. New entries now carry
max_input_tokens and the legacy max_tokens from the catalog and get an output
ceiling only from a reviewed capability rule. GLM-5.2 and GLM-5.3-Flash rules
carry the documented 128K ceiling, and the 26 other inflated together_ai chat
entries drop max_output_tokens in both registry copies.
119 entries pointed at 404ing or permanently-moved pages (Pylon #7777).
Replaced with verified working equivalents (200-checked or permanent
redirect targets).
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Reprice ten more retired xAI slugs (grok-3 and grok-3-mini families,
grok-4-1-fast) to the grok-4.3 rates they now bill at, with family-correct
deprecation dates. Restore cache_read_input_token_cost on the Bedrock Grok 4.6
entries so implicit cache hits bill at the cache-read rate while explicit
cachePoint stays unsupported. Drop the unsourced 1080p video rate and the
gemini/ live native-audio entry the Gemini API 404s on. Add Groq qwen3.8-27b
tool-use flags per Groq docs. Extend the xai and gemini tests to lock all of
this in
The models API reports 131072 input / 65536 output for the preview model
and the Interactions API accepts 100k tokens but rejects 130k, so the
1,048,576 input limit copied from the docs was wrong.
Gemini omni 1.1 flash and omni flash preview only answer on the Interactions
API, so both now list /v1beta/interactions as their endpoint and 1.1 flash
gets the 131072 / 65536 limits the models API reports.
grok-4.20-multi-agent and -latest now match the dated entry (mode responses,
/v1/responses only), and all three drop function calling and tool choice
since the API rejects client-side tools outside a beta.
kimi-k2.7-code gets the capability flags kimi-k2.6 carries (tools, reasoning,
JSON mode, image and video input) plus max_output_tokens.
grok-imagine-image-2.0 gets a low quality tier at $0.04 so quality=low is
not billed at the $0.06 default.
AWS bills a Bedrock GPT-5.5 or GPT-5.4 prompt past 272K tokens under the long-context usage types for the
whole prompt, at 2x input, 2x cache read, and 1.5x output, and the cost map only had the flat rates, so a
300K prompt was logged at half of what the invoice charges. The map's promo rates for gpt-5.6-sol are 20%
under the $5.50 input, $33.00 output, $0.55 cache read, and $6.88 cache write per million the invoice bills.
Adds the *_above_272k_tokens fields to gpt-5.5 and gpt-5.4, moves sol's base and tier rates to the invoiced
ones, replaces the test that pinned the flat behaviour with one that pins the invoiced numbers, and updates
the sol pins in the mantle transformation tests
A gpt-5 model accepts a non-default temperature only while its effective reasoning
effort resolves to "none". litellm had no representation of the effort a model applies
when the request omits reasoning_effort, so it substituted supports_none_reasoning_effort,
which is a different fact. Every model that supports "none" without defaulting to it
therefore had temperature forwarded and rejected upstream, and because the carve-out
returned before the drop_params branch, drop_params: true could not save it.
Declare the fact instead. A new cost-map key, default_reasoning_effort, states the effort
the provider applies when the request omits one, and one shared predicate resolves the
effective effort from it: an explicit reasoning_effort wins, otherwise the declared
default, otherwise the catalogue decides.
That last step matters because the cost map is fetched from the published branch at import
time, so it can be OLDER than the code reading it. On such a map every model looks
undeclared, and reading that as "reasoning is active" would strip temperature from the 39
gpt-5.1/5.2/5.4 entries that accept it, a regression caused by data lag rather than by
anything about the model. So an absent declaration is only meaningful once the catalogue
carries the key at all; a map that predates the feature keeps the answer litellm gave
before it existed, and the conservative answer applies from the moment the data lands.
The top_p/logprobs/top_logprobs gate carried the same assumption spelled differently and
now shares the predicate, as does the Responses API, which reimplemented the rule and is
what the default /v1/messages bridge routes openai models through. Azure normalises its
routing names in one resolver that every capability lookup goes through, which replaces
its bespoke per-lookup rewrite.
Declared on the 37 gpt-5.1/5.2/5.4 entries measured to accept temperature=0 today, so
their behaviour is unchanged. The 23 gpt-5.5/5.6 entries that reject it stay undeclared
and are fixed once the catalogue carries the key.
Resolves LIT-3797
Resolves LIT-5028
Kimi K3 accepts exactly low, high and max, defaults to max, and always thinks.
The map could not say that: medium and high have no supports_*_reasoning_effort
flag because every other reasoning model takes them, so the ten kimi-k3 entries
carried supports_reasoning alone and resolved to unknown. The dashboard then fell
back to a capability-blind level list that deliberately omits max, which is why a
kimi-k3 tier cannot be set to max thinking today.
Add reasoning_effort_levels, an array key in the shape the map already uses for
supported_endpoints and supported_modalities. Where present it is read first and
wins whole; every other entry keeps answering through the per-level flags,
unchanged. It is deliberately a different name from the computed
ModelGroupInfo.supported_reasoning_efforts, which stays derived from a group's
deployments and is never seeded from one deployment's model_info.
The levels are per entry rather than per model, because the deployments differ:
Moonshot, Together, Fireworks and Azure Foundry all forward the level unchanged
and get the model's own low/high/max, while Perplexity documents a six-value
enum it maps down internally and gets that. The /v1/messages degradation chain
consults the same declaration, so the level the map advertises is the level that
path forwards.
Adds a Gemini audio transcription config that maps /v1/audio/transcriptions
onto the Interactions API (speaker attribution and word timestamps land on
the OpenAI verbose_json shape), registers both models with published pricing,
routes text-only Live sessions to TEXT responseModalities so
gemini-3.5-transcribe-live sessions survive, and makes the token-priced
transcription cost path provider-aware instead of hardcoding OpenAI.
Adds pricing (0.15/0.50 per 1M tokens, 0.03 cached read), the 1M context window, and capability flags (tools, parallel tools, tool choice, response schema, reasoning, vision) for Together AI's zai-org/GLM-5.3-Flash, mirrored into the backup cost map, with exact-value regression tests.
Register both models, route image requests to the multimodal generation endpoint instead of the chat compatible-mode base, and pass OpenAI n through as DashScope n so multi-image requests return every image.
