models.litellm.ai and released litellm versions read
model_prices_and_context_window.json from main at runtime, so Kimi K3 is
missing from the hosted catalog even though the entry is in review for
litellm_internal_staging in #37552. This copies that entry onto main so
the catalog picks it up on its next fetch.
Data only: the cost map and its backup copy, no code changes. Pricing
matches Moonshot's published rates ($3/M input, $0.30/M cache read,
$15/M output, 1,048,576-token context). The fireworks_ai and Azure
Foundry kimi-k3 variants are separate work in #37512 and #37658; neither
touches the native moonshot/kimi-k3 key.
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>
The new perplexity/perplexity/glm-5.2 row carried 2.6e-07, which is glm-5.3's
cache read rate. api.perplexity.ai/v1/models publishes 0.14 usd per 1M cached
input tokens for glm-5.2, so the rate is 1.4e-07.
Combines the model-cost-map data from #35911, #36017, #36080, #36113, #36188, #36444, #37029, #37252 and #37632 onto current litellm_internal_staging, merged per entry field so older branches no longer revert fields the base has gained since they were opened. Drops the Gemini deprecation dates from #36188 and the text-embedding-004 date from #36080 that the official docs contradict.
Co-Authored-By: Devin AI <158243242+devin-ai-integration[bot]@users.noreply.github.com>
Mistral's live /v1/models reports max_context_length 1048576 and capabilities.reasoning
true for zai-glm-5-2, and its docs price cached input at $0.14/M. Without
cache_read_input_token_cost LiteLLM billed every cached prompt token at $0, so a repeat
request against a 21k-token cached prefix logged $0.0000135 instead of its real cost.
Mistral also serves the model under the short glm-5-2 name, which had no cost map entry
at all and therefore no pricing, so add it alongside.
The cache control hook also runs on litellm.responses() input. On a
GPT-5.6 deployment it wrapped a string-content item into a chat-shaped
{"type": "text"} part, which the Responses API rejects, and it never
marked input_text, input_image or input_file parts, so no breakpoint and
no prompt_cache_options reached the provider. Add the Responses part
types to the eligible block set and translate chat-shaped text parts on
non-assistant items to input_text in
ResponsesAPIRequestUtils.merge_prompt_management_input, which both the
async and the sync prompt management sites go through.
The dialect also fired for any GPT-5.6 name that resolved to provider
openai, including deployments pointed at a custom api_base that does not
understand prompt_cache_breakpoint. Decide it once per request from the
provider, the model map and the resolved api_base (request, then
litellm.api_base, then OPENAI_BASE_URL / OPENAI_API_BASE): only
api.openai.com and *.api.openai.com hosts speak the dialect, a top-level
prompt_cache_options opts a custom target in, and litellm_proxy/ targets
never get it. maybe_seed_default_injection_points takes api_base and
stamps the finished decision on the points as _litellm_openai_dialect so
the sync completion() path, whose hook params do not carry api_base,
honors it; maybe_inject_cache_control takes api_base from the
/v1/messages handler.
Eligibility now comes from a supports_prompt_cache_breakpoint model map
flag on the OpenAI gpt-5.6 entries, exposed through
litellm.utils.supports_prompt_cache_breakpoint, with the GPT version rule
kept only for models the map does not know. The OpenAI dialect no longer
reserves a slot for tool_config points, which OpenAI has no cache block
for, and with_prompt_cache_breakpoint plus the chat bridge helper return
a new block instead of mutating their input.
Price ApplyGuardrail usage units recorded by PR #37225 with a new
bedrock/guardrails entry in the model cost map (regional override via
bedrock/{region}/guardrails), add the per-request guardrail_cost to the
standard logging payload's response_cost and CostBreakdown, surface it in
the x-litellm-response-cost header, and bill blocked requests through the
failure hook so key and team budgets see what AWS bills
