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
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.
The GA vertex model had no cost map entry, and the realtime cost handler
accepted the router's price-less auto-registered deployment entry for the
session.created model at zero-defaulted rates, so sessions billed 0.0 even
when base_model pointed at the priced preview key. Adds the GA entry at its
published rates and makes the handler fall through zero-defaulted candidates
unless their cost map entry explicitly declares pricing.
Gemini API Maps-grounded prompts were billed as web search and Vertex AI Maps-grounded prompts were not billed at all. Classify grounding metadata per candidate into web search vs Maps requests, carry a distinct google_maps_grounding_requests usage counter through non-streaming and streaming paths, and price it via the new google_maps_grounding_cost_per_query cost map key with per-query and per-prompt defaults keyed off web_search_billing_unit. Fixes#35906
Gemini 2.5 Flash Preview TTS, Gemini 2.5 Pro Preview TTS, and the three
gemini-2.5-flash-native-audio entries carried rates copied from the text
models, so audio output was billed 2x to 6x under Google's published
prices. Set the published per-token rates on all ten keys, add
output_cost_per_audio_token to the native-audio entries, and drop the
long-context tier rates Google does not publish for Pro TTS.
