Replace the direct minimax allowlist with a denylist of the older M2, M2.1,
M2.5, and M2.5-highspeed models, applied where MiniMax models are built.
MiniMax models with models.dev effort levels use adaptive thinking with
output_config.effort and mark thinking off as unsupported, since
MiniMax-M3.1-Flash rejects thinking.type=disabled.
closes#11121
Switch kimi-coding to OpenAI Chat Completions at api.kimi.com/coding/v1,
the protocol the official Kimi Code client uses. K3, K3-256K, and Kimi For
Coding accept tools declared in later system messages, so tools loaded
mid-conversation (e.g. by tool_search) keep the prompt cache. Highspeed
rejects tool-bearing system messages and keeps added tools top-level.
Re-lands b06c1f2 (reverted in 6a1f799b6) with MINIMUM_PI_VERSION bumped
to 1.2.1. Released clients implement only Anthropic Messages for
kimi-coding and ignore a model's api, so they must keep receiving the
last catalog published under 0.80.7.
closes#10720
Reverts b06c1f2. The pi.dev model catalog is served to every client
since 0.80.7, so released clients received kimi-coding models with
api openai-completions and baseUrl https://api.kimi.com/coding/v1.
Their kimi-coding provider only implements Anthropic Messages and
ignores model.api, so requests went to /coding/v1/v1/messages and
failed with 404. The change will re-land together with a bump of the
catalog's minimum pi version.
refs #10720, refs #10985
Both models accept thinking: {type: "disabled"}, but were treated like
Opus 5.5, which cannot turn thinking off. "off" now sends disabled
thinking without effort markers or block_binding, which Anthropic
rejects while thinking is disabled.
fixes#10753
Switch kimi-coding to OpenAI Chat Completions at api.kimi.com/coding/v1,
the protocol the official Kimi Code client uses. K3, K3-256K, and Kimi For
Coding accept tools declared in later system messages, so tools loaded
mid-conversation (e.g. by tool_search) keep the prompt cache. Highspeed
rejects tool-bearing system messages and keeps added tools top-level.
closes#10720
MiMo Chat Completions rejects reasoning_effort "minimal" with a 400.
Xiaomi documents only thinking.type enabled/disabled and treats all effort
levels identically, so stop sending reasoning_effort and expose only the
off and high thinking levels.
fixes#10818
With an API key, GPT-6 models on the openai provider now keep reasoning.effort
fixed and send the thinking level as configuration_update input items. Each
reply records its level, so earlier items are rebuilt unchanged on every request.
Controlled by compat.supportsMidConvoEffort; skipped for Sign in with ChatGPT
and thinking off.
closes#10853
- Use opencode.ai/inference and opencode.ai/inference/go base URLs; /zen/go has no Gemini route (404)
- Add google-generative-ai support to the OpenCode Go provider
- Use Anthropic Messages for OpenCode Go minimax-m2.7; openai-completions now returns ModelProtocolUnsupported
- Remove test for opencode-go deepseek-v4-flash, which is no longer listed
On each OpenRouter refresh, combine the catalog (built-in models plus a newer pi.dev catalog) with GET /models/user for the user's key: keep only the listed chat models and take their context size, max output and prices from it. Image and classifier models are kept. The combined list is saved and replaces the OpenRouter catalog, including offline.
- ai: add personalizeOpenRouterCatalog and RefreshModelsContext.baseUrl, so the request follows a base URL configured in models.json (regional endpoints)
- ai: share OpenRouter price conversion with the generator; negative prices (router models such as openrouter/auto) become 0
- coding-agent: add withPersonalizedCatalog next to withRemoteCatalog
- docs: OpenRouter section in providers.md
fixes#10353
Remove hardcoded Opus 5.5, Sonnet 5.5, and Haiku 5.5 fallbacks now that models.dev lists them. Fix the Sonnet 5.5 test to expect the official cache read price of 0.1.
models.dev now lists the input rate as Kimi K3's cache write cost on Moonshot, which does not bill cache writes. Claude Haiku 5.5 uses adaptive thinking, so the metadata test now expects it.
models.dev loaders for opencode, opencode-go, google, minimax, and others built costs by hand and dropped tiers; they now use getModelsDevCost. OpenRouter prompt-length overrides and Vercel AI Gateway per-rate tiers are parsed into request-wide tiers. Rates a tier does not list keep the base price. OpenRouter time-of-day overrides are skipped.
Adds claude-haiku-5-5 to the anthropic provider with its prompt-length pricing tier (5x above 100k input tokens), adaptive thinking with xhigh/max effort, per-message effort, and mid-conversation system messages and tool changes. Bedrock requests detect Haiku 5.5 for adaptive thinking, native xhigh, block binding, and prompt caching.
Adds the openai-decisions classifier API with gpt-6-luna on the openai provider. bool questions map to predicates with the criteria in the instructions, refusals fail the result, and gateway timeouts (504) are not retried. The model needs an API key and is hidden from availability while openai uses Sign in with ChatGPT.
Adds optional images to ClassifierContext. Models without image input and APIs that cannot send images return an error result. Codemode's models.classify() accepts images.
Moves the shared classifier HTTP code out of system-one-shared.ts into classifier-shared.ts.
* feat(ai): support Azure Foundry Chat Completions deployments
The Azure provider only implemented the Responses API, so Foundry
deployments that speak Chat Completions could not be used. On Chat
Completions the deepseek/deepseek-v4-pro model sends `thinking` and
`prompt_cache_key`, both of which Azure rejects with 400.
The provider now dispatches openai-completions alongside
azure-openai-responses. A stream wrapper resolves the Azure endpoint
onto the model first, since the shared openai-completions
implementation reads it off the model.
The request-shape differences are catalog compat rather than api code:
reasoning_effort instead of DeepSeek's thinking field, no prompt cache
parameters, reasoning_content kept on assistant turns so the prefix
Foundry cached stays byte-identical, and effort clamped to the
low/medium/high the deployment accepts.
The provider is renamed from azure-openai-responses to azure, since it
now serves more than the Responses API. The azure-openai-responses api
id is unchanged. Breaking: auth.json and models.json entries keyed on
azure-openai-responses must move to azure, and existing sessions see a
different provider on their next turn.
closes#9645
* feat(ai): apply the Azure deployment name map to Chat Completions
The Responses path already sent the deployment name from
AZURE_OPENAI_DEPLOYMENT_NAME_MAP. The Chat Completions path sent the
catalog model id, so any deployment not named after the model 404'd.
The Azure provider now swaps the deployment name into the request via
onPayload before dispatch, then runs the caller's own onPayload on it.
model.id stays the catalog id. The map helpers move to
azure-openai-config.ts so the provider can share them without
importing the SDK.
* docs(ai,coding-agent): add changelog entries for the Azure provider rename
Breaking: the azure-openai-responses provider is now azure. Users rename
the key in auth.json, models.json, and settings.json; SDK users switch to
getModel("azure", ...) and providers/azure.
Also note Azure Foundry Chat Completions support (DeepSeek V4 Pro).
---------
Co-authored-by: David Brailovsky <davidbrai@gmail.com>
Add a Nix flake that builds Pi from the checkout on aarch64/x86_64 Linux
and macOS. The build is offline: provider model data is hydrated from a
pi.dev model catalog revision pinned in nix/model-catalog.json, and the
runtime dependency tree comes from packages/coding-agent/install-lock with
internal packages built from source.
The Nix workflow keeps the catalog pin current on main, builds release
tags on all systems, and fast-forwards the stable branch to each release
that builds. The release script refreshes the pin before tagging.
The gateway's /anthropic passthrough forwards the model ID to Anthropic
unchanged. models.dev lists the gateway's Claude models with dotted
versions (claude-opus-5.5), which Anthropic rejects with a 404. Convert
dots to dashes (claude-opus-5-5) when generating the catalog.
Add gpt-6.1-sol to the OpenAI, Azure OpenAI Responses, and OpenAI Codex
providers with official pricing ($2 input, $0.10 cached input, $2.50
cache writes, $10 output, long-context tier above 272k). The model
rejects reasoning.effort "none", so off is unsupported and clamps to
the lowest effort.
System One services (TypeSafe, OpenRouter, OpenCode Zen, Cloudflare Workers AI) return usage.input_tokens/output_tokens. ClassifierResult now carries usage priced from the model catalog like chat usage.
Vercel evaluation models are generated from the AI Gateway catalog and
routed through its TypeSafe-compatible /typesafe/v1/systemone endpoint.
OpenCode Zen's jev-1.13 and jev-1.13-free are routed through
/zen/v1/systemone.
Mistral models with effort levels now use reasoning_effort based on
their thinking level map from models.dev instead of a hardcoded id list.
GLM 5.3 no longer gets prompt_mode, GLM 5.2 can use max, and off sends
the model's off value when it has one.
fixes#9678
OpenRouter decision models use its TypeSafe-compatible /api/v1/systemone endpoint. Cloudflare Workers AI gets a cloudflare-workers-ai-system-one classifier API for the /ai/run envelope. Request and answer parsing is shared in system-one-shared.ts.
Make strict mode as unsupported in cerebras, as mixed strict/unstrict tool usage doesn't work (400 from API), and extensions may define both strict and unstrict tools
Device authorization flow against auth.meta.com mints a Model API key via the Muse Code key-mint endpoint. Identity token stored as refresh, minted key as access; daily re-mint via the standard OAuth scheduler. Identity is non-renewable (no refresh grant), so mint 401/403 directs to /login meta. Models sourced from models.dev; default muse-spark-1.3.
Extract Google catalog processing and apply models.dev reasoning effort
metadata directly for Google, Vertex, and OpenCode models. Keep the
provider-specific Gemma 4 toggle mapping.
This change makes system prompt text and tool changes part of the transcript rather than silently rewriting its starting conditions. This lets Pi record when instructions changed or tools became available, restore that state after resuming or navigating branches, and preserve cached prompt prefixes where the upstream supports it.
