Register mimo-v2.6-flash-free on opencode-zen (chat lane) with a v2.6
capability pattern, and switch the vision adapter default from the old
mimo-v2.5-free.
Co-Authored-By: Claude Code <noreply@anthropic.com>
- Export aggregateComboCapabilities: union for vision/audio/search/pdf,
intersection for tools, primary-model for reasoning fields, min
contextWindow, max maxOutput
- Support nested combo resolution in aggregateComboCapabilities via
comboLookup with depth guard (max 6)
- Wire capability metadata to all /v1/models entries and combos
- Show aggregated ctx/max metadata line and capability badges on combo chips
- Pattern fixes: MiMo v2.5/omni reasoning, qwen max/plus vision, minimax m2.x vision
- Sync commandcode model catalog and add openai gpt-5.5
- Add unit tests for capability patterns and combo capability aggregation
Route union-alpha to /zen/v1/messages with targetFormat claude, add anthropic-version header, and register model capabilities (vision, 262K context, 131K max output).
- Declare deepseek-v4.1-flash and deepseek-flash as vision-capable in MODEL_CAPABILITIES
- Share installed catalogSource across route chunks via globalThis.__9rCatalogSource
- Scope catalog modality keys by provider:model to prevent cross-gateway collisions
- Upgrade catalog format to v2 with automatic rebuild of older schemas
Command Code dropped vision and ignored client effort through the router:
image blocks became "[image omitted]", HTTP image URLs were never inlined,
and reasoning_effort landed on the envelope wrapper instead of params (so the
DeepSeek family mapping remapped low -> high). The catalog also treated
deepseek/deepseek-v4.1-flash as text-only, so the vision adapter stole those
requests to another provider.
- Map OpenAI image_url / Claude image blocks (base64 or data-URI) to the
native {type:"image", image:"data:...;base64,...", mimeType} generate block.
- Add FORMATS.COMMANDCODE to TARGETS_NEED_BASE64 so remote http(s) images are
inlined by the existing SSRF-safe fetcher before translation.
- Write reasoning_effort inside params for targetFormat commandcode and pass
low|medium|high|xhigh|max through unmapped; allow it in thinkingLevels.
- Provider-scoped capabilities for commandcode/cmc: vision except the CLI
text-only denylist, thinkingFormat commandcode, so family patterns
(deepseek-v4 -> thinkingFormat deepseek, vision false) no longer win.
- Quota Tracker: whoami + billing credits/subscriptions (credits vs plan cap,
5h and weekly windows), labels from AI_PROVIDERS[].name.
codebuddy-intl: deepseek-v4-flash replaced by deepseek-v4.1-flash (same
gateway catalog as CN) and a capability override so the model keeps the
openai-style reasoning_effort path instead of the vendor-native "deepseek"
thinking shape the gateway rejects. Thinking levels low/high/xhigh.
ollama: add deepseek-v4.1-flash:cloud (verified on ollama.com/api/tags) with
vision + 1M context caps.
Co-Authored-By: Claude Code <noreply@anthropic.com>
- Coalesce Qoder's empty finish-in-delta frame with the later choices:[] usage
frame so OpenAI and Claude clients receive prompt_tokens, completion_tokens
and cache-hit tokens (the dashboard already saw them)
- Upload inlined images through /api/v2/image/upload like qodercli, and stub
oversized non-image files instead of stuffing 30MB+ data URIs into
agent_chat_generation
- Emit response.completed -> response.usage for chat-native upstreams so
/v1/responses clients (Codex CLI, sub2api) no longer log 0/0/0
- Keep Claude message_delta.usage working when usage arrives without choices[0]
- Escalate to the smallest advertised Qoder context tier (200K/400K/1M) when
the estimated prompt no longer fits max_input_tokens
- Pass apiKey for PAT connections and list hidden enable:false catalog keys
from /v1/models
The server's product-config payload (which the IDE plugin fetches from
copilot.tencent.com) publishes deepseek-v4.1-flash and no longer lists
deepseek-v4-flash, so the old id is dropped — same pattern as the previous
catalog refreshes (#3648, #3802). The v4-flash endpoint still answers 200,
but the published list is the contract.
Per the server table, maxOutput rises 50000 -> 128000 while contextWindow
stays 1000000.
- registry/codebuddy-cn.js: models[] entry swapped to the new id
- capabilities.js: per-model entry swapped, maxOutput -> 128000
No changes needed in thinkingLevels.js (the deepseek-v4* pattern already
matches the new id and publishes low/high/xhigh, matching the server's
supportedEfforts or pricing.js (the deepseek-v* glob yields the same rates).
EOF
)
- Sync codebuddy-cn catalog and capabilities with copilot.tencent.com server payload
- Fix thinkingCanDisable semantics for glm-5.3 and deepseek-v4 models
- Add missing glm-5.2 thinking levels to thinkingLevels.js
- Add glm-5-turbo model to glm and glm-cn registries
- Registry/constants: drop qmodel_preview/gm51model, add lite,
qmodel_38max (Qwen3.8-Max), qfmodel (Qwen3.8-Flash), gmodel (GLM-5.3),
gfmodel (GLM-5.3-Flash)
- capabilities: add PROVIDER_CAPABILITIES['qoder'] so opaque internal
ids resolve to their real models' context windows and limits
- executor: preserve image blocks instead of flattening away, convert
Claude-style image blocks, and hash images into chat_record_id
- tests: cover image preservation, data-URI and Claude-block conversion
- build(docker): use CN mirrors for apk and npm
Route all Muse Spark models (not just 1.2) on OpenCode Free to
/zen/v1/responses via isMuseSparkModel(), fixing HTTP 500 on
muse-spark-1.3-contributor-free. Declare vision:true on Muse Spark
models so image input is no longer stripped; register 1.3 in the
registry and capabilities. Scoped to opencode only — other providers
keep Chat Completions routing.
- add claude-fable-5-1 to the Claude Code model catalog (1M context,
permanent adaptive thinking)
- centralize the spoofed Claude Code version and update both request
and billing identities to 2.1.257 (Fable 5.1 rejects < 2.1.251)
- send output_config.effort without the redundant thinking switch for
permanently adaptive models
- add regression coverage for capabilities, headers, billing identity
and adaptive-effort payload
# Conflicts:
# open-sse/providers/registry/claude.js
# open-sse/providers/shared.js
# open-sse/utils/claudeCloaking.js
# tests/__baseline__/providers-baseline.json
Z.ai / GLM-5.2+ require a top-level reasoning_effort (low/high/max)
alongside thinking:{type:"enabled"} to control reasoning depth; the zai
branch previously only set thinking and dropped reasoning_effort, so every
GLM-5.x request ran at the model default (max). Gate the field behind
GLM-5.2+ (thinkingEffortSupported in capabilities.js) since older GLM
(4.x, 5.0, 5.1, 5-turbo, 5v-turbo) do not read it, and map client levels
to the exact low/high/max values z.ai accepts.
extractThinking now checks reasoning_effort/reasoning.effort before the
thinking object so a client-supplied effort is not overwritten by
thinking:{type:"enabled"} mapping to mode:auto.
Fixes#2721
muse-spark-1.2-contributor-free returned HTTP 500 on /zen/v1/chat/completions.
The model is only served by /zen/v1/responses, so route it there via a per-model
targetFormat and normalize the Chat fields the Responses API rejects
(max_tokens -> max_output_tokens, reasoning_effort -> reasoning{effort,summary}),
clamping max/ultra down to the highest effort the model accepts (xhigh).
Routing stays per-model: the other free models (big-pickle, hy3-free, mimo,
nemotron, laguna) are not served by /responses and keep /chat/completions.
Capability tables are hand-maintained, so a model gains vision or a wider
context only when someone notices and edits the file. This adds a daily
sync that fills the gap for models already in the registry.
How it decides:
- Modalities (vision/pdf/audio/video) belong to the MODEL — every gateway
serving glm-5.3-flash serves the same weights — so they are keyed by
model id and shared. A majority of sources must declare one, which keeps
out lone mis-declarations: minimax-m2.5 (1 of 45), glm-4.7 (1 of 44) and
gpt-oss-120b (2 of 76) are text-only despite a reseller claiming vision.
- Context/output limits belong to the GATEWAY — each truncates differently
(glm-5 ships as 202752/16384 on one host and 204800/131072 on another) —
so they are keyed by provider + model and only the matching provider's
own numbers are trusted.
Both layers are strictly additive and sit BELOW the hand-written tables,
which short-circuit first. A capability already true stays true.
Mechanics: worker thread (the 4MB parse would block the loop ~20ms),
ETag so an unchanged catalog costs one empty request, 60s startup delay,
30min backoff on failure, MODEL_CATALOG_SYNC=off to disable. Only the
~57KB delta is kept; lookups cost ~0.1us via an mtime-guarded cache.
capabilities.js is bundled into the browser through useModelCaps, so it
cannot import node:fs — the server injects the reader via
setCatalogSource() from instrumentation.
visionPatterns.js is the last resort: a model nobody has catalogued yet
still accepts images when its id says so (qwen3-vl-plus, glm-4.6v, llava),
with image-generation and embedding ids excluded.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Vendors shipped four multimodal models the registry did not carry:
- glm-5.3-flash — z.ai's first natively multimodal GLM-5, 1M context,
image + video + pdf input (glm, glm-cn, opencode-go)
- deepseek-v4-flash-vision-exp — image input at V4-Flash text parity,
1M context / 384k output (deepseek, opencode-go)
- grok-4.6, grok-4.5 — 500k context; 4.6 has no text output limit (xai)
Capabilities needed hand entries because the existing globs mis-matched:
*glm-5* and *deepseek-v4* carry no vision, and *grok-4* would have capped
grok-4.6 at 256k instead of 500k. The grok-4.6 pattern sits above the
generic *grok-4* so it wins the first-match lookup.
Also corrects glm-4.6v / glm-4.5v, which were missing video input and
declared no maxOutput, and backfills glm-4.6v on glm-cn — zhipuai serves
it and the sibling provider already listed it.
tests/unit/opencode-go-models.test.js pins the opencode-go model list, so
its expected array moves with the registry.
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Add gemini-3.7-flash and its tiered high/medium/low variants to the
Antigravity and Gemini registries, with matching capabilities, pricing
and Antigravity quota tracking.
extractModel now recognises gemini-3.7-flash-tiered alongside 3.6 and
derives the version from the request, so thinkingLevel still maps to the
right tiered alias.
Closes#3286Closes#3281
- Enable vision + audioInput capacity-adapter pools by default for new
and existing users (mergeWithDefaults backward-compat)
- Fall back to oc/mimo-v2.5-free when an enabled pool has no models
configured, both in the backend resolver and the combos UI (auto
refill on removing the last model from a pool)
- Hide PDF/Video from the Vision Adapter UI (PDF never implemented,
Video lacks translator support) while keeping the settings shape
- Exclude combos from the model picker when opened from the Vision
Adapter section
- mimo-v2.5 registry entry now declares audioInput/videoInput
- Simplify combo strategy and Vision Adapter descriptions
Adds Poolside (inference.poolside.ai) as an API-key provider using the default OpenAI transport. Registers three Laguna models with reasoning capabilities (262K context, 32K max output).
Add GPT-5.6 Sol/Terra/Luna and their synthetic thinking/agentic/
thinking-agentic variants to the Kiro static catalog with the observed
272k context window and credit multipliers (2.4/1.2/0.6), register MITM
mapping slots for the new base ids, and override runtime capabilities so
the GPT-5.6 family reports the 272k window instead of the generic GPT-5
profile.
- Updated capabilities for NVIDIA models to enforce OpenAI-compatible reasoning formats.
- Added new models: MiniMax M3, GLM 5.2, DeepSeek V4 Pro, DeepSeek V4 Flash, Kimi K2.6, and Nemotron 3 Ultra to the NVIDIA registry.
This enhances the provider's functionality and aligns with OpenAI standards.
Add qwen omni (audio/video input), qwen3.5/3.6/3.7 (native vision/video),
and mark qwen coder & max as text-only reasoning models.
Co-authored-by: Cursor <cursoragent@cursor.com>
Registry exposes the dashed id claude-opus-4-7; matchPattern treats "."
as a literal, so it missed the dotted pattern and fell through to the
generic claude opus entry (200k / claude-budget). Add an exact entry so
it resolves to 1M context + adaptive thinking, plus a unit test covering
the dashed Opus ids.
Co-authored-by: Cursor <cursoragent@cursor.com>
Add 1M context capability overrides for claude-opus-4.8 and -thinking
variants, and use the resolved capability contextWindow (fallback 200k)
instead of the hardcoded 200k estimate in the Kiro executor.
Co-authored-by: Cursor <cursoragent@cursor.com>
The pattern matcher marked *minimax-m3* as vision: false, causing
9Router to strip image attachments before forwarding upstream. This
broke Claude Code / Cursor / Cline vision flows when routing through
MiniMax-M3.
Scoped vision: true to *minimax-m3* only. M2.7 and the catch-all
*minimax* pattern remain vision: false: those models are text-only
(per MiniMax docs / NVIDIA NIM model card), so forcing vision there
would send images to a model that errors instead of degrading.
Co-authored-by: Cursor <cursoragent@cursor.com>
Expose user-added imageToText custom models as vision-capable chat
models in the default LLM selector and /v1/models, map custom service
kinds to runtime capabilities, and keep typed filtering for
/v1/models/{kind}.
Co-authored-by: Cursor <cursoragent@cursor.com>
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