[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"skill-venice-ai-venice-models":3,"mdc-mu1an4-key":31,"related-org-venice-ai-venice-models":3859,"related-repo-venice-ai-venice-models":4026},{"slug":4,"name":4,"fn":5,"description":6,"org":7,"tags":12,"stars":20,"repoUrl":21,"updatedAt":22,"license":23,"forks":24,"topics":25,"repo":26,"sourceUrl":29,"mdContent":30},"venice-models","discover Venice AI models and capabilities","Discover Venice models, their capabilities, constraints, and pricing. Covers GET \u002Fmodels (with ?type filter), \u002Fmodels\u002Ftraits, \u002Fmodels\u002Fcompatibility_mapping, the ModelResponse schema (capabilities, constraints, pricing per type), and how to use this to pick the right model programmatically.",{"slug":8,"name":9,"logoUrl":10,"githubOrg":11},"venice-ai","Venice AI","https:\u002F\u002Fpexgzepcugksgbtrxkhf.supabase.co\u002Fstorage\u002Fv1\u002Fobject\u002Fpublic\u002Forg-logos\u002Fvenice-ai.png","veniceai",[13,17],{"name":14,"slug":15,"type":16},"LLM","llm","tag",{"name":18,"slug":19,"type":16},"Reference","reference",119,"https:\u002F\u002Fgithub.com\u002Fveniceai\u002Fskills","2026-07-17T06:07:32.35724",null,15,[],{"repoUrl":21,"stars":20,"forks":24,"topics":27,"description":28},[],"Agent Skills for the Venice.ai API. One folder per surface area, each with a SKILL.md for agent runtimes (Cursor, Claude, Codex, etc.).","https:\u002F\u002Fgithub.com\u002Fveniceai\u002Fskills\u002Ftree\u002FHEAD\u002Fskills\u002Fvenice-models","---\nname: venice-models\ndescription: Discover Venice models, their capabilities, constraints, and pricing. Covers GET \u002Fmodels (with ?type filter), \u002Fmodels\u002Ftraits, \u002Fmodels\u002Fcompatibility_mapping, the ModelResponse schema (capabilities, constraints, pricing per type), and how to use this to pick the right model programmatically.\n---\n\n# Venice Models\n\nThree read-only endpoints for model discovery — all `GET`:\n\n| Endpoint | Returns |\n|---|---|\n| `\u002Fmodels` | Full model catalog with `model_spec` (capabilities, constraints, pricing). |\n| `\u002Fmodels\u002Ftraits` | Trait → model ID mapping (e.g. `\"default\"`, `\"fastest\"`, `\"default_reasoning\"`, `\"highest_quality\"`). |\n| `\u002Fmodels\u002Fcompatibility_mapping` | Legacy \u002F OpenAI \u002F third-party model ID → Venice model ID aliases. |\n\nAll three take an optional `?type=` filter: `text`, `image`, `video`, `music`, `tts`, `asr`, `embedding`, `upscale`, `inpaint`, `all`, `code`.\n\nAll three are authenticated (Bearer API key or x402 SIWE) like every other `\u002Fapi\u002Fv1` route.\n\n## Use when\n\n- You need to pick a model at runtime based on capabilities (vision, reasoning, function calling, E2EE, X search, multi-image, …).\n- You need to validate a request against a model's `constraints` (prompt length, aspect ratio, resolution, steps).\n- You need the current **price per million tokens \u002F per image \u002F per second \u002F per 1k chars** to build a cost estimate.\n- You want to resolve a user-friendly trait name (e.g. `default`, `default_reasoning`, `highest_quality`) or a frontier-style ID (`openai-gpt-54-pro`, `claude-opus-4-7`) to a concrete Venice model ID.\n\n## `GET \u002Fmodels`\n\n```bash\ncurl \"https:\u002F\u002Fapi.venice.ai\u002Fapi\u002Fv1\u002Fmodels?type=text\"\n```\n\n```json\n{\n  \"object\": \"list\",\n  \"type\": \"text\",\n  \"data\": [\n    {\n      \"id\": \"zai-org-glm-5-1\",\n      \"created\": 1699000000,\n      \"model_spec\": {\n        \"name\": \"GLM 5.1\",\n        \"description\": \"Balanced blend of speed and capability...\",\n        \"availableContextTokens\": 200000,\n        \"maxCompletionTokens\": 24000,\n        \"privacy\": \"private\",\n        \"beta\": false,\n        \"betaModel\": false,\n        \"modelSource\": \"https:\u002F\u002Fhuggingface.co\u002Fzai-org\u002FGLM-5.1\",\n        \"offline\": false,\n        \"capabilities\": { ... },\n        \"constraints\": { ... },\n        \"pricing\": { ... },\n        \"regionRestrictions\": [\"US\"],\n        \"deprecation\": {\"date\": \"2025-03-01T00:00:00.000Z\"}\n      }\n    }\n  ]\n}\n```\n\n### `model_spec.capabilities` — text models\n\n| Flag | Meaning |\n|---|---|\n| `optimizedForCode` | Tuned for coding tasks. |\n| `quantization` | `fp4` \u002F `fp8` \u002F `fp16` \u002F `bf16` \u002F `int8` \u002F `int4` \u002F `not-available`. |\n| `supportsFunctionCalling` | Tools are allowed. |\n| `supportsReasoning` | Emits `\u003Cthinking>...\u003C\u002Fthinking>` blocks (and\u002For provider-specific `reasoning_content`). |\n| `supportsReasoningEffort` | Honors `reasoning.effort` \u002F `reasoning_effort`. |\n| `supportsResponseSchema` | Honors `response_format: json_schema`. |\n| `supportsMultipleImages` + `maxImages` | Multi-image vision support. |\n| `supportsVision` | Accepts `image_url` parts. |\n| `supportsVideoInput` | Accepts `video_url` parts. |\n| `supportsWebSearch` | Honors `venice_parameters.enable_web_search`. |\n| `supportsLogProbs` | Honors `logprobs` \u002F `top_logprobs`. |\n| `supportsTeeAttestation` | Runs inside a TEE with hardware attestation. |\n| `supportsE2EE` | End-to-end encrypted inference available (requires TEE). |\n| `supportsXSearch` | xAI native web + X\u002FTwitter search. |\n| `supportsAudioInput` | Accepts audio-content message parts (set by the runtime capability builder — not part of the OpenAPI strict schema but appears on `\u002Fmodels` responses). |\n\n### `model_spec.constraints` — by model family\n\n- **Text** — `temperature.default`, `top_p.default`, and `{frequency,presence,repetition}_penalty.default`.\n- **Image** — `promptCharacterLimit`, `widthHeightDivisor`, `steps.{default,max}`, optional `aspectRatios[]` + `defaultAspectRatio`, optional `resolutions[]` + `defaultResolution` (the last two appear only on models that use ratio\u002Fresolution-based sizing).\n- **Video** — `aspect_ratios[]`, `resolutions[]`, `durations[]`, `model_type` (`text-to-video`\u002F`image-to-video`\u002F`video`), `audio`, `audio_configurable`, `prompt_character_limit`.\n- **Inpaint \u002F edit** — `aspectRatios[]`, `promptCharacterLimit`, `combineImages`.\n- **TTS \u002F Music** (fields surface at the top level of `model_spec`, not inside `constraints`) — `voices[]`, `default_voice`, `supports_lyrics`, `lyrics_required`, `supports_lyrics_optimizer`, `supports_force_instrumental`, `supports_speed`, `supports_language_code`, `min_speed`, `max_speed`, `min_prompt_length`, `prompt_character_limit`. Internal TTS per-model toggles like `supportsPromptParam` \u002F `supportsTemperatureParam` \u002F `supportsTopPParam` exist on the model definitions but are **not** merged into `\u002Fmodels` output today — treat the speech request schema as the support matrix.\n- **Embedding** (top-level, not inside `constraints`) — `embeddingDimensions`, `maxInputTokens`, `supportsCustomDimensions`.\n\n### `model_spec.pricing` — by model family\n\n- **LLM** — `input.{usd,diem}`, `output.{usd,diem}` per 1 000 000 tokens, plus optional `cache_input` (reads), `cache_write` (writes, e.g. Anthropic 1.25×), and `extended.*` tier triggered by `context_token_threshold`.\n- **Image** — either `generation.{usd,diem}` per image (flat) or `resolutions.\u003Ctier>.{usd,diem}` (per `1K`\u002F`2K`\u002F`4K`). Every image row also carries a global `upscale.{2x,4x}.{usd,diem}` block (derived from shared upscale SKUs) — treat it as account-wide upscale pricing, not a signal that this specific model can upscale. Combine with the inpaint\u002Fupscale model's own capability check to decide what's actually callable.\n- **Inpaint \u002F edit** — `inpaint.{usd,diem}` per edit.\n- **Video** — **not currently returned on `\u002Fmodels`.** `calculatePricing()` has no video branch, so video entries have no `model_spec.pricing`. Use `POST \u002Fvideo\u002Fquote` for the authoritative per-request price.\n- **Music \u002F long audio** — `generation.{usd,diem}` (per job), `per_second.{usd,diem}` (per second generated), `per_thousand_characters.{usd,diem}` (character-priced narration), or `durations.\u003Ctier>.{usd,diem,min_seconds,max_seconds}` (duration-bucketed).\n- **TTS** — `input.{usd,diem}` per **1 000 000 input characters**.\n- **ASR** — `per_audio_second.{usd,diem}`.\n- **Embeddings** — `input.{usd,diem}` per 1 000 000 tokens.\n\nCrypto RPC pricing is **not** in `\u002Fmodels` — it's tier × chain multipliers on `\u002Fcrypto\u002Frpc\u002F{network}` (see [`venice-crypto-rpc`](..\u002Fvenice-crypto-rpc\u002FSKILL.md)).\n\n### Other top-level `model_spec` fields\n\n| Field | Use |\n|---|---|\n| `privacy` (`private` \u002F `anonymized`) | Zero data retention if `private`. |\n| `beta` \u002F `betaModel` | Gated to beta users (`beta: true` ⇒ need access). |\n| `offline` | Currently unavailable; skip. |\n| `regionRestrictions[]` | Country codes. `403` outside them. |\n| `deprecation.date` | Retirement date. Migrate before. |\n\n## `GET \u002Fmodels\u002Ftraits`\n\n```bash\ncurl \"https:\u002F\u002Fapi.venice.ai\u002Fapi\u002Fv1\u002Fmodels\u002Ftraits?type=text\"\n```\n\nReturns `{ object: \"list\", type: \"text\", data: { \"default\": \"zai-org-glm-5-1\", \"fastest\": \"grok-41-fast\", \"most_uncensored\": \"venice-uncensored\", \"default_reasoning\": \"...\", \"default_code\": \"...\", \"default_vision\": \"...\", \"function_calling_default\": \"...\", \"most_intelligent\": \"...\" } }` for `type=text`. For `type=image`, expect keys like `default`, `fastest`, `highest_quality`, `eliza-default`. Trait keys come from the internal `ApiModelTraits` \u002F `LLMApiModelTraits` \u002F `ImageApiModelTraits` enums.\n\nUse this to avoid hard-coding model IDs — resolve a trait at boot and cache for the session.\n\n## `GET \u002Fmodels\u002Fcompatibility_mapping`\n\n```bash\ncurl \"https:\u002F\u002Fapi.venice.ai\u002Fapi\u002Fv1\u002Fmodels\u002Fcompatibility_mapping?type=text\"\n```\n\nReturns `{ object: \"list\", type: \"text\", data: { \"openai-gpt-54-pro\": \"zai-org-glm-5-1\", \"claude-opus-4-7\": \"claude-opus-4-7\", \"gpt-5-4-pro\": \"openai-gpt-54-pro\", ... } }`. Both OpenAI-style IDs (`openai-gpt-54-pro`) and vendor-style aliases (`gpt-5-4-pro`) may appear as keys.\n\nLets an OpenAI-style client call Venice with its native model IDs — Venice substitutes behind the scenes. Useful when porting existing code.\n\n## Common patterns\n\n### Pick a vision+reasoning model at runtime\n\n```ts\nconst list = await fetch(`${base}\u002Fmodels?type=text`).then(r => r.json())\nconst match = list.data.find((m: any) =>\n  m.model_spec.capabilities.supportsVision &&\n  m.model_spec.capabilities.supportsReasoning &&\n  !m.model_spec.offline &&\n  !m.model_spec.beta\n)\n```\n\n### Validate an image request before submit\n\n```ts\nconst spec = (await fetch(`${base}\u002Fmodels?type=image`).then(r => r.json()))\n  .data.find((m: any) => m.id === myModel)!.model_spec\n\nconst { widthHeightDivisor, promptCharacterLimit, aspectRatios } = spec.constraints\nif (prompt.length > promptCharacterLimit) throw new Error('prompt too long')\nif (width % widthHeightDivisor !== 0) throw new Error('width not divisible')\nif (aspectRatios && !aspectRatios.includes(myAspect)) throw new Error('bad aspect')\n```\n\n### Estimate LLM cost\n\n```ts\nconst p = spec.pricing\nconst cost =\n  (inputTokens \u002F 1_000_000) * p.input.usd +\n  (outputTokens \u002F 1_000_000) * p.output.usd +\n  (cachedTokens \u002F 1_000_000) * (p.cache_input?.usd ?? 0)\n```\n\nFor extended-context runs, check if `inputTokens > p.extended?.context_token_threshold` and switch to `p.extended.*` rates.\n\n## `?type=code`\n\n`type=code` is a convenience filter returning text models with `capabilities.optimizedForCode === true`. Same response shape as `type=text`.\n\n## Gotchas\n\n- The catalog changes — cache for minutes, not days.\n- `beta: true` models require beta-flagged keys — otherwise `401` with \"only available to Pro users\".\n- `offline: true` means the model exists in the catalog but can't currently serve requests — treat it as absent for scheduling.\n- `model_spec.pricing` can be **missing** on free \u002F internal models — guard against `undefined`.\n- `traits` differ by `type` — there's no \"global default\"; always pass `?type=...`.\n- `compatibility_mapping` resolves model IDs, not capabilities. 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