模型
探索 0G 網路上可用的 AI 模型
0GM-1.0-35B-A3B0G.AI in-house model optimized for agentic coding and tool use; thinking enabled by default. Stream long outputs: non-streaming requests must complete within about 10 minutes.
0GM-1.0-35B-A3B-SIAA 35B hybrid MoE model enhanced with per-token reward guidance. At each decoding step, a 4B Value Model scores candidate tokens and steers generation toward higher-quality outputs, with improvements in harmlessness, helpfulness, and honesty.
Qwen3 Embedding 0.6BQwen3-Embedding-0.6B (open weights, Apache-2.0). OpenAI Embeddings API compatible (POST /embeddings: model/input/encoding_format); 1024-dimension dense vectors, 100+ languages including programming languages, 32K-token input. The small, fast member of the family. Instruction-aware: prefix queries with "Instruct: <task>\nQuery: " (documents need no prefix). Billed on input tokens only.
Qwen3 Embedding 8BQwen3-Embedding-8B (open weights, Apache-2.0). OpenAI Embeddings API compatible (POST /embeddings: model/input/encoding_format); 4096-dimension dense vectors, 100+ languages including programming languages, 32K-token input. Instruction-aware: prefix queries with "Instruct: <task>\nQuery: " for best retrieval quality (documents need no prefix). Billed on input tokens only.
Zhipu AI GLM-5.3 for long-horizon reasoning, coding, and agentic tool use; 1M context, up to 131K output. Deep thinking supported; whether it is on by default and whether it can be turned off varies by provider. Function calling, JSON mode, and implicit prompt caching supported. Stream long outputs: non-streaming requests must complete within about 10 minutes.
LayaLaya (Convai Innovations, open weights, Apache-2.0): a non-autoregressive System One decision model, the open alternative to TypeSafe Jev. Same request and response shape as Jev (POST /decisions: model/state/questions, typed noul / choice / score answers with calibrated probabilities instead of text). Routes by language inside the model: English state to a ModernBERT-large checkpoint (512 tokens), other languages to a 100+-language mmBERT checkpoint (1024 tokens); longer state is truncated. Best with fewer than 20 options per choice question. Billed on input tokens only.
Z-Image-TurboAsynchronous text-to-image model with Base64 output. Generates at most 2 images per request — requesting more (n > 2) returns 2 images, not an error.
Multilingual automatic speech recognition (ASR); transcription and translation.
MiniMax-M3MiniMax-M3, a natively-multimodal model on MiniMax Sparse Attention (MSA); agentic coding, native tool use, and long-horizon tasks. 1M context, thinking on by default.
Qwen-FlashAlibaba Qwen-Flash, the cheapest 1M-token-context model of the Qwen line. Text in / text out, hybrid thinking/non-thinking modes, function calling, built-in tools, and structured output.
Qwen3-VL-30B-A3B-InstructAlibaba multimodal vision-language model; strong at visual reasoning, OCR, and document understanding.
Qwen3.5-FlashAlibaba Qwen3.5-Flash, the fast, low-cost tier of the Qwen3.5 series. Text in / text out, 1M-token context, hybrid thinking/non-thinking modes, function calling, and structured output.
Qwen3.7-FlashAlibaba Qwen3.7-Flash, the fast, low-cost model of the Qwen3.7 series. Text in / text out, 1M-token context, hybrid thinking/non-thinking modes, function calling, built-in tools, structured output, and implicit prompt caching.
Qwen-Flash-CharacterAlibaba Qwen-Flash-Character, a role-play / character-dialogue model. Text in / text out, 8K-token context. No function calling and no thinking mode.
Qwen3.5-35B-A3BAlibaba Qwen3.5-35B-A3B, a 35B-total / 3B-active MoE model of the Qwen3.5 series. Text in / text out, 256K-token context, hybrid thinking/non-thinking modes, function calling, and structured output.
Qwen3.7 Text EmbeddingAlibaba Tongyi Lab text embedding model (Qwen3.7-based), OpenAI Embeddings API compatible (POST /embeddings: model/input/dimensions/encoding_format). Single input up to 128,000 tokens and user-selectable output dimension. Billed on input tokens only — the response's `usage` carries no completion_tokens, matching this router's `embedding` service type (input-side billing, no output/completion price).
Qianfan OCRA document-understanding model for OCR, layout parsing and visual question answering over scanned pages; text and image input, text output, 60K-token context, billed per token rather than per page. Image tokens scale with resolution and are billed through prompt_tokens.
Qwen3.5-27BAlibaba Qwen3.5-27B, the dense 27B model of the Qwen3.5 series. Text in / text out, 256K-token context, hybrid thinking/non-thinking modes, function calling, and structured output.
Qwen-PlusAlibaba Qwen-Plus, the long-running balanced model of the Qwen line. Text in / text out, 1M-token context, hybrid thinking/non-thinking modes, function calling, built-in tools, and structured output.
Qwen3.5-122B-A10BAlibaba Qwen3.5-122B-A10B, a 122B-total / 10B-active MoE model of the Qwen3.5 series. Text in / text out, 256K-token context, hybrid thinking/non-thinking modes, function calling, and structured output.
Qwen3.5-PlusAlibaba Qwen3.5-Plus, the balanced tier of the Qwen3.5 series. Text in / text out, 1M-token context, hybrid thinking/non-thinking modes, function calling, built-in tools, and structured output.
ERNIE 4.5 Turbo (2026-04-02)The 2026-04-02 pinned snapshot of Baidu ERNIE 4.5 Turbo, for callers who need behaviour fixed to one build; text in, text out, function calling supported. The upstream does not report cached_tokens, so no cache discount applies.
ERNIE 4.5 Turbo 128KBaidu ERNIE 4.5 Turbo with a 128K-token context window, a low-cost general-purpose model for chat, extraction and classification; text in, text out, function calling supported. The upstream does not report cached_tokens, so no cache discount applies.
ERNIE 4.5 Turbo 32KThe 32K-token context tier of Baidu ERNIE 4.5 Turbo, priced the same per token as the 128K tier; text in, text out, function calling supported. The upstream does not report cached_tokens, so no cache discount applies.
Zhipu AI GLM-5.3-Flash, a fast, cost-effective model for coding and agentic tasks. Text in / text out, 1M context, up to 128K output. Deep thinking is always on and cannot be disabled. Function calling, JSON mode, and implicit prompt caching supported.
Qwen3-Coder-FlashAlibaba Qwen3-Coder-Flash, a fast, low-cost coding model. Text in / text out, 1M-token context, function calling, and structured output.
Qwen3-VL-PlusAlibaba Qwen3-VL-Plus, the high-capability tier of the Qwen3-VL vision-language series: image, video and text in, text out. 256K-token context, hybrid thinking/non-thinking modes, function calling, and structured output.
Qwen3.8-FlashAlibaba Qwen3.8-Flash, a fast, cost-effective multimodal model for coding and agentic work; visual and video understanding, function calling, web search, deep thinking, and implicit prompt caching. 1M context.
Qwen3.5-397B-A17BAlibaba Qwen3.5-397B-A17B, a 397B-total / 17B-active MoE model of the Qwen3.5 series. Text in / text out, 256K-token context, hybrid thinking/non-thinking modes, function calling, and structured output.
Qwen3.6-FlashAlibaba Qwen3.6-Flash, a fast, low-cost model of the Qwen3.6 series. Text in / text out, 1M-token context, hybrid thinking/non-thinking modes, function calling, built-in tools, and structured output.
Hunyuan-3Tencent Hunyuan 3 (hy3); 295B total / 21B active MoE, native 256K context. Text in / text out. Function calling and implicit prompt caching supported.
Xiaomi MiMo-V2.6-Flash, the low-cost high-throughput sibling of MiMo-V2.6-Pro. Text + image in / text out, 1M context. Thinking is on by default and can be disabled. Function calling, JSON mode, and implicit prompt caching supported.
ERNIE 5.1Baidu ERNIE 5.1 for complex reasoning, coding and agentic work; text in, text out, 243K-token context, function calling supported. Input-length tiered pricing: rates rise above 32K input tokens.
Qwen3-Omni-FlashAlibaba Qwen3-Omni-Flash, an any-modality-in / text-out model accepting text, image, audio and video input. 48K-token context and function calling.
Qwen3.6-35B-A3BAlibaba Qwen3.6-35B-A3B, a 35B-total / 3B-active MoE model of the Qwen3.6 series. Text in / text out, 256K-token context, hybrid thinking/non-thinking modes, function calling, and structured output.
DeepSeek-V4-Flash-0731Lightweight MoE (284B total / 13B active) tuned for fast, low-cost, high-throughput text work; function calling, web search, and thinking on by default; 1M context, up to 384K output.
Qwen3.6-PlusAlibaba Qwen3.6-Plus with hybrid linear attention and sparse MoE; 1M context, 119 languages.
Qwen3.7-PlusAlibaba multimodal model with vision and video understanding; native function calling, 1M context.
Qwen3.5-Omni-FlashAlibaba Qwen3.5-Omni-Flash, the fast any-modality-in / text-out model of the Qwen3.5-Omni series, accepting text, image, audio and video input. 192K-token context and function calling.
MiniMax-M2.7MiniMax-M2.7. Text in / text out, 200K-token context, thinking always on, function calling, and implicit prompt caching.
MiniMax-M2.5MiniMax-M2.5. Text in / text out, 192K-token context, thinking always on, function calling, and implicit prompt caching.
Qwen-MaxAlibaba Qwen-Max, the top capability tier of the original Qwen line. Text in / text out, 32K-token context, non-thinking only, function calling, built-in tools, and structured output.
Zhipu AI GLM-5.3-FlashX, the higher-throughput tier of GLM-5.3-Flash for coding and agentic tasks. Text + image in / text out, 1M context, up to 128K output. Deep thinking is always on and cannot be disabled. Function calling, JSON mode, and implicit prompt caching supported.
DeepSeek-V4.1-FlashDeepSeek-V4.1-Flash for fast, low-cost general chat, coding, and agentic work; function calling, structured output, and web search; thinking on by default; 1M context, up to 384K output.
Qwen3.6-27BAlibaba Qwen3.6-27B, the dense 27B model of the Qwen3.6 series. Text in / text out, 256K-token context, hybrid thinking/non-thinking modes, function calling, and structured output.
ERNIE 4.5 Turbo VLThe vision-language build of Baidu ERNIE 4.5 Turbo, for image understanding, visual question answering and document parsing; text and image input, text output, 128K-token context. Image tokens are billed through prompt_tokens at a flat rate per image regardless of resolution.
ERNIE 4.5 Turbo VL 32KThe 32K-token context tier of Baidu ERNIE 4.5 Turbo VL, priced the same per token as the 128K tier; text and image input, text output.
Qwen3.8-27BAlibaba Qwen3.8-27B, the compact dense model of the Qwen3.8 series. Text in / text out, native 1M-token context, hybrid thinking/non-thinking modes, function calling, structured output, and implicit prompt caching.
Xiaomi MiMo-V2.6-Pro, a multimodal reasoning model. Text + image in / text out, 1M context. Thinking is on by default and can be disabled. Function calling, JSON mode, and implicit prompt caching supported. No hidden system prompt is billed.
Xiaomi MiMo-v2.5-Pro. Text in / text out, 1M-token context, thinking on by default and can be disabled, function calling, and implicit prompt caching. A hidden system prompt of roughly 250 tokens is billed as input, so even a one-word request reports about 256 prompt tokens.
DeepSeek-V4-Flash-Vision (experimental)DeepSeek-V4-Flash-Vision, the experimental vision-enabled build of DeepSeek-V4-Flash. Image and text in, text out, 1M-token context, deep thinking, function calling, and implicit prompt caching.
Moonshot AI Kimi-K2.5. Text in / text out, 256K-token context, hybrid thinking/non-thinking modes, function calling, and implicit prompt caching.
Zhipu AI GLM-5, purpose-built for coding and agent workflows; 744B foundation, 200K context.
DeepSeek-V4-Pro-0813DeepSeek-V4-Pro for agentic coding, multi-step workflows, and complex reasoning; 1M context, up to 384K output.
Zhipu AI GLM-5.2, open-source, purpose-built for long-horizon tasks; 1M lossless context. Strong coding and engineering: autonomous task decomposition, architecture design, full-stack development, integration testing, and multi-platform deployment.
Qwen3.5-Omni-PlusAlibaba Qwen3.5-Omni-Plus, the high-capability any-modality-in / text-out model of the Qwen3.5-Omni series, accepting text, image, audio and video input. 192K-token context and function calling.
ERNIE 5.0Baidu ERNIE 5.0, a multimodal model for reasoning over text and images; text and image input, text output, 243K-token context, function calling supported. Input-length tiered pricing: rates rise above 32K input tokens.
ERNIE 5.0 Thinking (preview)The extended-reasoning variant of Baidu ERNIE 5.0, which returns its chain of thought alongside the answer; text and image input, text output, 243K-token context, function calling supported. Preview: behaviour may change without a new model id.
Moonshot AI Kimi-K2.6. Text in / text out, 256K-token context, hybrid thinking/non-thinking modes, and function calling.
Moonshot AI coding model for agentic coding and tool use; multimodal input (text, image, video), thinking always on. 256K context.
Zhipu AI GLM-5-Turbo, the low-latency turbo variant of GLM-5. Text in / text out, 195K-token context, hybrid reasoning with a deep-thinking mode, function calling, structured output, and implicit prompt caching.
Hunyuan-4-previewTencent Hunyuan Hy4 preview; 770B total / 49B active MoE tuned for agent, coding and production workflows — stronger task decomposition, long-horizon tool use and long-chain execution than Hunyuan 3. Text in / text out, 1M context, up to 64K output. Deep thinking is on by default and can be disabled; function calling, JSON mode, and implicit prompt caching supported.
StepFun Step 5 Preview, a 600B sparse-MoE (27B active) model built for agentic coding, software engineering and finance tasks. Text + image in / text out, 1M context. Thinking is always on and cannot be disabled; the trace is returned in reasoning_content and its tokens are billed inside completion_tokens, so max_tokens must cover thinking plus the answer. Function calling, JSON mode and implicit prompt caching supported.
Zhipu AI GLM-5.1, purpose-built for long-horizon tasks; 744B foundation, 200K context.
Qwen3.7-MaxAlibaba Qwen3.7-Max with native function calling and web search; 1M context.
Qwen3.8-2.4T-A95BAlibaba Qwen3.8-2.4T-A95B, the 2.4T-parameter / 95B-active MoE member of the Qwen3.8 series. Text in / text out, native 1M-token context, thinking on by default, function calling, structured output, and implicit prompt caching.
Zhipu AI GLM-5V-Turbo, the vision-language turbo variant of GLM-5. Image and text in, text out, 200K-token context, deep-thinking mode, function calling, structured output, and implicit prompt caching.
Zhipu AI GLM-5.2-Fast-Preview, the low-latency preview build of GLM-5.2. Text in / text out, 1M-token context, hybrid thinking/non-thinking modes, function calling, structured output, and implicit prompt caching.
Qwen3.8-MaxAlibaba Qwen3.8-Max, a 2.4T-parameter MoE model for coding and agentic work; native visual understanding of documents and video, function calling and web search, 1M context.
Moonshot AI Kimi-K2.7-Code-HighSpeed, the high-throughput variant of Kimi-K2.7-Code. Image and text in, text out, 256K-token context, deep thinking, function calling, and implicit prompt caching.
Moonshot AI Kimi K3; text and image input, text output. 1M context, deep thinking on by default and can be disabled. Function calling and implicit prompt caching supported.
ByteDance Seedance 2.0ByteDance Seedance 2.0 text-to-video and image-to-video (single first-frame reference via input_reference) — the two Seedance capabilities that map onto a real OpenAI Video API field, the same scope as this catalog's Seedance 2.5 entry (ByteDance's own model also supports first+last-frame control and multimodal reference generation, but OpenAI's Video API has no field to express either one, so this integration exposes no client-facing input for them — same reasoning as 2.5). Duration 4-15s (default 5) — a narrower ceiling than 2.5's 30s; seconds is required on this endpoint and is clamped into that range rather than rejected. Resolution 480p/720p/1080p/4k — the one axis where 2.0 serves MORE than 2.5: 4k is a real, forwarded tier here, where 2.5 instead downgrades an equivalent request to 1080p/720p. ratio 16:9/9:16/4:3/3:4/1:1/21:9/adaptive, optional synchronized audio (generate_audio, on by default), fps fixed at 24. Unlike 2.5, this model has NO output_format field — 2.0's own wire shape predates that field (added only in the later 2.5 migration) — so a client-supplied output_format is silently ignored rather than forwarded or rejected. Billed on the vendor-reported token count (usage.completion_tokens) — not a flat per-second rate. Async: POST /v1/videos, poll GET /v1/videos/{id} until completed, then GET /v1/videos/{id}/content for the MP4.
ByteDance Seedance 2.5ByteDance Seedance 2.5 text-to-video and image-to-video (single first-frame reference via input_reference) — the two Seedance capabilities that map onto a real OpenAI Video API field. ByteDance's own model also supports first+last-frame control and multimodal reference generation (multiple reference images/videos/audio composited per the prompt, including audio-only input), but OpenAI's Video API has no field to express either one, so this integration does not expose a client-facing input for them. Duration 4-30s (default 5); seconds is required on this endpoint and is clamped into that range rather than rejected (the vendor's own 'model-chosen' -1 duration is not reachable through this endpoint — seconds must be a positive number here), resolution 480p/720p/1080p (narrower than earlier Seedance versions — 4k is not served, and is downgraded rather than rejected: a 4K pixel size renders at 1080p, while the bare token '4k' is unrecognised and falls to the 720p default), ratio 16:9/9:16/4:3/3:4/1:1/21:9/adaptive, optional synchronized audio (generate_audio, on by default), fps fixed at 24, optional output_format passthrough (default mp4). Billed on the vendor-reported token count (usage.completion_tokens) — not a flat per-second rate. Async: POST /v1/videos, poll GET /v1/videos/{id} until completed, then GET /v1/videos/{id}/content for the MP4.
MiniMax-H3MiniMax-H3 (Hailuo-03) text-to-video and image-to-video, billed per generated second. `seconds` must be 4-15, and outside that range it is silently clamped rather than rejected. `size` names a resolution TIER, not output dimensions: send one of the names listed under Resolution Tiers. OpenAI pixel dimensions (1280x720) are also accepted, but they set only the aspect ratio and only for text-to-video — on image-to-video the ratio follows your reference image, so pixel dimensions have no effect there, while a tier name still selects the tier. Prompt required, up to 7000 characters. Image-to-video takes one first-frame image via multipart `input_reference`. Async: POST /v1/videos, poll GET /v1/videos/{id} until completed, then GET /v1/videos/{id}/content for the MP4.
Qwen2.5-VL-72B-InstructAlibaba Qwen2.5-VL 72B Instruct, a vision-language model for visual reasoning, OCR and document understanding; text and image input, text output, 128K-token context.
The smallest tier of the OpenAI GPT-5 line, for high-volume, latency-sensitive work; text and image input, text output, 400K-token context, function calling and implicit prompt caching.
The smallest tier of OpenAI GPT-4.1, for high-volume classification, extraction and other light tasks; text and image input, text output, 1M-token context, function calling and implicit prompt caching.
Gemini 2.5 Flash-LiteThe cheapest tier of the Google Gemini 2.5 line, for classification, extraction and other light tasks; text and image input, text output, 1M-token context, function calling supported.
The lightweight build of Z.ai GLM-4.5, with fewer active parameters for lower cost and latency; text in, text out, 128K-token context, function calling and implicit prompt caching.
Fast, cost-efficient model in the GPT-5.6 family, optimized for high-volume, cost-sensitive workloads: responsive chat, classification, extraction, lightweight coding, and agentic workflows at lower latency and cost. Text and image input, text output, 1M-token context.
The smallest, lowest-latency tier of the OpenAI GPT-5.4 line; text and image input, text output, 400K-token context, function calling and implicit prompt caching.
Gemini 3.1 Flash-LiteThe low-latency, low-cost tier of the Google Gemini 3.1 line, for high-volume tasks; text and image input, text output, 1M-token context, function calling supported.
The mid-size tier of the OpenAI GPT-5 line, for everyday tasks at lower cost than the full model; text and image input, text output, 400K-token context, function calling supported.
DeepSeek-V3671B mixture-of-experts model; strong at coding, math, and multi-step reasoning.
DeepSeek-V3.1DeepSeek-V3.1, the hybrid-thinking successor to V3; text in, text out, 160K-token context, function calling supported.
Gemini 2.5 FlashGoogle Gemini 2.5 Flash, the fast tier of the Gemini 2.5 line, for high-volume work; text and image input, text output, 1M-token context, function calling supported.
Gemini 3.5 Flash-LiteThe cost-efficient tier of the Google Gemini 3.5 line; text and image input, text output, 1M-token context, function calling and implicit prompt caching.
DeepSeek-V3.1-TerminusThe Terminus build of DeepSeek-V3.1, tuned for agentic and tool-use workloads; text in, text out, 160K-token context, function calling and implicit prompt caching.
DeepSeek-V3.2-ExpThe experimental build of DeepSeek-V3.2; text in, text out, 160K-token context, function calling supported. Experimental: behaviour may change without a new model id.
The mid-size tier of OpenAI GPT-4.1, trading some capability for lower cost and latency; text and image input, text output, 1M-token context, function calling and implicit prompt caching.
DeepSeek-V3.2DeepSeek-V3.2 with sparse attention for long-context work; text in, text out, 160K-token context, function calling supported.
DeepSeek-R1DeepSeek-R1, a reasoning model that emits a chain of thought before its answer; text in, text out, 64K-token context, function calling and implicit prompt caching.
Gemini 3 Flash (preview)Preview build of the fast tier of the Google Gemini 3 line; text and image input, text output, 1M-token context, function calling supported. Preview: behaviour may change without a new model id.
Z.ai GLM-4.5, a mixture-of-experts model for reasoning, coding and agent workflows; text in, text out, 128K-token context, function calling and implicit prompt caching.
The 2025-09-05 snapshot of Moonshot AI Kimi K2, a mixture-of-experts model tuned for agentic coding and tool use; text in, text out, 256K-token context, function calling and implicit prompt caching.
The extended-reasoning build of Moonshot AI Kimi K2, which thinks across long tool-calling chains; text in, text out, 256K-token context, function calling supported.
The mid-size tier of the OpenAI GPT-5.4 line; text and image input, text output, 400K-token context, function calling and implicit prompt caching.
Z.ai GLM-4.7 for reasoning, coding and agent workflows; text in, text out, 200K-token context, function calling and implicit prompt caching.
OpenAI GPT-5.1 for reasoning, coding and agentic workflows; text and image input, text output, 400K-token context, function calling and implicit prompt caching.
Gemini 2.5 ProThe reasoning tier of the Google Gemini 2.5 line, for coding, math and long-document analysis; text and image input, text output, 1M-token context, function calling supported.
LongCat 2.0Meituan LongCat 2.0, a long-context mixture-of-experts model; text in, text out, 1M-token context, function calling and implicit prompt caching.
Qwen3-Coder-PlusAlibaba Qwen3-Coder-Plus, tuned for repository-scale code generation and agentic coding; text in, text out, 1M-token context, function calling and implicit prompt caching.
Gemini 3.5 FlashThe fast tier of the Google Gemini 3.5 line; text and image input, text output, 1M-token context, function calling and implicit prompt caching.
Gemini 3.6 FlashThe fast tier of the Google Gemini 3.6 line; text and image input, text output, 1M-token context, function calling supported.
OpenAI GPT-5.2 for reasoning, coding and agentic workflows; text and image input, text output, 400K-token context, function calling supported.
Qwen3-MaxThe largest tier of the Alibaba Qwen3 line; text in, text out, 256K-token context, function calling and implicit prompt caching.
Anthropic Claude Sonnet 5; text and image input, text output, with a 1M-token context window. Extended thinking and tool use supported.
Qwen3.6-Max (preview)Preview build of the largest tier of the Alibaba Qwen3.6 line; text in, text out, 256K-token context, function calling supported. Preview: behaviour may change without a new model id.
OpenAI GPT-4.1 for general chat, coding and instruction following; text and image input, text output, 1M-token context, function calling and implicit prompt caching.
Gemini 3.1 Pro (preview)Preview build of the reasoning tier of the Google Gemini 3.1 line; text and image input, text output, 1M-token context, function calling supported. Preview: behaviour may change without a new model id.
Balanced model in the GPT-5.6 family, tuned for workloads that need strong reasoning, coding, and agentic capability at lower cost than the flagship tier. Text and image input, text output, 1M-token context.
The higher-capability tier of the OpenAI GPT-5.4 line; text and image input, text output, 1M-token context, function calling and implicit prompt caching.
Anthropic Claude Sonnet 4.5; text and image input, text output, 1M-token context. Extended thinking and tool use are supported. Served over the Anthropic Messages format.
Anthropic Claude Sonnet 4.6; text and image input, text output, 1M-token context. Extended thinking, tool use, and explicit cache_control prompt caching are supported. Served over the Anthropic Messages format.
Anthropic Claude Opus 4.6; text and image input, text output, 1M-token context. Extended thinking, tool use, and explicit cache_control prompt caching are supported. Served over the Anthropic Messages format.
Anthropic Claude Opus 4.7; text and image input, text output, 1M-token context. Extended thinking, tool use, and explicit cache_control prompt caching are supported. Served over the Anthropic Messages format.
GPT-5.5, designed for complex professional workloads, with strong reasoning, high reliability, and improved token efficiency on hard tasks. Text and image input, text output, 1M-token context.
Flagship of the GPT-5.6 series, built for advanced reasoning, complex coding, and agentic workflows: multi-step software engineering, long-horizon problem solving, and autonomous tool use. Text and image input, text output, 1M-token context.
Anthropic Claude Opus 4.8; text and image input, text output, with a 1M-token context window. Extended thinking and tool use supported.
Anthropic Claude Opus 5; text and image input, text output, with a 1M-token context window. Extended thinking and tool use supported.
Anthropic Claude Fable 5.1; text and image input, text output, 1M-token context. Extended thinking, tool use, and explicit cache_control prompt caching are supported. Served over the Anthropic Messages format.
OpenAI GPT-6 Astra, an agentic model built for long end-to-end tasks: reasoning, software engineering, computer use, browsing, scientific analysis, research, and professional document creation in one model. Text and image input, text output, 1.05M-token context. Sampling controls are fixed upstream; only reasoning depth is tunable.