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- カタログ範囲
- 公開モデルすべて
- モデル
- 631
- プロバイダー
- 86
- 最終同期
- 2026/09/29
openaiOpenAI: gpt-oss-120b
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized...
- コンテキスト
- 131K
- 入力
- text
- 出力
- text
openaiOpenAI: gpt-oss-120b (batch)
gpt-oss-120b is an open-weight, 117B-parameter Mixture-of-Experts (MoE) language model from OpenAI designed for high-reasoning, agentic, and general-purpose production use cases. It activates 5.1B parameters per forward pass and is optimized...
- コンテキスト
- 131K
- 入力
- text
- 出力
- text
openaiOpenAI: gpt-oss-20b
gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for...
- コンテキスト
- 131K
- 入力
- text
- 出力
- text
openaiOpenAI: gpt-oss-20b (batch)
gpt-oss-20b is an open-weight 21B parameter model released by OpenAI under the Apache 2.0 license. It uses a Mixture-of-Experts (MoE) architecture with 3.6B active parameters per forward pass, optimized for...
- コンテキスト
- 131K
- 入力
- text
- 出力
- text
anthropicAnthropic: Claude Opus 4.1
Claude Opus 4.1 is an updated version of Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains...
- コンテキスト
- 200K
- 入力
- image · text · file
- 出力
- text
anthropicAnthropic: Claude Opus 4.1 (batch)
Claude Opus 4.1 is an updated version of Anthropic’s flagship model, offering improved performance in coding, reasoning, and agentic tasks. It achieves 74.5% on SWE-bench Verified and shows notable gains...
- コンテキスト
- 200K
- 入力
- image · text · file
- 出力
- text
mistralaiMistral: Codestral 2508
Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. Blog Post
- コンテキスト
- 256K
- 入力
- text · file
- 出力
- text
mistralaiMistral: Codestral 2508 (batch)
Mistral's cutting-edge language model for coding released end of July 2025. Codestral specializes in low-latency, high-frequency tasks such as fill-in-the-middle (FIM), code correction and test generation. Blog Post
- コンテキスト
- 256K
- 入力
- text · file
- 出力
- text
qwenQwen: Qwen3 Coder 30B A3B Instruct
Qwen3-Coder-30B-A3B-Instruct is a 30.5B parameter Mixture-of-Experts (MoE) model with 128 experts (8 active per forward pass), designed for advanced code generation, repository-scale understanding, and agentic tool use. Built on the...
- コンテキスト
- 262K
- 入力
- text
- 出力
- text
qwenQwen: Qwen3 30B A3B Instruct 2507
Qwen3-30B-A3B-Instruct-2507 is a 30.5B-parameter mixture-of-experts language model from Qwen, with 3.3B active parameters per inference. It operates in non-thinking mode and is designed for high-quality instruction following, multilingual understanding, and...
- コンテキスト
- 262K
- 入力
- text
- 出力
- text
z-aiZ.ai: GLM 4.5
GLM-4.5 is our latest flagship foundation model, purpose-built for agent-based applications. It leverages a Mixture-of-Experts (MoE) architecture and supports a context length of up to 128k tokens. GLM-4.5 delivers significantly...
- コンテキスト
- 131K
- 入力
- text
- 出力
- text
z-aiZ.ai: GLM 4.5 Air
GLM-4.5-Air is the lightweight variant of our latest flagship model family, also purpose-built for agent-centric applications. Like GLM-4.5, it adopts the Mixture-of-Experts (MoE) architecture but with a more compact parameter...
- コンテキスト
- 131K
- 入力
- text
- 出力
- text
qwenQwen: Qwen3 235B A22B Thinking 2507
Qwen3-235B-A22B-Thinking-2507 is a high-performance, open-weight Mixture-of-Experts (MoE) language model optimized for complex reasoning tasks. It activates 22B of its 235B parameters per forward pass and natively supports up to 262,144...
- コンテキスト
- 131K
- 入力
- text
- 出力
- text
qwenQwen: Qwen3 Coder 480B A35B
Qwen3-Coder-480B-A35B-Instruct is a Mixture-of-Experts (MoE) code generation model developed by the Qwen team. It is optimized for agentic coding tasks such as function calling, tool use, and long-context reasoning over...
- コンテキスト
- 262K
- 入力
- text
- 出力
- text
bytedanceByteDance: UI-TARS 7B
UI-TARS-1.5 is a multimodal vision-language agent optimized for GUI-based environments, including desktop interfaces, web browsers, mobile systems, and games. Built by ByteDance, it builds upon the UI-TARS framework with reinforcement...
- コンテキスト
- 128K
- 入力
- image · text
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- text
googleGoogle: Gemini 2.5 Flash Lite
Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance...
- コンテキスト
- 1.0M
- 入力
- text · image · file · audio · video
- 出力
- text
googleGoogle: Gemini 2.5 Flash Lite (batch)
Gemini 2.5 Flash-Lite is a lightweight reasoning model in the Gemini 2.5 family, optimized for ultra-low latency and cost efficiency. It offers improved throughput, faster token generation, and better performance...
- コンテキスト
- 1.0M
- 入力
- text · image · file · audio · video
- 出力
- text
qwenQwen: Qwen3 235B A22B Instruct 2507
Qwen3-235B-A22B-Instruct-2507 is a multilingual, instruction-tuned mixture-of-experts language model based on the Qwen3-235B architecture, with 22B active parameters per forward pass. It is optimized for general-purpose text generation, including instruction following,...
- コンテキスト
- 262K
- 入力
- text
- 出力
- text
moonshotaiMoonshotAI: Kimi K2 0711
Kimi K2 Instruct is a large-scale Mixture-of-Experts (MoE) language model developed by Moonshot AI, featuring 1 trillion total parameters with 32 billion active per forward pass. It is optimized for...
- コンテキスト
- 131K
- 入力
- text
- 出力
- text
cognitivecomputationsVenice: Uncensored
Venice Uncensored Dolphin Mistral 24B Venice Edition is a fine-tuned variant of Mistral-Small-24B-Instruct-2501, developed by dphn.ai in collaboration with Venice.ai. This model is designed as an “uncensored” instruct-tuned LLM, preserving...
- コンテキスト
- 128K
- 入力
- text
- 出力
- text
tencentTencent: Hunyuan A13B Instruct
Hunyuan-A13B is a 13B active parameter Mixture-of-Experts (MoE) language model developed by Tencent, with a total parameter count of 80B and support for reasoning via Chain-of-Thought. It offers competitive benchmark...
- コンテキスト
- 131K
- 入力
- text
- 出力
- text
morphMorph: Morph V3 Large
Morph's high-accuracy apply model for complex code edits. 4,500 tokens/sec with 98% accuracy for precise code transformations. The model requires the prompt to be in the following format: <instruction>{instruction}</instruction> <code>{initialcode}</code>...
- コンテキスト
- 262K
- 入力
- text
- 出力
- text
morphMorph: Morph V3 Fast
Morph's fastest apply model for code edits. 10,500 tokens/sec with 96% accuracy for rapid code transformations. The model requires the prompt to be in the following format: <instruction>{instruction}</instruction> <code>{initialcode}</code> <update>{editsnippet}</update>...
- コンテキスト
- 82K
- 入力
- text
- 出力
- text
baiduBaidu: ERNIE 4.5 VL 424B A47B
ERNIE-4.5-VL-424B-A47B is a multimodal Mixture-of-Experts (MoE) model from Baidu’s ERNIE 4.5 series, featuring 424B total parameters with 47B active per token. It is trained jointly on text and image data...
- コンテキスト
- 123K
- 入力
- image · text
- 出力
- text