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AIモデルを、同じ基準で比較。
OpenRouterで現在公開されているすべてのモデルを検索し、モダリティ、コンテキスト、料金、設定、プロバイダー情報を比較できます。
- 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-5.2 Pro (batch)
GPT-5.2 Pro is OpenAI’s most advanced model, offering major improvements in agentic coding and long context performance over GPT-5 Pro. It is optimized for complex tasks that require step-by-step reasoning,...
- コンテキスト
- 400K
- 入力
- image · text · file
- 出力
- text
openaiOpenAI: GPT-5.2
GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly...
- コンテキスト
- 400K
- 入力
- file · image · text
- 出力
- text
openaiOpenAI: GPT-5.2 (batch)
GPT-5.2 is the latest frontier-grade model in the GPT-5 series, offering stronger agentic and long context perfomance compared to GPT-5.1. It uses adaptive reasoning to allocate computation dynamically, responding quickly...
- コンテキスト
- 400K
- 入力
- file · image · text
- 出力
- text
mistralaiMistral: Devstral 2 2512
Devstral 2 is a state-of-the-art open-source model by Mistral AI specializing in agentic coding. It is a 123B-parameter dense transformer model supporting a 256K context window. Devstral 2 supports exploring...
- コンテキスト
- 262K
- 入力
- text · file
- 出力
- text
relaceRelace: Relace Search
The relace-search model uses 4-12 viewfile and grep tools in parallel to explore a codebase and return relevant files to the user request. In contrast to RAG, relace-search performs agentic...
- コンテキスト
- 256K
- 入力
- text
- 出力
- text
z-aiZ.ai: GLM 4.6V
GLM-4.6V is a large multimodal model designed for high-fidelity visual understanding and long-context reasoning across images, documents, and mixed media. It supports up to 128K tokens, processes complex page layouts...
- コンテキスト
- 131K
- 入力
- image · text · video
- 出力
- text
openrouterBody Builder (beta)
Transform your natural language requests into structured OpenRouter API request objects. Describe what you want to accomplish with AI models, and Body Builder will construct the appropriate API calls. Example:...
- コンテキスト
- 128K
- 入力
- text
- 出力
- text
openaiOpenAI: GPT-5.1-Codex-Max
GPT-5.1-Codex-Max is OpenAI’s latest agentic coding model, designed for long-running, high-context software development tasks. It is based on an updated version of the 5.1 reasoning stack and trained on agentic...
- コンテキスト
- 400K
- 入力
- text · image
- 出力
- text
amazonAmazon: Nova 2 Lite
Nova 2 Lite is a fast, cost-effective reasoning model for everyday workloads that can process text, images, and videos to generate text. Nova 2 Lite demonstrates standout capabilities in processing...
- コンテキスト
- 1M
- 入力
- text · image · video · file
- 出力
- text
mistralaiMistral: Ministral 3 14B 2512
The largest model in the Ministral 3 family, Ministral 3 14B offers frontier capabilities and performance comparable to its larger Mistral Small 3.2 24B counterpart. A powerful and efficient language...
- コンテキスト
- 262K
- 入力
- text · image
- 出力
- text
mistralaiMistral: Ministral 3 8B 2512
A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
- コンテキスト
- 262K
- 入力
- text · image
- 出力
- text
mistralaiMistral: Ministral 3 8B 2512 (batch)
A balanced model in the Ministral 3 family, Ministral 3 8B is a powerful, efficient tiny language model with vision capabilities.
- コンテキスト
- 262K
- 入力
- text · image
- 出力
- text
mistralaiMistral: Ministral 3 3B 2512
The smallest model in the Ministral 3 family, Ministral 3 3B is a powerful, efficient tiny language model with vision capabilities.
- コンテキスト
- 131K
- 入力
- text · image
- 出力
- text
mistralaiMistral: Mistral Large 3 2512
Mistral Large 3 2512 is Mistral’s most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.
- コンテキスト
- 262K
- 入力
- text · image · file
- 出力
- text
mistralaiMistral: Mistral Large 3 2512 (batch)
Mistral Large 3 2512 is Mistral’s most capable model to date, featuring a sparse mixture-of-experts architecture with 41B active parameters (675B total), and released under the Apache 2.0 license.
- コンテキスト
- 262K
- 入力
- text · image · file
- 出力
- text
deepseekDeepSeek: DeepSeek V3.2
DeepSeek-V3.2 is a large language model designed to harmonize high computational efficiency with strong reasoning and agentic tool-use performance. It introduces DeepSeek Sparse Attention (DSA), a fine-grained sparse attention mechanism...
- コンテキスト
- 164K
- 入力
- text
- 出力
- text
black-forest-labsBlack Forest Labs: FLUX.2 Flex
FLUX.2 [flex] excels at rendering complex text, typography, and fine details, and supports multi-reference editing in the same unified architecture. Pricing is as follows, per the docs: We charge $0.06...
- コンテキスト
- 67K
- 入力
- text · image
- 出力
- image
black-forest-labsBlack Forest Labs: FLUX.2 Pro
A high-end image generation and editing model focused on frontier-level visual quality and reliability. It delivers strong prompt adherence, stable lighting, sharp textures, and consistent character/style reproduction across multi-reference inputs....
- コンテキスト
- 47K
- 入力
- text · image
- 出力
- image
anthropicAnthropic: Claude Opus 4.5
Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...
- コンテキスト
- 200K
- 入力
- file · image · text
- 出力
- text
anthropicAnthropic: Claude Opus 4.5 (batch)
Claude Opus 4.5 is Anthropic’s frontier reasoning model optimized for complex software engineering, agentic workflows, and long-horizon computer use. It offers strong multimodal capabilities, competitive performance across real-world coding and...
- コンテキスト
- 200K
- 入力
- file · image · text
- 出力
- text
googleGoogle: Nano Banana Pro (Gemini 3 Pro Image Preview)
Nano Banana Pro is Google’s most advanced image-generation and editing model, built on Gemini 3 Pro. It extends the original Nano Banana with significantly improved multimodal reasoning, real-world grounding, and...
- コンテキスト
- 66K
- 入力
- image · text
- 出力
- image · text
thenlperThenlper: GTE-Base
The gte-base embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, delivering efficient and effective semantic embeddings optimized for textual similarity, semantic search, and clustering applications.
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings
thenlperThenlper: GTE-Large
The gte-large embedding model converts English sentences, paragraphs and moderate-length documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for information retrieval, semantic textual similarity, reranking and...
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings
intfloatIntfloat: E5-Large-v2
The e5-large-v2 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-accuracy semantic embeddings optimized for retrieval, semantic search, reranking, and similarity-scoring tasks.
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings