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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
- 534
- プロバイダー
- 75
- 最終同期
- 2026/08/31
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
intfloatIntfloat: E5-Base-v2
The e5-base-v2 embedding model encodes English sentences and paragraphs into a 768-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, similarity scoring,...
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings
intfloatIntfloat: Multilingual-E5-Large
The multilingual-e5-large embedding model encodes sentences, paragraphs, and documents across over 90 languages into a 1024-dimensional dense vector space, delivering robust semantic embeddings optimized for multilingual retrieval, cross-language similarity, and...
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings
sentence-transformersSentence Transformers: paraphrase-MiniLM-L6-v2
The paraphrase-MiniLM-L6-v2 embedding model converts sentences and short paragraphs into a 384-dimensional dense vector space, producing high-quality semantic embeddings optimized for paraphrase detection, semantic similarity scoring, clustering, and lightweight retrieval...
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings
sentence-transformersSentence Transformers: all-MiniLM-L12-v2
The all-MiniLM-L12-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, producing efficient and high-quality semantic embeddings optimized for tasks such as semantic search, clustering, and...
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings
baaiBAAI: bge-base-en-v1.5
The bge-base-en-v1.5 embedding model converts English sentences and paragraphs into 768-dimensional dense vectors, delivering efficient, high-quality semantic embeddings optimized for retrieval, semantic search, and document-matching workflows. This version (v1.5) features...
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings
sentence-transformersSentence Transformers: multi-qa-mpnet-base-dot-v1
The multi-qa-mpnet-base-dot-v1 embedding model transforms sentences and short paragraphs into a 768-dimensional dense vector space, generating high-quality semantic embeddings optimized for question-and-answer retrieval, semantic search, and similarity-scoring across diverse content.
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings
baaiBAAI: bge-large-en-v1.5
The bge-large-en-v1.5 embedding model maps English sentences, paragraphs, and documents into a 1024-dimensional dense vector space, delivering high-fidelity semantic embeddings optimized for semantic search, document retrieval, and downstream NLP tasks...
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings
baaiBAAI: bge-m3
The bge-m3 embedding model encodes sentences, paragraphs, and long documents into a 1024-dimensional dense vector space, delivering high-quality semantic embeddings optimized for multilingual retrieval, semantic search, and large-context applications.
- コンテキスト
- 8K
- 入力
- text
- 出力
- embeddings
sentence-transformersSentence Transformers: all-mpnet-base-v2
The all-mpnet-base-v2 embedding model encodes sentences and short paragraphs into a 768-dimensional dense vector space, providing high-fidelity semantic embeddings well suited for tasks like information retrieval, clustering, similarity scoring, and...
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings
sentence-transformersSentence Transformers: all-MiniLM-L6-v2
The all-MiniLM-L6-v2 embedding model maps sentences and short paragraphs into a 384-dimensional dense vector space, enabling high-quality semantic representations that are ideal for downstream tasks such as information retrieval, clustering,...
- コンテキスト
- 1K
- 入力
- text
- 出力
- embeddings
openaiOpenAI: GPT-5.1
GPT-5.1 is the latest frontier-grade model in the GPT-5 series, offering stronger general-purpose reasoning, improved instruction adherence, and a more natural conversational style compared to GPT-5. It uses adaptive reasoning...
- コンテキスト
- 400K
- 入力
- image · text · file
- 出力
- text
openaiOpenAI: GPT-5.1-Codex
GPT-5.1-Codex is a specialized version of GPT-5.1 optimized for software engineering and coding workflows. It is designed for both interactive development sessions and long, independent execution of complex engineering tasks....
- コンテキスト
- 400K
- 入力
- text · image
- 出力
- text