MODEL RADAR · OPENROUTER
Comparer les modèles d’IA sur une même base.
Tous les modèles actuellement référencés par OpenRouter, avec des informations cohérentes sur les modalités, le contexte, les tarifs, la configuration et les fournisseurs.
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- 29 sept. 2026
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,...
- Contexte
- 1K
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- text
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- 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...
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- 1K
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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...
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- 1K
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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...
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- 1K
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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...
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- 1K
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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.
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- 1K
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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...
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- 1K
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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.
- Contexte
- 8K
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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...
- Contexte
- 1K
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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,...
- Contexte
- 1K
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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...
- Contexte
- 400K
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- image · text · file
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openaiOpenAI: GPT-5.1 (batch)
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...
- Contexte
- 400K
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- image · text · file
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- 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....
- Contexte
- 400K
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- text · image
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- text
openaiOpenAI: GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini is a smaller and faster version of GPT-5.1-Codex
- Contexte
- 400K
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- image · text
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- text
moonshotaiMoonshotAI: Kimi K2 Thinking
Kimi K2 Thinking is Moonshot AI’s most advanced open reasoning model to date, extending the K2 series into agentic, long-horizon reasoning. Built on the trillion-parameter Mixture-of-Experts (MoE) architecture introduced in...
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amazonAmazon: Nova Premier 1.0
Amazon Nova Premier is the most capable of Amazon’s multimodal models for complex reasoning tasks and for use as the best teacher for distilling custom models.
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- 1M
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mistralaiMistral: Mistral Embed 2312
Mistral Embed is a specialized embedding model for text data, optimized for semantic search and RAG applications. Developed by Mistral AI in late 2023, it produces 1024-dimensional vectors that effectively...
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- 8K
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googleGoogle: Gemini Embedding 001
gemini-embedding-001 provides a unified cutting edge experience across domains, including science, legal, finance, and coding. This embedding model has consistently held a top spot on the Massive Text Embedding Benchmark...
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- 20K
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openaiOpenAI: Text Embedding Ada 002
text-embedding-ada-002 is OpenAI's legacy text embedding model.
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- 8K
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openaiOpenAI: Text Embedding Ada 002 (batch)
text-embedding-ada-002 is OpenAI's legacy text embedding model.
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- 8K
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mistralaiMistral: Codestral Embed 2505
Mistral Codestral Embed is specially designed for code, perfect for embedding code databases, repositories, and powering coding assistants with state-of-the-art retrieval.
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- 8K
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openaiOpenAI: Text Embedding 3 Large
text-embedding-3-large is OpenAI's most capable embedding model for both english and non-english tasks. Embeddings are a numerical representation of text that can be used to measure the relatedness between two...
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- 8K
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openaiOpenAI: Text Embedding 3 Large (batch)
text-embedding-3-large is OpenAI's most capable embedding model for both english and non-english tasks. Embeddings are a numerical representation of text that can be used to measure the relatedness between two...
- Contexte
- 8K
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- text
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openaiOpenAI: Text Embedding 3 Small
text-embedding-3-small is OpenAI's improved, more performant version of the ada embedding model. Embeddings are a numerical representation of text that can be used to measure the relatedness between two pieces...
- Contexte
- 8K
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- text
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- embeddings