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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- 31 août 2026
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.
- Contexte
- 131K
- Entrée
- text · image
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- 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.
- Contexte
- 262K
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- text · image · file
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- 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.
- Contexte
- 262K
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- text · image · file
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- 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...
- Contexte
- 164K
- Entrée
- text
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- 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...
- Contexte
- 67K
- Entrée
- text · image
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- 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....
- Contexte
- 47K
- Entrée
- text · image
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- 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...
- Contexte
- 200K
- Entrée
- file · image · text
- Sortie
- 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...
- Contexte
- 200K
- Entrée
- file · image · text
- Sortie
- 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...
- Contexte
- 66K
- Entrée
- image · text
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- 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.
- Contexte
- 1K
- Entrée
- text
- Sortie
- 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...
- Contexte
- 1K
- Entrée
- text
- Sortie
- 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.
- Contexte
- 1K
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- text
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- 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,...
- Contexte
- 1K
- Entrée
- text
- Sortie
- 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...
- Contexte
- 1K
- Entrée
- text
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- 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...
- Contexte
- 1K
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- text
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- 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...
- Contexte
- 1K
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- text
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- 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...
- Contexte
- 1K
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- text
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- 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.
- Contexte
- 1K
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- text
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- 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...
- Contexte
- 1K
- Entrée
- text
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- 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.
- Contexte
- 8K
- Entrée
- text
- Sortie
- 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...
- Contexte
- 1K
- Entrée
- text
- Sortie
- 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,...
- Contexte
- 1K
- Entrée
- text
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- 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...
- Contexte
- 400K
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- image · text · file
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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
- Entrée
- text · image
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- text