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Tutti i modelli attualmente elencati da OpenRouter, con dati coerenti su modalità, contesto, prezzi, configurazione e provider.
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- modelli
- 631
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- ultimo aggiornamento
- 29 set 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,...
- Contesto
- 1K
- Input
- text
- Output
- 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...
- Contesto
- 1K
- Input
- text
- Output
- 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...
- Contesto
- 1K
- Input
- text
- Output
- 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...
- Contesto
- 1K
- Input
- text
- Output
- 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...
- Contesto
- 1K
- Input
- text
- Output
- 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.
- Contesto
- 1K
- Input
- text
- Output
- 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...
- Contesto
- 1K
- Input
- text
- Output
- 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.
- Contesto
- 8K
- Input
- text
- Output
- 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...
- Contesto
- 1K
- Input
- text
- Output
- 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,...
- Contesto
- 1K
- Input
- text
- Output
- 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...
- Contesto
- 400K
- Input
- image · text · file
- Output
- text
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...
- Contesto
- 400K
- Input
- image · text · file
- Output
- 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....
- Contesto
- 400K
- Input
- text · image
- Output
- text
openaiOpenAI: GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini is a smaller and faster version of GPT-5.1-Codex
- Contesto
- 400K
- Input
- image · text
- Output
- 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...
- Contesto
- 262K
- Input
- text
- Output
- text
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.
- Contesto
- 1M
- Input
- text · image
- Output
- text
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...
- Contesto
- 8K
- Input
- text
- Output
- embeddings
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...
- Contesto
- 20K
- Input
- text
- Output
- embeddings
openaiOpenAI: Text Embedding Ada 002
text-embedding-ada-002 is OpenAI's legacy text embedding model.
- Contesto
- 8K
- Input
- text
- Output
- embeddings
openaiOpenAI: Text Embedding Ada 002 (batch)
text-embedding-ada-002 is OpenAI's legacy text embedding model.
- Contesto
- 8K
- Input
- text
- Output
- embeddings
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.
- Contesto
- 8K
- Input
- text
- Output
- embeddings
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...
- Contesto
- 8K
- Input
- text
- Output
- embeddings
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...
- Contesto
- 8K
- Input
- text
- Output
- embeddings
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...
- Contesto
- 8K
- Input
- text
- Output
- embeddings