RADAR MODELI AI
Porównuj modele AI według tych samych kryteriów
Wszystkie modele obecnie dostępne w OpenRouter wraz ze spójnymi danymi o modalnościach, kontekście, cenach, konfiguracji i dostawcach.
- ZAKRES KATALOGU
- WSZYSTKIE PUBLICZNE
- modele
- 691
- dostawców
- 98
- ostatnia synchronizacja
- 30 paź 2026
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.
- Kontekst
- 1K
- Wejście
- text
- Wyjście
- 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...
- Kontekst
- 1K
- Wejście
- text
- Wyjście
- 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.
- Kontekst
- 8K
- Wejście
- text
- Wyjście
- 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...
- Kontekst
- 1K
- Wejście
- text
- Wyjście
- 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,...
- Kontekst
- 1K
- Wejście
- text
- Wyjście
- 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...
- Kontekst
- 400K
- Wejście
- image · text · file
- Wyjście
- 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...
- Kontekst
- 400K
- Wejście
- image · text · file
- Wyjście
- 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....
- Kontekst
- 400K
- Wejście
- text · image
- Wyjście
- text
openaiOpenAI: GPT-5.1-Codex-Mini
GPT-5.1-Codex-Mini is a smaller and faster version of GPT-5.1-Codex
- Kontekst
- 400K
- Wejście
- image · text
- Wyjście
- 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...
- Kontekst
- 262K
- Wejście
- text
- Wyjście
- 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.
- Kontekst
- 1M
- Wejście
- text · image
- Wyjście
- 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...
- Kontekst
- 8K
- Wejście
- text
- Wyjście
- 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...
- Kontekst
- 20K
- Wejście
- text
- Wyjście
- embeddings
openaiOpenAI: Text Embedding Ada 002
text-embedding-ada-002 is OpenAI's legacy text embedding model.
- Kontekst
- 8K
- Wejście
- text
- Wyjście
- embeddings
openaiOpenAI: Text Embedding Ada 002 (batch)
text-embedding-ada-002 is OpenAI's legacy text embedding model.
- Kontekst
- 8K
- Wejście
- text
- Wyjście
- 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.
- Kontekst
- 8K
- Wejście
- text
- Wyjście
- 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...
- Kontekst
- 8K
- Wejście
- text
- Wyjście
- 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...
- Kontekst
- 8K
- Wejście
- text
- Wyjście
- 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...
- Kontekst
- 8K
- Wejście
- text
- Wyjście
- embeddings
openaiOpenAI: Text Embedding 3 Small (batch)
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...
- Kontekst
- 8K
- Wejście
- text
- Wyjście
- embeddings
perplexityPerplexity: Sonar Pro Search
Exclusively available on the OpenRouter API, Sonar Pro's new Pro Search mode is Perplexity's most advanced agentic search system. It is designed for deeper reasoning and analysis. Pricing is based...
- Kontekst
- 200K
- Wejście
- text · image
- Wyjście
- text
mistralaiMistral: Voxtral Small 24B 2507
Voxtral Small is an enhancement of Mistral Small 3, incorporating state-of-the-art audio input capabilities while retaining best-in-class text performance. It excels at speech transcription, translation and audio understanding. Input audio...
- Kontekst
- 33K
- Wejście
- text · audio · file
- Wyjście
- text
openaiOpenAI: gpt-oss-safeguard-20b
gpt-oss-safeguard-20b is a safety reasoning model from OpenAI built upon gpt-oss-20b. This open-weight, 21B-parameter Mixture-of-Experts (MoE) model offers lower latency for safety tasks like content classification, LLM filtering, and trust...
- Kontekst
- 131K
- Wejście
- text
- Wyjście
- text
qwenQwen: Qwen3 Embedding 8B
The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. This series inherits the exceptional multilingual capabilities, long-text...
- Kontekst
- 41K
- Wejście
- text
- Wyjście
- embeddings