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| 3 models | intfloatIntfloat: E5-Large-v2Remove × | intfloatmultilingual-e5-large-instructRemove × | QwenQwen3-Embedding-8BRemove × |
|---|---|---|---|
| What’s different | 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… | The Multilingual-E5 models, initialized from XLM-RoBERTa, support up to 512 tokens per input — any longer text will be silently truncated. To ensure optimal performance, always prefix inputs with “query:”… | The Qwen3 Embedding model series is the latest proprietary model of the Qwen family, specifically designed for text embedding and ranking tasks. Building upon the dense foundational models of the Qwen3… |
| Date | Nov 22, 2023Listed | Apr 17, 2025Listed | Jun 10, 2025Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | See provider quotes | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 1 endpoint → | 0 endpoints → | 1 endpoint → |
Benchmarks Each result retains its evaluation configuration | |||
| FreshStack · MTEB 1.38.43 ↗MTEB 1.38.43; test/default; dataset 7a20df1a | Not reported | Not reported | 0.43017 ↗Revision 4e423935; test/defaultContributor reported |
Specifications | |||
| Context window | 512 | 512 | 41K |
| Max output | 460 | — | 32K |
| Parameters | — | — | 8B |
| Open weights | Supported | Not reported | Supported |