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| 3 models | BAAIbge-m3Remove × | intfloatIntfloat: E5-Base-v2Remove × | QwenQwen3-Embedding-8BRemove × |
|---|---|---|---|
| What’s different | BGE-M3 is a versatile text embedding model that supports multi-functionality, multi-linguality, and multi-granularity, allowing it to perform dense retrieval, multi-vector retrieval, and sparse retrieval in… | 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… | 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 | May 27, 2024Listed | Nov 22, 2023Listed | Jun 10, 2025Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 1 endpoint → | 1 endpoint → | 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 | 8.2K | 512 | 41K |
| Max output | 7.4K | 460 | 32K |
| Parameters | — | — | 8B |
| Open weights | Supported | Supported | Supported |