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| 3 models | googleembeddinggemma-2Remove × | BAAIbge-m3Remove × | QwenQwen3-Embedding-8BRemove × |
|---|---|---|---|
| What’s different | EmbeddingGemma 2 is a 740M parameter multimodal embedding model from Google DeepMind that maps text (incl. code), images, video and audio, alone or interleaved, into a single 768-dimensional vector space,… | 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 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 | Oct 8, 2026Listed | May 27, 2024Listed | Jun 10, 2025Listed |
API prices USD · provider configuration shown below | |||
| Listed rate | See provider quotes | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ | $0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗ |
| Provider endpoints | 0 endpoints → | 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 | 8.2K | 41K |
| Max output | — | 7.4K | 32K |
| Parameters | — | — | 8B |
| Open weights | Not reported | Supported | Supported |
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