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Comparison of Intfloat: E5-Base-v2, text-embedding-ada-002, Qwen3-Embedding-8B
3 modelsintfloatIntfloat: E5-Base-v2Remove ×Unknowntext-embedding-ada-002Remove ×QwenQwen3-Embedding-8BRemove ×
What’s differentThe 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…8.2K contextThe 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…
DateNov 22, 2023ListedOct 16, 2026DiscoveredJun 10, 2025Listed
Listed rate$0.005 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗$0.10 in / $0.00 out/ 1M tokensExperiential · all routes ↗Standard list rate$0.01 in / $0.00 out/ 1M tokensOpenRouter · all routes ↗
Provider endpoints1 endpoint →1 endpoint →1 endpoint →
FreshStack · MTEB 1.38.43 ↗MTEB 1.38.43; test/default; dataset 7a20df1aNot reportedNot reported0.43017 ↗Revision 4e423935; test/defaultContributor reported
Context window5128.2K41K
Max output460—32K
Parameters——8B
Open weightsSupportedNot reportedSupported

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