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Compare Embedding Models

Side-by-side comparison of embedding model specifications

Linq-Embed-Mistral▾
Jina Embeddings v4 Jina Embeddings v3 Qwen3-Embedding-8B Gemini Embedding 001 mxbai-embed-large-v1 Cohere Embed v4 Cohere Embed English v3 Cohere Embed Multilingual v3 Voyage 3 Large GTE-Qwen2-7B-instruct✓ Linq-Embed-Mistral LFM2-ColBERT-350M Jina Embeddings v5 Omni Small Jina Embeddings v5 Omni Nano Jina Embeddings v5 Omni Nano Text-Matching Jina Embeddings v5 Text Small Jina Embeddings v5 Text Nano Jina Embeddings v5 Text Small Text-Matching
Linq-Embed-MistralLinq AI
Qwen3-Embedding-8B▾
Jina Embeddings v4 Jina Embeddings v3✓ Qwen3-Embedding-8B Gemini Embedding 001 mxbai-embed-large-v1 Cohere Embed v4 Cohere Embed English v3 Cohere Embed Multilingual v3 Voyage 3 Large GTE-Qwen2-7B-instruct Linq-Embed-Mistral LFM2-ColBERT-350M Jina Embeddings v5 Omni Small Jina Embeddings v5 Omni Nano Jina Embeddings v5 Omni Nano Text-Matching Jina Embeddings v5 Text Small Jina Embeddings v5 Text Nano Jina Embeddings v5 Text Small Text-Matching
Qwen3-Embedding-8BQwen
Gemini Embedding 001▾
Jina Embeddings v4 Jina Embeddings v3 Qwen3-Embedding-8B✓ Gemini Embedding 001 mxbai-embed-large-v1 Cohere Embed v4 Cohere Embed English v3 Cohere Embed Multilingual v3 Voyage 3 Large GTE-Qwen2-7B-instruct Linq-Embed-Mistral LFM2-ColBERT-350M Jina Embeddings v5 Omni Small Jina Embeddings v5 Omni Nano Jina Embeddings v5 Omni Nano Text-Matching Jina Embeddings v5 Text Small Jina Embeddings v5 Text Nano Jina Embeddings v5 Text Small Text-Matching
Gemini Embedding 001Google
Gemini Embedding 001▾
Jina Embeddings v4 Jina Embeddings v3 Qwen3-Embedding-8B✓ Gemini Embedding 001 mxbai-embed-large-v1 Cohere Embed v4 Cohere Embed English v3 Cohere Embed Multilingual v3 Voyage 3 Large GTE-Qwen2-7B-instruct Linq-Embed-Mistral LFM2-ColBERT-350M Jina Embeddings v5 Omni Small Jina Embeddings v5 Omni Nano Jina Embeddings v5 Omni Nano Text-Matching Jina Embeddings v5 Text Small Jina Embeddings v5 Text Nano Jina Embeddings v5 Text Small Text-Matching
Gemini Embedding 001Google
SPECIFICATIONS
Max dimension 4K 4K 3K 3K
Max tokens 4K 33K 2K 2K
Parameters 7B 8B — —
License Apache 2.0 Apache 2.0 Proprietary Proprietary
EMBEDDING TYPES
Types
Dense
Dense
Dense
Dense
MATRYOSHKA DIMENSIONS
Available sizes —
3264128256512102420484096
1282565127681024153620483072
1282565127681024153620483072
INPUT MODALITIES
Text ✓ ✓ ✓ ✓
Image — — — —
PDF — — — —
OUTPUT TYPES
Single vector ✓ ✓ ✓ ✓
Multi vector — — — —
LANGUAGE SUPPORT
Languages 🌍 Multi 🌍 100+ 🌍 Multi 🌍 Multi
PRICING
Per 1M tokens — — $0.150 $0.150
BENCHMARKS
CMTEB CHINESE — 73.84 — —
MTEB 1536D — — 68.17 68.17
MTEB 2048D — — 68.16 68.16
MTEB 768D — — 67.99 67.99
MTEB AVG 68.20 — — —
MTEB EN V2 — 75.22 — —
MTEB MULTILINGUAL — 70.58 — —
MTEB MULTILINGUAL AVG — — 68.32 68.32
MTEB RETRIEVAL 60.20 — — —