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Compare Embedding Models

Side-by-side comparison of embedding model specifications

Jina Embeddings v3▾
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
Jina Embeddings v3Jina AI
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
GTE-Qwen2-7B-instruct▾
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
GTE-Qwen2-7B-instructAlibaba
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
SPECIFICATIONS
Max dimension 1K 3K 4K 4K
Max tokens 8K 2K 32K 4K
Parameters 559M — 7B 7B
License CC BY-NC 4.0 Proprietary Apache 2.0 Apache 2.0
EMBEDDING TYPES
Types
Dense
Dense
Dense
Dense
MATRYOSHKA DIMENSIONS
Available sizes
32641282565121024
1282565127681024153620483072
— —
INPUT MODALITIES
Text ✓ ✓ ✓ ✓
Image — — — —
PDF — — — —
OUTPUT TYPES
Single vector ✓ ✓ ✓ ✓
Multi vector — — — —
LANGUAGE SUPPORT
Languages 🌍 89+ 🌍 Multi 🌍 Multi 🌍 Multi
PRICING
Per 1M tokens $0.020 $0.150 — —
BENCHMARKS
CMTEB 35 — — 72.05 —
LONGEMBED 55.66 — — —
MMTEB 58.58 — — —
MTEB 1536D — 68.17 — —
MTEB 2048D — 68.16 — —
MTEB 56 — — 70.24 —
MTEB 768D — 67.99 — —
MTEB AVG — — — 68.20
MTEB EN 54.33 — — —
MTEB FR — — 68.25 —
MTEB MULTILINGUAL AVG — 68.32 — —
MTEB PL — — 67.86 —
MTEB RETRIEVAL — — — 60.20