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Compare Embedding Models

Side-by-side comparison of embedding model specifications

Jina Embeddings v5 Text Nano▾
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 v5 Text NanoJina 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
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
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
SPECIFICATIONS
Max dimension 768 3K 4K 4K
Max tokens 8K 2K 33K 32K
Parameters 239M — 8B 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
3264128256512768
1282565127681024153620483072
3264128256512102420484096
—
INPUT MODALITIES
Text ✓ ✓ ✓ ✓
Image — — — —
PDF — — — —
OUTPUT TYPES
Single vector — ✓ ✓ ✓
Multi vector — — — —
LANGUAGE SUPPORT
Languages — 🌍 Multi 🌍 100+ 🌍 Multi
PRICING
Per 1M tokens — $0.150 — —
BENCHMARKS
CMTEB 35 — — — 72.05
CMTEB CHINESE — — 73.84 —
MMTEB 65.50 — — —
MTEB 1536D — 68.17 — —
MTEB 2048D — 68.16 — —
MTEB 56 — — — 70.24
MTEB 768D — 67.99 — —
MTEB EN V2 — — 75.22 —
MTEB ENGLISH V2 71.00 — — —
MTEB FR — — — 68.25
MTEB MULTILINGUAL — — 70.58 —
MTEB MULTILINGUAL AVG — 68.32 — —
MTEB PL — — — 67.86