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Compare Embedding Models

Side-by-side comparison of embedding model specifications

Jina Embeddings v5 Text Small▾
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 SmallJina 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
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
mxbai-embed-large-v1▾
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
mxbai-embed-large-v1Mixedbread
SPECIFICATIONS
Max dimension 1K 3K 1K 1K
Max tokens 33K 2K 8K 512
Parameters 677M — 559M 335M
License CC BY-NC 4.0 Proprietary CC BY-NC 4.0 Apache 2.0
EMBEDDING TYPES
Types
Dense
Dense
Dense
Dense
MATRYOSHKA DIMENSIONS
Available sizes
32641282565127681024
1282565127681024153620483072
32641282565121024
641282565121024
INPUT MODALITIES
Text ✓ ✓ ✓ ✓
Image — — — —
PDF — — — —
OUTPUT TYPES
Single vector — ✓ ✓ ✓
Multi vector — — — —
LANGUAGE SUPPORT
Languages — 🌍 Multi 🌍 89+ —
PRICING
Per 1M tokens — $0.150 $0.020 —
BENCHMARKS
LONGEMBED — — 55.66 —
MMTEB 67.70 — 58.58 —
MTEB 1536D — 68.17 — —
MTEB 2048D — 68.16 — —
MTEB 768D — 67.99 — —
MTEB AVG — — — 64.68
MTEB EN — — 54.33 —
MTEB ENGLISH V2 71.70 — — —
MTEB MULTILINGUAL AVG — 68.32 — —
MTEB RETRIEVAL — — — 54.39
MTEB STS — — — 85.00