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Compare Embedding Models

Side-by-side comparison of embedding model specifications

Jina Embeddings v5 Omni 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 Omni NanoJina AI
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
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 768 4K 4K 1K
Max tokens 8K 4K 33K 512
Parameters 1.04B 7B 8B 335M
License CC BY-NC 4.0 Apache 2.0 Apache 2.0 Apache 2.0
EMBEDDING TYPES
Types
Dense
Dense
Dense
Dense
MATRYOSHKA DIMENSIONS
Available sizes
3264128256512768
—
3264128256512102420484096
641282565121024
INPUT MODALITIES
Text ✓ ✓ ✓ ✓
Image ✓ — — —
PDF ✓ — — —
OUTPUT TYPES
Single vector — ✓ ✓ ✓
Multi vector — — — —
LANGUAGE SUPPORT
Languages — 🌍 Multi 🌍 100+ —
PRICING
Per 1M tokens — — — —
BENCHMARKS
CMTEB CHINESE — — 73.84 —
MTEB AVG — 68.20 — 64.68
MTEB EN V2 — — 75.22 —
MTEB MULTILINGUAL — — 70.58 —
MTEB RETRIEVAL — 60.20 — 54.39
MTEB STS — — — 85.00