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Compare Embedding Models

Side-by-side comparison of embedding model specifications

Jina Embeddings v4▾
✓ 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 v4Jina 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
LFM2-ColBERT-350M▾
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
LFM2-ColBERT-350MLiquid 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
SPECIFICATIONS
Max dimension 2K 1K — 4K
Max tokens 33K 512 — 4K
Parameters 3.8B 335M 350M 7B
License Apache 2.0 Apache 2.0 Apache 2.0 Apache 2.0
EMBEDDING TYPES
Types
DenseLate
Dense
LateColBERT
Dense
MATRYOSHKA DIMENSIONS
Available sizes
12825651210242048
641282565121024
— —
INPUT MODALITIES
Text ✓ ✓ ✓ ✓
Image ✓ — — —
PDF ✓ — — —
OUTPUT TYPES
Single vector ✓ ✓ — ✓
Multi vector ✓ — ✓ —
LANGUAGE SUPPORT
Languages 🌍 29+ — 🌍 Multi 🌍 Multi
PRICING
Per 1M tokens $0.050 — — —
BENCHMARKS
COIR 71.59 — — —
JINA VDR 84.11 — — —
LONGEMBED 67.11 — — —
MMTEB 66.49 — — —
MTEB AVG — 64.68 — 68.20
MTEB EN 55.97 — — —
MTEB RETRIEVAL — 54.39 — 60.20
MTEB STS — 85.00 — —
VIDORE 90.17 — — —