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Compare Embedding Models

Side-by-side comparison of embedding model specifications

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
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
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
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 4K 768 — 4K
Max tokens 33K 8K — 32K
Parameters 8B 1.04B 350M 7B
License Apache 2.0 CC BY-NC 4.0 Apache 2.0 Apache 2.0
EMBEDDING TYPES
Types
Dense
Dense
LateColBERT
Dense
MATRYOSHKA DIMENSIONS
Available sizes
3264128256512102420484096
3264128256512768
— —
INPUT MODALITIES
Text ✓ ✓ ✓ ✓
Image — ✓ — —
PDF — ✓ — —
OUTPUT TYPES
Single vector ✓ — — ✓
Multi vector — — ✓ —
LANGUAGE SUPPORT
Languages 🌍 100+ — 🌍 Multi 🌍 Multi
PRICING
Per 1M tokens — — — —
BENCHMARKS
CMTEB 35 — — — 72.05
CMTEB CHINESE 73.84 — — —
MTEB 56 — — — 70.24
MTEB EN V2 75.22 — — —
MTEB FR — — — 68.25
MTEB MULTILINGUAL 70.58 — — —
MTEB PL — — — 67.86