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	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	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
SPECIFICATIONS
Max dimension	768	4K	2K	—
Max tokens	8K	4K	33K	—
Parameters	1.04B	7B	3.8B	350M
License	CC BY-NC 4.0	Apache 2.0	Apache 2.0	Apache 2.0
EMBEDDING TYPES
Types	Dense	Dense	DenseLate	LateColBERT
MATRYOSHKA DIMENSIONS
Available sizes	3264128256512768	—	12825651210242048	—
INPUT MODALITIES
Text	✓	✓	✓	✓
Image	✓	—	✓	—
PDF	✓	—	✓	—
OUTPUT TYPES
Single vector	—	✓	✓	—
Multi vector	—	—	✓	✓
LANGUAGE SUPPORT
Languages	—	🌍 Multi	🌍 29+	🌍 Multi
PRICING
Per 1M tokens	—	—	$0.050	—
BENCHMARKS
COIR	—	—	71.59	—
JINA VDR	—	—	84.11	—
LONGEMBED	—	—	67.11	—
MMTEB	—	—	66.49	—
MTEB AVG	—	68.20	—	—
MTEB EN	—	—	55.97	—
MTEB RETRIEVAL	—	60.20	—	—
VIDORE	—	—	90.17	—