Compare Embedding Models

Side-by-side comparison of embedding model specifications

	Voyage 3 Large▾ 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 Voyage 3 LargeVoyage 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	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	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	2K	2K	3K	—
Max tokens	32K	33K	2K	—
Parameters	—	3.8B	—	350M
License	Proprietary	Apache 2.0	Proprietary	Apache 2.0
EMBEDDING TYPES
Types	Dense	DenseLate	Dense	LateColBERT
MATRYOSHKA DIMENSIONS
Available sizes	25651210242048	12825651210242048	1282565127681024153620483072	—
INPUT MODALITIES
Text	✓	✓	✓	✓
Image	—	✓	—	—
PDF	—	✓	—	—
OUTPUT TYPES
Single vector	✓	✓	✓	—
Multi vector	—	✓	—	✓
LANGUAGE SUPPORT
Languages	🌍 Multi	🌍 29+	🌍 Multi	🌍 Multi
PRICING
Per 1M tokens	—	$0.050	$0.150	—
BENCHMARKS
COIR	—	71.59	—	—
JINA VDR	—	84.11	—	—
LONGEMBED	—	67.11	—	—
MMTEB	—	66.49	—	—
MTEB 1536D	—	—	68.17	—
MTEB 2048D	—	—	68.16	—
MTEB 768D	—	—	67.99	—
MTEB EN	—	55.97	—	—
MTEB MULTILINGUAL AVG	—	—	68.32	—
VIDORE	—	90.17	—	—