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 v5 Text Small▾ 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 Text SmallJina 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	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	2K	4K
Max tokens	32K	33K	33K	4K
Parameters	—	677M	3.8B	7B
License	Proprietary	CC BY-NC 4.0	Apache 2.0	Apache 2.0
EMBEDDING TYPES
Types	Dense	Dense	DenseLate	Dense
MATRYOSHKA DIMENSIONS
Available sizes	25651210242048	32641282565127681024	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	—	67.70	66.49	—
MTEB AVG	—	—	—	68.20
MTEB EN	—	—	55.97	—
MTEB ENGLISH V2	—	71.70	—	—
MTEB RETRIEVAL	—	—	—	60.20
VIDORE	—	—	90.17	—