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	Cohere Embed 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 Cohere Embed v4Cohere	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	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	2K	1K	4K
Max tokens	33K	128K	33K	4K
Parameters	3.8B	—	677M	7B
License	Apache 2.0	Proprietary	CC BY-NC 4.0	Apache 2.0
EMBEDDING TYPES
Types	DenseLate	Dense	Dense	Dense
MATRYOSHKA DIMENSIONS
Available sizes	12825651210242048	25651210241536	32641282565127681024	—
INPUT MODALITIES
Text	✓	✓	✓	✓
Image	✓	✓	—	—
PDF	✓	✓	—	—
OUTPUT TYPES
Single vector	✓	✓	—	✓
Multi vector	✓	—	—	—
LANGUAGE SUPPORT
Languages	🌍 29+	🌍 100+	—	🌍 Multi
PRICING
Per 1M tokens	$0.050	—	—	—
BENCHMARKS
COIR	71.59	—	—	—
JINA VDR	84.11	—	—	—
LONGEMBED	67.11	—	—	—
MMTEB	66.49	—	67.70	—
MTEB AVG	—	—	—	68.20
MTEB EN	55.97	—	—	—
MTEB ENGLISH V2	—	—	71.70	—
MTEB RETRIEVAL	—	—	—	60.20
VIDORE	90.17	—	—	—