Compare Embedding Models

Side-by-side comparison of embedding model specifications

	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	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	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	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	2K	3K	768	4K
Max tokens	128K	2K	8K	32K
Parameters	—	—	1.04B	7B
License	Proprietary	Proprietary	CC BY-NC 4.0	Apache 2.0
EMBEDDING TYPES
Types	Dense	Dense	Dense	Dense
MATRYOSHKA DIMENSIONS
Available sizes	25651210241536	1282565127681024153620483072	3264128256512768	—
INPUT MODALITIES
Text	✓	✓	✓	✓
Image	✓	—	✓	—
PDF	✓	—	✓	—
OUTPUT TYPES
Single vector	✓	✓	—	✓
Multi vector	—	—	—	—
LANGUAGE SUPPORT
Languages	🌍 100+	🌍 Multi	—	🌍 Multi
PRICING
Per 1M tokens	—	$0.150	—	—
BENCHMARKS
CMTEB 35	—	—	—	72.05
MTEB 1536D	—	68.17	—	—
MTEB 2048D	—	68.16	—	—
MTEB 56	—	—	—	70.24
MTEB 768D	—	67.99	—	—
MTEB FR	—	—	—	68.25
MTEB MULTILINGUAL AVG	—	68.32	—	—
MTEB PL	—	—	—	67.86