Compare Embedding Models

Side-by-side comparison of embedding model specifications

	Jina Embeddings v3▾ 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 v3Jina AI	Jina Embeddings v5 Omni Nano Text-Matching▾ 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 Nano Text-MatchingJina 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	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
SPECIFICATIONS
Max dimension	1K	768	4K	2K
Max tokens	8K	8K	4K	128K
Parameters	559M	0.95B	7B	—
License	CC BY-NC 4.0	CC BY-NC 4.0	Apache 2.0	Proprietary
EMBEDDING TYPES
Types	Dense	Dense	Dense	Dense
MATRYOSHKA DIMENSIONS
Available sizes	32641282565121024	3264128256512768	—	25651210241536
INPUT MODALITIES
Text	✓	✓	✓	✓
Image	—	✓	—	✓
PDF	—	✓	—	✓
OUTPUT TYPES
Single vector	✓	—	✓	✓
Multi vector	—	—	—	—
LANGUAGE SUPPORT
Languages	🌍 89+	—	🌍 Multi	🌍 100+
PRICING
Per 1M tokens	$0.020	—	—	—
BENCHMARKS
LONGEMBED	55.66	—	—	—
MMTEB	58.58	—	—	—
MTEB AVG	—	—	68.20	—
MTEB EN	54.33	—	—	—
MTEB RETRIEVAL	—	—	60.20	—