Compare Embedding Models

Side-by-side comparison of embedding model specifications

	Cohere Embed Multilingual 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 Cohere Embed Multilingual v3Cohere	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	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	1K	1K	4K	—
Max tokens	512	33K	4K	—
Parameters	—	677M	7B	350M
License	Proprietary	CC BY-NC 4.0	Apache 2.0	Apache 2.0
EMBEDDING TYPES
Types	Dense	Dense	Dense	LateColBERT
MATRYOSHKA DIMENSIONS
Available sizes	—	32641282565127681024	—	—
INPUT MODALITIES
Text	✓	✓	✓	✓
Image	✓	—	—	—
PDF	—	—	—	—
OUTPUT TYPES
Single vector	✓	—	✓	—
Multi vector	—	—	—	✓
LANGUAGE SUPPORT
Languages	🌍 100+	—	🌍 Multi	🌍 Multi
PRICING
Per 1M tokens	—	—	—	—
BENCHMARKS
MMTEB	—	67.70	—	—
MTEB AVG	—	—	68.20	—
MTEB ENGLISH V2	—	71.70	—	—
MTEB RETRIEVAL	—	—	60.20	—