Best Embedding Models in 2026
We benchmarked the top embedding models on retrieval accuracy, latency, and dimensional efficiency using MTEB and custom evaluation sets. This guide covers text, image, and multimodal embedding options for production applications.
How We Evaluated
Retrieval Quality
NDCG@10 and recall scores on MTEB benchmark tasks and domain-specific evaluation sets.
Latency & Throughput
Embedding generation speed per document and batch throughput for large-scale indexing.
Dimensional Efficiency
Quality of embeddings relative to vector dimensionality, considering storage and search costs.
Multimodal Support
Ability to embed multiple data types (text, image, video, audio) into a shared vector space.
Mixpeek
Multimodal AI platform offering configurable embedding models including E5, ArcFace, and Vertex multimodal embeddings. Manages the full pipeline from content to embeddings to indexed vectors with support for ColBERT and SPLADE.
Pros
- +Multiple embedding models configurable per pipeline
- +ColBERT, ColPaLI, and SPLADE for advanced retrieval
- +Unified embedding space across text, image, video, and audio
- +Handles embedding generation and indexing end-to-end
Cons
- -Not a standalone embedding API for quick vector generation
- -Embedding model selection tied to pipeline configuration
- -Requires understanding of retrieval pipeline concepts
OpenAI Embeddings
OpenAI's text embedding API featuring text-embedding-3-small and text-embedding-3-large models. Offers strong text retrieval quality with adjustable dimensionality through Matryoshka representations.
Pros
- +Strong text retrieval quality on MTEB benchmarks
- +Adjustable dimensionality for storage optimization
- +Simple API with fast response times
- +Large developer community and ecosystem
Cons
- -Text-only, no native image or video embeddings
- -No self-hosted option for data sovereignty
- -Pricing per token at scale can be significant
Cohere Embed
Cohere's embedding models with strong multilingual support and search-optimized variants. Offers embed-v3 with 1024 dimensions and input type optimization for queries vs documents.
Pros
- +Excellent multilingual support across 100+ languages
- +Search-optimized with separate query and document modes
- +Strong MTEB scores especially for multilingual retrieval
- +Compression support for cost-effective storage
Cons
- -Text-only, no multimodal embedding support
- -Pricing higher than OpenAI for comparable workloads
- -Enterprise features require sales engagement
Jina AI Embeddings
Open-weight embedding models with strong multimodal capabilities. jina-embeddings-v3 supports text with 8192 token context, while jina-clip-v2 provides text-image unified embeddings.
Pros
- +Open-weight models available for self-hosting
- +Text-image multimodal embeddings via CLIP variants
- +Long context support up to 8192 tokens
- +Competitive pricing for API usage
Cons
- -Multimodal models less mature than text-only variants
- -Smaller community than OpenAI or Cohere
- -Self-hosted deployment requires GPU infrastructure
Voyage AI
Embedding models optimized for retrieval quality, consistently ranking high on MTEB benchmarks. Offers domain-specific models for code, legal, and financial content.
Pros
- +Top MTEB scores for retrieval tasks
- +Domain-specific models for code, legal, and finance
- +Good context window up to 32K tokens
- +Simple API design
Cons
- -Text-only, no multimodal capabilities
- -Smaller company with less ecosystem support
- -Limited self-hosting options
Google Vertex AI Embeddings
Google's embedding models including multimodal embeddings from Gemini. Offers text-embedding-005 for text and multimodal embeddings supporting text, images, and video in a shared space.
Pros
- +True multimodal embeddings spanning text, image, and video
- +Strong text embedding quality
- +GCP ecosystem integration
- +Generous free tier for experimentation
Cons
- -GCP lock-in for production usage
- -Multimodal embedding dimensions are fixed
- -Less flexibility for custom embedding pipelines
Frequently Asked Questions
What are embedding models and why do they matter for search?
Embedding models convert text, images, or other content into dense numerical vectors that capture semantic meaning. Similar content produces similar vectors, enabling semantic search where queries match by meaning rather than keywords. The quality of your embeddings directly determines your search relevance.
How do I choose between text-only and multimodal embedding models?
Use text-only models when your content and queries are purely textual, as they typically offer higher text retrieval quality. Choose multimodal models when you need to search across content types, such as finding images with text queries or matching video frames to text descriptions. Platforms like Mixpeek let you use different models for different use cases.
Does embedding dimension size matter?
Higher dimensions generally capture more semantic nuance but increase storage costs and search latency. For most applications, 768-1024 dimensions provide an excellent quality-to-cost ratio. Models with Matryoshka representations let you truncate dimensions to find your optimal trade-off.
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