Embedding models turn text into numerical vectors that capture semantic meaning. They power RAG systems, semantic search, classification, clustering, and recommendation engines. Choosing the right one affects retrieval quality, latency, storage costs, and ultimately how well your AI application performs.
This guide compares every major embedding model available in April 2026, with benchmark data, pricing, and specific recommendations by use case.
Embedding Model Comparison Table
| Model | Provider | Dimensions | Max Tokens | MTEB Avg Score | Price per 1M Tokens |
|---|---|---|---|---|---|
| text-embedding-3-large | OpenAI | 3,072 (configurable) | 8,191 | 64.6 | $0.13 |
| text-embedding-3-small | OpenAI | 1,536 (configurable) | 8,191 | 62.3 | $0.02 |
| embed-v4 | Cohere | 1,024 (configurable) | 512 | 66.3 | $0.10 |
| voyage-3-large | Voyage AI | 1,024 | 32,000 | 67.2 | $0.18 |
| voyage-3-lite | Voyage AI | 512 | 32,000 | 63.1 | $0.02 |
| voyage-code-3 | Voyage AI | 1,024 | 32,000 | N/A (code-specific) | $0.18 |
| bge-m3 | BAAI (open source) | 1,024 | 8,192 | 65.0 | Free (self-host) |
| gte-Qwen2-7B-instruct | Alibaba (open source) | 3,584 | 131,072 | 65.4 | Free (self-host) |
| nomic-embed-text-v1.5 | Nomic (open source) | 768 (configurable) | 8,192 | 62.3 | Free (self-host) / $0.01 (API) |
| Gemini text-embedding | 768 | 2,048 | 63.8 | Free (API tier) |
MTEB (Massive Text Embedding Benchmark) scores represent average performance across retrieval, classification, clustering, and semantic similarity tasks. Higher is better, but a 2-3 point difference may not be noticeable in your specific use case.
Top Picks by Use Case
Best for RAG / Document Retrieval
Winner: Voyage voyage-3-large
Voyage AI's flagship model leads MTEB retrieval benchmarks with a 67.2 average score. Its 32,000 token context window means you can embed much longer chunks without truncation, which is critical for technical documents, legal texts, and research papers where meaning spans multiple paragraphs.
The $0.18/1M token price is reasonable for retrieval use cases where you embed documents once and query many times. For budget-sensitive deployments, voyage-3-lite at $0.02/1M provides 94% of the retrieval quality.
Best for Code Search
Winner: Voyage voyage-code-3
The only major embedding model specifically trained for code. It understands syntax, function signatures, variable naming conventions, and code structure across 20+ programming languages.
Best Price-Performance (API)
Winner: OpenAI text-embedding-3-small
At $0.02/1M tokens, it's the cheapest commercial embedding API with competitive quality (62.3 MTEB). The configurable dimensions feature lets you reduce from 1,536 to 256 dimensions with minimal quality loss, cutting vector storage costs by 6x.
Best Overall Quality (API)
Winner: Cohere embed-v4
Cohere's latest embedding model scores 66.3 on MTEB with only 1,024 dimensions, meaning excellent quality with moderate storage requirements. It supports input types (search_document, search_query, classification, clustering) that let you optimize embeddings for your specific task. The 512-token limit is its main weakness; long documents need careful chunking.
Best Open Source
Winner: BGE-M3 (BAAI)
BGE-M3 is the most versatile open-source embedding model. It supports dense, sparse, and multi-vector retrieval in a single model, which means you can do hybrid search without running separate models. It handles 100+ languages, making it the default choice for multilingual applications.
Best for Long Documents
Winner: GTE-Qwen2-7B-instruct
With a 131,072 token context window, GTE-Qwen2 can embed entire books in a single pass. The 7B parameter model requires significant GPU resources to self-host, but the quality (65.4 MTEB) and context length are unmatched.
Benchmark Deep Dive: What MTEB Actually Measures
MTEB isn't a single number. It's an average across 8 task categories, and models perform differently across them.
| Model | Retrieval | Classification | Clustering | STS (Similarity) |
|---|---|---|---|---|
| voyage-3-large | 58.2 | 86.1 | 54.3 | 84.5 |
| embed-v4 (Cohere) | 56.8 | 87.3 | 55.1 | 85.2 |
| bge-m3 | 55.3 | 85.7 | 53.8 | 83.9 |
| text-embedding-3-large | 55.4 | 85.9 | 52.1 | 82.7 |
| text-embedding-3-small | 53.1 | 83.4 | 50.3 | 80.2 |
| nomic-embed-text-v1.5 | 52.8 | 83.1 | 49.7 | 80.5 |
Key takeaways: retrieval scores are lower than other tasks across all models. The gap between models narrows on classification. Clustering performance varies most and benchmark averages don't predict it well.
Pricing Analysis: Cost Per Million Embeddings
| Model | API Cost per 1M Docs (avg 500 tokens) | Storage per 1M Vectors | Total Cost per 1M Docs |
|---|---|---|---|
| text-embedding-3-large (3072d) | $0.065 | 12.3 GB | $0.065 + storage |
| text-embedding-3-small (1536d) | $0.01 | 6.1 GB | $0.01 + storage |
| embed-v4 (1024d) | $0.05 | 4.1 GB | $0.05 + storage |
| voyage-3-large (1024d) | $0.09 | 4.1 GB | $0.09 + storage |
| voyage-3-lite (512d) | $0.01 | 2.0 GB | $0.01 + storage |
| nomic-embed-text (768d) | $0.005 | 3.1 GB | $0.005 + storage |
| bge-m3 (1024d, self-hosted) | ~$0.001 (GPU cost) | 4.1 GB | GPU rental + storage |
Storage costs depend on your vector database. See our Pinecone pricing and Weaviate pricing guides for details.
Dimensions: How to Choose
- 256-512 dimensions: Adequate for simple classification, FAQ matching, and low-resource environments.
- 768-1024 dimensions: The sweet spot for most production RAG systems.
- 1536-3072 dimensions: Marginal quality improvement over 1024. Justified only for high-precision applications (medical, legal).
Matryoshka embedding models (like OpenAI's text-embedding-3 family and Nomic) let you choose dimensions at query time. Embed at full dimensions once, then truncate for different use cases.
Multilingual Embedding
- BGE-M3: Best multilingual open-source option. 100+ languages with strong cross-lingual retrieval.
- Cohere embed-v4: 100+ languages with commercial support.
- OpenAI text-embedding-3: Good multilingual support but weaker on cross-lingual retrieval benchmarks.
- Voyage AI: Primarily English-optimized.
How to Evaluate for Your Use Case
- Create a test dataset. 50-100 queries with known relevant documents from your actual corpus.
- Embed with 3-4 candidate models. Use the same chunking strategy across all models.
- Measure retrieval quality. Track recall@5 and recall@10.
- Measure latency. Time the embedding and retrieval steps.
- Calculate total cost. Include API pricing, storage, and infrastructure.
Embedding model choice is a one-way door. Switching models later means re-embedding your entire corpus.
Integration with Vector Databases
- Pinecone: Works with all models. Integrated inference API bundles embedding with storage.
- Weaviate: Built-in vectorizer modules for OpenAI, Cohere, Hugging Face, and custom models.
- Chroma: Model-agnostic. Uses sentence-transformers by default.
- pgvector: Pure storage; you compute embeddings externally. Good for teams already on Postgres.
For a comprehensive vector database comparison, see our Best Vector Databases guide and specific matchups like Pinecone vs Chroma and Pinecone vs Weaviate.