Embedding models convert text into numerical vectors for semantic search, RAG pipelines, clustering, and similarity matching. Compare pricing, vector dimensions, and context limits across 7 providers and 14 models to find the right fit for your application.
14
Total Models
Free
Cheapest Price
128K
Largest Context
3,072
Most Dimensions
| # | Provider | Model | Dimensions | Price / 1M Tokens |
|---|---|---|---|---|
| 1 | text-embedding-004 | 768 | Free Free | |
| 2 | Together AI | togethercomputer/m2-bert-80M-8k-retrieval | 768 | $0.008 Cheapest |
| 3 | Fireworks | nomic-embed-text-v1.5 | 768 | $0.008 Cheapest |
| 4 | OpenAI | text-embedding-3-small | 1,536 | $0.02 |
| 5 | Voyage AI | voyage-3-lite | 512 | $0.02 |
| 6 | Voyage AI | voyage-3 | 1,024 | $0.06 |
| 7 | OpenAI | text-embedding-ada-002 | 1,536 | $0.10 |
| 8 | Cohere | embed-v4.0 | 1,024 | $0.10 |
| 9 | Cohere | embed-english-v3.0 | 1,024 | $0.10 |
| 10 | Cohere | embed-multilingual-v3.0 | 1,024 | $0.10 |
| 11 | Mistral | mistral-embed | 1,024 | $0.10 |
| 12 | OpenAI | text-embedding-3-large | 3,072 | $0.13 |
| 13 | Voyage AI | voyage-3-large | 1,024 | $0.18 |
| 14 | Voyage AI | voyage-code-3 | 1,024 | $0.18 |
Go beyond keyword matching by embedding both queries and documents into the same vector space. Retrieve results based on meaning, not just exact word overlap. Ideal for knowledge bases, documentation search, and e-commerce product discovery where users describe what they want in natural language.
Embed your document corpus and store the vectors in a database. At query time, retrieve the most relevant chunks and feed them to a language model as context. This gives LLMs access to your private data without fine-tuning, improving factual accuracy and reducing hallucinations.
Group similar documents, support tickets, or customer feedback automatically. Embeddings enable unsupervised clustering (e.g., k-means on vectors) and few-shot classification where you compare new inputs against labeled examples by vector similarity instead of training a dedicated classifier.
Measure how semantically similar two texts are using cosine similarity or dot product on their embeddings. Use this for duplicate detection across large datasets, recommendation engines (find similar articles, products, or content), and anomaly detection in text streams.
Higher-dimensional vectors (3072) capture more nuance but consume more storage in your vector database and slow down nearest-neighbor lookups. For most applications, 1024 dimensions provide an excellent balance. If storage is a concern, look for models that support Matryoshka embeddings, which let you truncate vectors to 256-512 dimensions with minimal quality loss.
Models with small context windows (512 tokens) force you to split documents into tiny chunks, losing paragraph-level coherence. Models with 8K-32K windows allow larger, more meaningful chunks. Cohere embed-v4.0 at 128K tokens can embed entire documents without chunking. Choose based on your chunking strategy and document lengths.
Embedding costs scale linearly with your corpus size. For a million-document corpus averaging 500 tokens each, the difference between $0.008/M tokens and $0.18/M tokens is $4 vs. $90 for initial embedding. Factor in re-embedding frequency and query volume. Free tiers (Google) are great for prototyping but may have rate limits.
General-purpose models (text-embedding-3-small, embed-v4.0) work well across domains. Voyage AI offers voyage-code-3, which is optimized for code retrieval and performs significantly better on code search benchmarks. If your use case is predominantly code, a specialized model may justify its higher price.
3 models
From $0.02/M
Up to 3,072 dimensions
Max 8K tokens
3 models
From $0.1/M
Up to 1,024 dimensions
Max 128K tokens
1 model
From Free
Up to 768 dimensions
Max 2K tokens
4 models
From $0.02/M
Up to 1,024 dimensions
Max 32K tokens
1 model
From $0.1/M
Up to 1,024 dimensions
Max 8K tokens
1 model
From $0.008/M
Up to 768 dimensions
Max 8K tokens
1 model
From $0.008/M
Up to 768 dimensions
Max 8K tokens
An embedding model converts text (words, sentences, or documents) into dense numerical vectors. These vectors capture semantic meaning, so texts with similar meanings produce vectors that are close together in the embedding space. Embedding models are foundational to semantic search, retrieval-augmented generation (RAG), clustering, classification, and recommendation systems.
Google's text-embedding-004 is completely free for standard usage. Among paid options, Together AI's m2-bert-80M-8k-retrieval and Fireworks' nomic-embed-text-v1.5 are the cheapest at $0.008 per million tokens, followed by OpenAI's text-embedding-3-small and Voyage AI's voyage-3-lite at $0.02 per million tokens.
Higher dimensions can capture more nuanced semantic relationships, but the quality depends on the model's training data and architecture, not just the vector size. OpenAI's text-embedding-3-large (3072 dimensions) is generally more accurate than ada-002 (1536 dimensions), but models like Cohere embed-v4.0 achieve excellent results with 1024 dimensions. More dimensions also increase storage costs and retrieval latency in vector databases.
For RAG (Retrieval-Augmented Generation), consider context window size (to embed longer chunks), retrieval quality, and cost at scale. Cohere embed-v4.0 stands out with a 128K token context window. Voyage AI models offer 32K context and are known for strong retrieval benchmarks. OpenAI text-embedding-3-small provides a good balance of quality and cost for most RAG applications.
The context window (max input tokens) determines the longest text you can embed in a single API call. Models with small windows (512 tokens) require you to split documents into small chunks before embedding, which can lose cross-sentence context. Models with large windows (32K-128K tokens) can embed entire documents or long passages at once, preserving more meaning.
Some models like OpenAI's text-embedding-3 series support Matryoshka representation learning, which allows you to truncate vectors to fewer dimensions (e.g., 256 or 512 instead of 1536) with minimal quality loss. This can significantly reduce vector database storage and speed up similarity searches while maintaining most of the retrieval accuracy.
It depends on document length and the model you choose. If each document averages 500 tokens, a million documents is 500 million tokens. At $0.02/M tokens (text-embedding-3-small), that costs $10. At $0.13/M tokens (text-embedding-3-large), it costs $65. With Google's free model, the API cost is $0. You also need to factor in vector database storage costs.
Compare full LLM pricing, estimate your API costs, or find the best models for retrieval-augmented generation.