Embedding Models Comparison
Choose the right embedding model for your use case.
Best Quality
text-embedding-3-large
OpenAI, 3072 dimensions
Best Open Source
all-mpnet-base-v2
Sentence Transformers, free
Best Multilingual
embed-multilingual-v3
Cohere, 100+ languages
| Model | Provider | Dimensions | Max Tokens | Cost | Multilingual |
|---|---|---|---|---|---|
| text-embedding-3-large | OpenAI | 3072 | 8191 | $0.13/1M tokens | ✓ |
| text-embedding-3-small | OpenAI | 1536 | 8191 | $0.02/1M tokens | ✓ |
| embed-english-v3.0 | Cohere | 1024 | 512 | $0.10/1M tokens | ✗ |
| embed-multilingual-v3.0 | Cohere | 1024 | 512 | $0.10/1M tokens | ✓ (100+) |
| all-mpnet-base-v2 | Sentence Transformers | 768 | 384 | Free | ✗ |
| all-MiniLM-L6-v2 | Sentence Transformers | 384 | 256 | Free | ✗ |
| multilingual-e5-large | Microsoft | 1024 | 512 | Free | ✓ (100+) |
| voyage-large-2 | Voyage AI | 1536 | 16000 | $0.12/1M tokens | ✓ |
API-Based Models
OpenAI
Pros: Highest quality, great documentation, easy to use, good multilingual support
Cons: API dependency, costs at scale, data privacy concerns
Best for: Production applications where quality matters most
Cohere
Pros: Excellent multilingual, input type optimization, competitive pricing
Cons: Lower max tokens, smaller community
Best for: Multilingual applications, search-optimized embeddings
Voyage AI
Pros: Very long context (16K tokens), domain-specific models
Cons: Newer provider, smaller ecosystem
Best for: Long documents, legal/code specific use cases
Pros: Integrated with GCP, good quality
Cons: GCP lock-in, complex pricing
Best for: Teams already on Google Cloud
Open Source Models
Sentence Transformers
Pros: Free, runs locally, many model options, active community
Cons: Requires compute resources, setup complexity
Best for: Cost-sensitive applications, privacy-focused use cases
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('all-mpnet-base-v2')
embeddings = model.encode(texts)E5 Models (Microsoft)
Pros: Excellent multilingual, state-of-the-art quality, free
Cons: Requires GPU for efficiency
Best for: Multilingual search, high-quality free option
from sentence_transformers import SentenceTransformer
model = SentenceTransformer('intfloat/multilingual-e5-large')
embeddings = model.encode(texts)Quick Decision Guide
Budget-conscious?
Use Sentence Transformers (all-MiniLM-L6-v2 for speed, all-mpnet-base-v2 for quality)
Multiple languages?
Cohere embed-multilingual-v3 or Microsoft multilingual-e5-large
Best quality, cost no object?
OpenAI text-embedding-3-large
Very long documents?
Voyage AI voyage-large-2 (16K context)
Privacy critical?
Self-host with Sentence Transformers or E5