OpenAI text-embedding-3 vs Cohere embed-v4
A head-to-head comparison of two leading embedding models for AI-powered growth. See how they stack up on pricing, performance, and capabilities.
OpenAI text-embedding-3
Pricing: $0.02-0.13 per 1M tokens
Best for: Best general-purpose embeddings with flexible dimension tuning
Cohere embed-v4
Pricing: Free trial, then $0.10 per 1M tokens
Best for: Multilingual applications and cross-language search
Head-to-Head Comparison
| Criteria | OpenAI text-embedding-3 | Cohere embed-v4 |
|---|---|---|
| Accuracy (MTEB) | Top-tier — text-embedding-3-large scores 64.6 on MTEB | Top-tier — embed-v4 leads on multilingual retrieval benchmarks |
| Cost per 1M Tokens | $0.02 (small) / $0.13 (large) | $0.10 per 1M tokens (unified pricing) |
| Multilingual Support | 100+ languages but primarily optimized for English | 100+ languages with state-of-the-art cross-lingual retrieval |
| Self-Hosting | Not available — API only | Not available — API only |
| Dimension Flexibility | 256 to 3072 — Matryoshka embeddings allow truncation without retraining | Fixed 1024 dimensions for embed-v4 |
The Verdict
For English-first applications, OpenAI text-embedding-3-large delivers excellent accuracy at a lower cost than Cohere for comparable quality, and its Matryoshka architecture lets you trade dimension size against cost dynamically. Cohere embed-v4 leads when your retrieval needs to work across multiple languages, particularly for cross-lingual search where the query and documents may be in different languages. Neither model is self-hostable, so teams with data residency requirements should evaluate open-source alternatives.
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