Capability · Comparison
Cohere Embed v3 vs OpenAI text-embedding-3-large
Cohere Embed v3 and OpenAI text-embedding-3-large are the two leading closed-API text embedding models in 2026-04. Cohere ships multilingual and English variants with compression-aware training — its embeddings stay accurate at lower precision, which cuts storage. OpenAI's 3-large is the quality leader on English retrieval benchmarks and offers Matryoshka-style dimension truncation. The decision comes down to language mix, cost, and which provider your stack is already on.
Side-by-side
| Criterion | Cohere Embed v3 | OpenAI text-embedding-3-large |
|---|---|---|
| Native dimensions | 1024 | 3072 (truncatable via dimensions param) |
| Multilingual coverage | 100+ languages (multilingual variant) | Good, English-centric at top of benchmarks |
| English retrieval (MTEB) | Strong, slightly below 3-large on pure English | Top tier on English MTEB |
| Compression friendliness | Int8 and binary embeddings supported natively | Matryoshka truncation; binary requires post-hoc |
| Pricing ($/M tokens, as of 2026-04) | $0.10 | $0.13 |
| Rate limits | High for trial + enterprise | High on paid tier |
| Best-fit stack | Cohere, AWS Bedrock, Azure AI | OpenAI, Azure OpenAI, many vector DBs |
| Task-type prompting | Yes — input_type parameter (search_document, search_query) | Not required |
Verdict
For multilingual products, cost-sensitive deployments, or any team that wants to ship compressed int8 or binary embeddings at scale, Cohere Embed v3 is the stronger pick — its training explicitly targets quality under compression. For English-first retrieval where raw MTEB quality matters and you're already on the OpenAI stack, 3-large is the easier and slightly higher-quality choice. The performance gap on English is small enough that cost and language mix usually decide.
When to choose each
Choose Cohere Embed v3 if…
- Your product serves multilingual users (100+ languages supported).
- You plan to store int8 or binary embeddings to cut vector DB costs.
- You're on AWS Bedrock or want a clean Cohere stack.
- You want task-type-aware embeddings via the input_type parameter.
Choose OpenAI text-embedding-3-large if…
- Your workload is English-first and retrieval quality is paramount.
- You're already on OpenAI or Azure OpenAI.
- You want Matryoshka truncation to trade quality for speed.
- Ecosystem integration is a priority (most vector DBs have OpenAI helpers).
Frequently asked questions
How much storage do I save with binary embeddings?
Roughly 32x versus float32 storage, with small retrieval quality trade-offs. Cohere Embed v3 is specifically trained to be robust under this compression.
Can I truncate OpenAI 3-large to 1024 dimensions?
Yes — pass dimensions=1024. Quality drops gracefully thanks to the Matryoshka training objective.
Which is better for code embeddings?
Neither is optimised for code specifically; for code-heavy RAG look at models like Voyage Code, BGE-M3 Code, or OpenAI's code-specific options.
Sources
- Cohere — Embed v3 — accessed 2026-04-20
- OpenAI — Embeddings — accessed 2026-04-20