OpenAI Embeddings vs Cohere Embeddings: which should you choose?
Use openai embeddings if you need the highest semantic quality and don't mind $0.02 per 1M tokens. Use cohere embeddings if you want lower cost ($0.10 per 1M tokens) and fine-tuning control.
VERDICT
Side-by-side comparison
| Feature | openai embeddings | cohere embeddings | Winner |
|---|---|---|---|
| Model name (latest) | text-embedding-3-large | embed-english-v3.0 | Tie |
| Vector dimensions | 3072 (large), 1536 (small) | 1024 (default), configurable | cohere embeddings |
| Cost per 1M tokens | $0.02 (small), $0.06 (large) | $0.10 | openai embeddings |
| Latency (p50) | ~40ms avg | ~60-80ms avg | openai embeddings |
| MTEB benchmark score | 64.3 (text-embedding-3-large) | 62.0 (embed-english-v3.0) | openai embeddings |
| Fine-tuning support | No | Yes (via Cohere API) | cohere embeddings |
| Max input tokens | 8191 | 512 (standard), 2048 (long) | openai embeddings |
| Multilingual support | Weak (English-optimized) | Strong (100+ languages) | cohere embeddings |
| API rate limits | 3,500 req/min (free tier) | 1,000 req/min (free tier) | openai embeddings |
| Open source alternative | No | No | Tie |
Performance benchmarks
Semantic retrieval quality (MTEB: higher is better)
Measured on MTEB retrieval benchmarks; OpenAI's large model achieves state-of-the-art for general embeddings
Cost per 1M embedding calls (100k documents, 200 tokens avg)
OpenAI small model is 16x cheaper; large model is 5.5x cheaper than Cohere for equal volume
Latency p50 (single-token embedding call)
OpenAI's infrastructure typically faster; both support batching for throughput
Max input sequence length
OpenAI handles long documents without truncation; Cohere requires document chunking for most use cases
Fine-tuning capability
Cohere allows domain-specific embedding customization; OpenAI embeddings are fixed
When to use each
- ✓ Building a general-purpose semantic search or RAG system where you want the best-in-class retrieval quality with minimal tuning: OpenAI's text-embedding-3-large consistently ranks highest on MTEB benchmarks
- ✓ You need to embed documents longer than 512 tokens without chunking: OpenAI handles up to 8191 tokens in a single call, while Cohere truncates at 512
- ✓ Cost is secondary to quality and you have an OpenAI API key already active: text-embedding-3-small is only $0.02 per 1M tokens, making bulk embedding economical
- ✓ You need the fastest single-call latency in a real-time search interface: OpenAI typically returns results in 35-50ms vs Cohere's 60-100ms
- ✓ Building with Python/Node.js where OpenAI SDK is the default choice: integrated into LangChain, LlamaIndex, and Vercel AI by default
- ✓ You need embeddings fine-tuned to your domain (e.g., legal docs, medical records) and can't use OpenAI's fixed embeddings: Cohere's fine-tuning API adapts embeddings to your terminology and semantic patterns
- ✓ Supporting 100+ languages and need strong multilingual embedding quality: Cohere's embed-multilingual-v3.0 handles diverse languages better than OpenAI's English-optimized models
- ✓ Working with shorter text snippets (tweets, search queries, product descriptions <512 tokens) where cost matters more than semantic depth: Cohere's standard model is cheaper and sufficient
- ✓ You're already on Cohere's platform for text generation (Command model) and want unified billing and API authentication
- ✓ Building on Vercel/Next.js where Cohere's SDKs are lightweight and the API is a natural fit for serverless functions
Common misconceptions
openai embeddings
OpenAI embeddings can be fine-tuned to improve domain-specific retrieval
OpenAI does not offer fine-tuning for embeddings. The text-embedding-3 models are fixed. If your domain has unique terminology, Cohere's fine-tuning is the only API-based option.
You need to use the large 3072-dimensional model for good quality
text-embedding-3-small (1536 dims) performs nearly as well as -large on most benchmarks (62 vs 64.3) while costing 66% less. Start with small; upgrade only if retrieval quality drops.
OpenAI embeddings work equally well in all languages
OpenAI embeddings are English-optimized. For multilingual tasks, Cohere's embed-multilingual-v3.0 significantly outperforms OpenAI on non-English retrieval by 5-10 points on MTEB.
cohere embeddings
Cohere embeddings are cheaper so they must be lower quality
Cohere embeddings are competitive on retrieval quality (62.0 vs 64.3 MTEB) but cost 5x less. The quality gap is real but often negligible for production use. Fine-tuning can close it.
You can embed entire documents with Cohere's 512-token limit
Cohere's standard model truncates at 512 tokens. For documents >512 tokens, you must chunk and embed parts separately, then aggregate vectors: adding complexity. OpenAI's 8191-token limit avoids this.
Cohere fine-tuning is straightforward and quick
Cohere fine-tuning requires labeled datasets, custom training runs (days to weeks), and verification. It's powerful but not a quick plug-and-play upgrade for ad-hoc domains.
Code examples
Task: Embed a text string and return a vector using the OpenAI API.
import os
from openai import OpenAI
client = OpenAI(api_key=os.environ["OPENAI_API_KEY"])
# Create embedding using text-embedding-3-small
response = client.embeddings.create(
model="text-embedding-3-small", # OpenAI model choice
input="The quick brown fox jumps over the lazy dog"
)
# Extract vector
vector = response.data[0].embedding
print(f"Vector dimension: {len(vector)}")
print(f"First 5 values: {vector[:5]}")OpenAI's embeddings.create() is the primary method; you specify the model name and input text directly, and receive embedding vectors in the response.data array.
import os
import cohere
client = cohere.ClientV2(api_key=os.environ["COHERE_API_KEY"])
# Create embedding using embed-english-v3.0
response = client.embed(
model="embed-english-v3.0", # Cohere model choice
texts=["The quick brown fox jumps over the lazy dog"],
input_type="search_document"
)
# Extract vector
vector = response.embeddings[0]
print(f"Vector dimension: {len(vector)}")
print(f"First 5 values: {vector[:5]}")Cohere's embed() method requires input_type ('search_document' or 'search_query') to optimize embeddings for retrieval; texts are passed as an array, and vectors are returned in embeddings list.
Migration path
- Switching from OpenAI to Cohere embeddings:
- Install: pip install cohere instead of openai.
- Replace client initialization: from openai import OpenAI → import cohere; client = cohere.ClientV2(api_key=...).
- Replace the embedding call: client.embeddings.create(model='text-embedding-3-small', input=text) → client.embed(model='embed-english-v3.0', texts=[text], input_type='search_document').
- Extract the vector: response.data[0].embedding → response.embeddings[0].
- Update vector dimensions in your database schema from 1536 (OpenAI small) to 1024 (Cohere standard). If you're using LangChain, replace from langchain_openai import OpenAIEmbeddings with from langchain_cohere import CohereEmbeddings. Cost savings are typically 5-10x, but run retrieval benchmarks on your domain first to confirm quality is acceptable.
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