Reduce Embedding Costs by 80%: OpenAI vs Cohere vs Local Models
Embeddings are a hidden cost driver in RAG applications. Most teams are using text-embedding-3-large when text-embedding-3-small is 80% cheaper and nearly identical in quality for most use cases. This guide shows you the right model for each scenario.
Why Embedding Costs Sneak Up On You
For every user query in a RAG application, you embed:
At scale, ingestion costs dominate. A legal tech company ingesting 1M documents/day at 500 tokens each spends:
Same quality for most use cases. $55/day difference. $20K/year difference.
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The Embedding Model Landscape (2026)
| Model | Cost per 1M tokens | Dimensions | MTEB Score | Best For |
|---|---|---|---|---|
| text-embedding-3-large | $0.13 | 3,072 | 64.6 | High-precision retrieval |
| text-embedding-3-small | $0.02 | 1,536 | 62.3 | General RAG |
| Cohere embed-v3 | $0.10 | 1,024 | 64.5 | Multilingual |
| Cohere embed-v3 (English) | $0.10 | 1,024 | 64.5 | Production RAG |
| nomic-embed-text (local) | $0 | 768 | 62.0 | High volume / private data |
| sentence-transformers/all-MiniLM | $0 | 384 | 56.2 | Low-resource scenarios |
The Decision Tree
Is your data multilingual?
├── Yes → Cohere embed-multilingual-v3.0 ($0.10/1M)
└── No:
Is your data sensitive / can't leave your servers?
├── Yes → nomic-embed-text (local, free)
└── No:
Is MTEB accuracy critical (legal, medical)?
├── Yes → text-embedding-3-large ($0.13/1M)
└── No → text-embedding-3-small ($0.02/1M) ✓For 85% of RAG use cases: text-embedding-3-small is the right answer.
Switching from text-embedding-3-large to small
# BEFORE
response = client.embeddings.create(
model="text-embedding-3-large",
input=texts
)
# AFTER — 6.5x cheaper, 1.5% less accurate
response = client.embeddings.create(
model="text-embedding-3-small",
input=texts
)Important: Switching models requires re-embedding your entire index. Plan for this:
def re_embed_index(texts: list[str], batch_size: int = 100):
# Re-embed in batches of 100 to avoid rate limits
all_embeddings = []
for i in range(0, len(texts), batch_size):
batch = texts[i:i + batch_size]
response = client.embeddings.create(
model="text-embedding-3-small",
input=batch
)
all_embeddings.extend([e.embedding for e in response.data])
return all_embeddingsGoing Local: nomic-embed-text
For teams with sensitive data or high volume, local embeddings eliminate costs entirely:
# Install Ollama (runs models locally)
curl -fsSL https://ollama.ai/install.sh | sh
ollama pull nomic-embed-textimport requests
def embed_local(text: str) -> list[float]:
response = requests.post(
"http://localhost:11434/api/embeddings",
json={"model": "nomic-embed-text", "prompt": text}
)
return response.json()["embedding"]
# Or use the Ollama Python client
from ollama import Client
client = Client()
response = client.embeddings(model="nomic-embed-text", prompt="Your text here")
embeddings = response["embedding"]Benchmarks on a MacBook M3 Pro:
Dimension Reduction: Another Cost Lever
OpenAI's text-embedding-3 models support dimensions to reduce vector size:
# Reduce from 1536 to 512 dimensions — 3x cheaper storage
response = client.embeddings.create(
model="text-embedding-3-small",
input=texts,
dimensions=512 # loses ~1% accuracy, saves 66% on vector storage
)For Pinecone or pgvector, smaller vectors mean: