Cohere Embed v3: Multilingual Embeddings Built for Enterprise RAG

Cohere's Embed v3 introduces a critical input_type parameter that tells the model whether it's encoding a query or a document - a distinction that meaningfully improves retrieval precision in production RAG pipelines.

Mahmudul Haque Qudrati

CEO & ML Engineer

March 20, 2026

7 min read

// tags

#cohere-embed#multilingual#embeddings#rag#enterprise

FIG. ART-26

7 min read

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Cohere Embed v3: Multilingual Embeddings Built for Enterprise RAG

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// LLMs & Language Models

When to Fine-Tune an LLM (And When to Rely on RAG Instead)

Fine-tuning updates model weights, while RAG inserts context. Learn how to combine these strategies or choose the right one for your data.

9 min read

// Machine Learning

ONNX: Export Any ML Model and Run It Anywhere

Model Variants

embed-english-v3.0 - 1024 dimensions, English only, highest English performance
embed-multilingual-v3.0 - 1024 dimensions, 108 languages, within 2-3 points of English-only on most tasks

Both output 1024-dimensional float32 vectors. Pricing is $0.10/1M tokens for both.

Compressed Embedding Formats

Cohere Embed v3 is one of the first production embedding APIs to natively support compressed output formats:

# int8 compression  -  4x storage reduction, ~1% quality loss
doc_embs_int8 = co.embed(
    texts=["Your document text here"],
    model="embed-english-v3.0",
    input_type="search_document",
    embedding_types=["int8"],
).embeddings.int8

# binary compression  -  32x storage reduction, ~3-5% quality loss
doc_embs_binary = co.embed(
    texts=["Your document text here"],
    model="embed-english-v3.0",
    input_type="search_document",
    embedding_types=["ubinary"],
).embeddings.ubinary

For a corpus of 10 million documents at 1024 dimensions:

float32: ~40GB
int8: ~10GB
binary: ~1.25GB

The binary format fits a 10M-document index in the RAM of a standard cloud VM, enabling in-memory vector search without specialized hardware.

MTEB Comparison

Model	MTEB English Retrieval	Multilingual
Cohere embed-english-v3.0	55.0	English only
Cohere embed-multilingual-v3.0	54.1	108 languages
OpenAI text-embedding-3-large	55.4	Good
Voyage-3	58.1	Limited

OpenAI edges ahead on raw MTEB retrieval, but Cohere's multilingual coverage (108 languages) and compressed format support make it the stronger choice for international enterprise deployments.

Weaviate Integration

import weaviate
from weaviate.classes.init import Auth

client = weaviate.connect_to_weaviate_cloud(
    cluster_url="YOUR_WEAVIATE_URL",
    auth_credentials=Auth.api_key("YOUR_WEAVIATE_KEY"),
    headers={"X-Cohere-Api-Key": "YOUR_COHERE_KEY"},
)

# Weaviate handles Cohere embedding automatically with text2vec-cohere
collection = client.collections.get("Document")
results = collection.query.near_text(
    query="machine learning optimization techniques",
    limit=5,
)

Cohere Embed v3: Multilingual Embeddings Built for Enterprise RAG

Related Articles

When to Fine-Tune an LLM (And When to Rely on RAG Instead)

ONNX: Export Any ML Model and Run It Anywhere

What Makes Embed v3 Different

The input_type Parameter

Model Variants

Compressed Embedding Formats

MTEB Comparison

Weaviate Integration

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Cohere Embed v3: Multilingual Embeddings Built for Enterprise RAG

Related Articles

When to Fine-Tune an LLM (And When to Rely on RAG Instead)

ONNX: Export Any ML Model and Run It Anywhere

What Makes Embed v3 Different

The input_type Parameter

Model Variants

Compressed Embedding Formats

MTEB Comparison

Weaviate Integration

Links

The workspace your teamactually needs

AI & ML insights, weekly

Mahmudul Haque Qudrati

Using LLMs for Business Analysis and Decision Support: What Works, What Doesn't

The workspace your team
actually needs