Cohere Embed — Multilingual AI Embeddings API

What is Cohere Embed?

Cohere Embed is a multilingual embedding API that converts text into high-dimensional vectors for semantic search, RAG, and classification. Version 4.0 supports 100+ languages, offers specialized input types (document vs. query), and includes built-in compression for storage efficiency. It consistently ranks among the top embedding models on the MTEB benchmark.

Answer-Ready: Cohere Embed v4.0 generates multilingual embeddings for search and RAG. Top MTEB benchmark scores, 100+ languages, specialized input types (document/query/classification). Binary and int8 compression for 32x storage savings. Production API with generous free tier.

Best for: Teams building multilingual search or RAG pipelines. Works with: Any vector database, LangChain, LlamaIndex. Setup time: Under 2 minutes.

Core Features

1. Input Types

# Different modes optimize for different tasks
docs = co.embed(texts=[...], input_type="search_document")    # For indexing
queries = co.embed(texts=[...], input_type="search_query")     # For searching
classify = co.embed(texts=[...], input_type="classification")  # For classification
cluster = co.embed(texts=[...], input_type="clustering")       # For clustering

2. Compression (32x Savings)

response = co.embed(
    texts=["Hello world"],
    model="embed-v4.0",
    input_type="search_document",
    embedding_types=["float", "int8", "ubinary"],
)

# float: 1024 x 4 bytes = 4KB per vector
# int8:  1024 x 1 byte  = 1KB per vector (4x savings)
# binary: 1024 / 8 bytes = 128B per vector (32x savings)

3. Multilingual (100+ Languages)

# Same model handles all languages — no separate models needed
texts = [
    "What is AI?",           # English
    "AI 是什么？",            # Chinese
    "AIとは何ですか？",       # Japanese
    "Was ist KI?",           # German
]
response = co.embed(texts=texts, model="embed-v4.0", input_type="search_document")
# Cross-lingual similarity works automatically

4. Batch Processing

# Embed up to 96 texts per request
all_embeddings = []
for batch in chunks(documents, 96):
    response = co.embed(texts=batch, model="embed-v4.0", input_type="search_document")
    all_embeddings.extend(response.embeddings.float_)

Cohere Embed vs Alternatives

Pricing

FAQ

Q: How does it compare to OpenAI embeddings? A: Comparable quality on MTEB, better multilingual support, and built-in binary compression for significant storage savings.

Q: Can I use it with Pinecone/Qdrant/Weaviate? A: Yes, generate embeddings with Cohere and store in any vector database.

Q: Is there an open-source alternative? A: BGE-M3 and E5-Mistral are strong open-source options, but require self-hosting.