Cohere

Definition

Cohere is an enterprise AI company that builds language models and APIs purpose-built for business applications, with a distinct focus on search, information retrieval, and retrieval-augmented generation (RAG). Unlike general-purpose providers that offer a broad range of consumer and developer features, Cohere targets enterprise customers who need reliable, production-ready NLP infrastructure — particularly for use cases where finding and surfacing the right information is the core problem.

Cohere's model lineup reflects this focus. Command R and Command R+ are conversational and instruction-following models optimized specifically for RAG workflows — they support long context windows and are trained to follow retrieval-grounded prompts reliably. Embed provides state-of-the-art multilingual dense vector embeddings across 100+ languages, making it the go-to choice for global enterprise search applications. Rerank is a cross-encoder model that takes an initial set of retrieved documents and re-scores them against the original query for precision that sparse and dense retrieval alone cannot achieve.

What differentiates Cohere from general-purpose providers like OpenAI is that its entire product suite is designed around the retrieval pipeline as a first-class workflow. The Embed, Rerank, and Command R models are built to work together as a cohesive stack, and Cohere offers on-premises and private cloud deployment options that meet stringent enterprise data governance and compliance requirements — a critical distinction for regulated industries like finance, healthcare, and government.

How it works

Chat and Generate API

The Command R and Command R+ models are accessed via Cohere's Chat API and support both conversational multi-turn interactions and single-turn generation tasks. Command R+ is the larger, more capable variant suited for complex reasoning and document-heavy RAG, while Command R is optimized for lower latency and cost in high-throughput production pipelines. Both models accept a documents parameter that allows you to pass retrieved context directly into the prompt, enabling a native RAG mode where the model is instructed to ground its answer in the supplied content and cite sources.

Embed API (multilingual embeddings)

The Embed API converts text into dense vector representations suitable for semantic similarity search. Cohere's embedding models support over 100 languages in a single model, making cross-lingual search and multilingual document retrieval possible without separate language-specific models. Embeddings can be generated with different input_type values — search_document for indexing content at rest, and search_query for encoding queries at runtime — a distinction that applies asymmetric training signals and typically improves retrieval accuracy compared to symmetric embedding schemes.

Rerank API

The Rerank API accepts a query and a list of candidate documents (usually the top-k results from a vector or keyword search) and returns each document with a relevance score computed by a cross-encoder. Cross-encoders evaluate the query and document jointly in a single forward pass, giving much higher precision than bi-encoders that encode query and document separately. Reranking is a lightweight but highly effective step that dramatically improves precision@k — it is most valuable when initial retrieval is relatively cheap (BM25 or ANN search) but precision needs to be maximized before passing context to an LLM.

RAG Integration

Cohere's RAG integration ties Embed, Rerank, and Command R together into a unified pipeline. The typical flow is: embed the query, run approximate nearest neighbor search in a vector database, rerank the top candidates to get the most relevant documents, then pass those documents to Command R with the original query for grounded generation. The model returns an answer along with citation objects that reference specific passages in the retrieved documents, making it straightforward to build auditable, source-cited AI applications.

When to use / When NOT to use

Use when	Avoid when
Building enterprise search or knowledge base Q&A where retrieval precision is critical	You need general-purpose chat assistance with no retrieval component
Your content spans multiple languages and you need a single embedding model for all of them	Your use case is primarily image, audio, or multimodal — Cohere is text-only
You want to add a reranking step to improve precision after an initial vector or BM25 search	You need highly capable reasoning, math, or coding for standalone tasks (GPT-4o or Claude may outperform)
Data governance requirements mandate on-premises or private cloud deployment	Your project is a quick prototype and you want the broadest ecosystem of integrations
You need source citations and document grounding natively in the model output	Budget is extremely tight — Cohere's enterprise pricing is higher than some alternatives

Comparisons

Criteria	Cohere	OpenAI	Mistral
Embedding quality (MTEB)	Top-tier multilingual, 100+ languages	Strong English-first (text-embedding-3-large)	Competitive; mistral-embed available
Reranking	Native Rerank API (cross-encoder)	No native reranking endpoint	No native reranking endpoint
RAG-native models	Command R/R+ designed for RAG with citations	GPT-4o works well with RAG prompts but not RAG-native	Mixtral/Mistral work with RAG prompts
Open weights	No (proprietary API only)	No (proprietary API only)	Yes (Mistral models on Hugging Face)
On-premises / private cloud	Yes (enterprise contracts)	Azure OpenAI (limited)	Yes (self-host open weights)
Multilingual embedding	Single model, 100+ languages	Separate or limited multilingual support	Limited multilingual embedding support
Pricing model	Enterprise / pay-per-token	Pay-per-token, well-documented	Pay-per-token; self-host option free

Pros and cons

Pros	Cons
Best-in-class multilingual embeddings in a single model	Smaller general ecosystem compared to OpenAI
Native Rerank API significantly improves retrieval precision	No open-weights option for self-hosting
Command R/R+ are purpose-built for grounded, cited RAG	Less capable than GPT-4o / Claude for complex standalone reasoning
Enterprise-grade deployment options including private cloud	Documentation and community resources thinner than OpenAI
RAG pipeline components (Embed + Rerank + Command R) work as a coherent stack	Pricing can be higher for small-scale experiments

Code examples

Chat with Command R

import cohere

co = cohere.Client("YOUR_COHERE_API_KEY")

response = co.chat(
    model="command-r-plus",
    message="Explain retrieval-augmented generation in plain English.",
)
print(response.text)

Embeddings for semantic search

import cohere

co = cohere.Client("YOUR_COHERE_API_KEY")

# Embed documents at indexing time
documents = [
    "Cohere specializes in enterprise NLP and semantic search.",
    "RAG combines retrieval with language model generation.",
    "Multilingual embeddings support over 100 languages.",
]
doc_embeddings = co.embed(
    texts=documents,
    model="embed-multilingual-v3.0",
    input_type="search_document",
).embeddings

# Embed a query at search time
query_embedding = co.embed(
    texts=["What does Cohere specialize in?"],
    model="embed-multilingual-v3.0",
    input_type="search_query",
).embeddings[0]

# Compute cosine similarity (or use a vector DB)
import numpy as np

doc_array = np.array(doc_embeddings)
query_array = np.array(query_embedding)
scores = doc_array @ query_array / (
    np.linalg.norm(doc_array, axis=1) * np.linalg.norm(query_array)
)
top_idx = int(np.argmax(scores))
print(f"Most relevant: '{documents[top_idx]}' (score: {scores[top_idx]:.4f})")

Reranking retrieved candidates

import cohere

co = cohere.Client("YOUR_COHERE_API_KEY")

query = "How does multilingual embedding work?"
candidates = [
    "Cohere Embed supports over 100 languages in a single model.",
    "Command R+ is optimized for RAG workflows with long context.",
    "Rerank re-scores retrieved documents with a cross-encoder.",
    "BM25 is a classic keyword-based retrieval algorithm.",
]

results = co.rerank(
    model="rerank-multilingual-v3.0",
    query=query,
    documents=candidates,
    top_n=3,
)

for hit in results.results:
    print(f"[{hit.relevance_score:.4f}] {candidates[hit.index]}")

Full RAG pipeline with Command R+ citations

import cohere

co = cohere.Client("YOUR_COHERE_API_KEY")

# Documents retrieved from your vector store (simplified)
retrieved_docs = [
    {"id": "doc1", "text": "Cohere Embed supports 100+ languages for multilingual search."},
    {"id": "doc2", "text": "Command R+ is designed for grounded generation with source citations."},
    {"id": "doc3", "text": "Rerank improves precision by re-scoring candidates with a cross-encoder."},
]

response = co.chat(
    model="command-r-plus",
    message="How does Cohere's pipeline improve search quality?",
    documents=retrieved_docs,
)

print(response.text)
print("\n--- Citations ---")
for citation in response.citations:
    print(f"  [{citation.start}:{citation.end}] → {[doc['id'] for doc in citation.documents]}")

Practical resources

• Cohere API documentation — Full reference for all Cohere APIs including Chat, Embed, and Rerank
• Cohere Embed documentation — Detailed guide on embedding models, input types, and multilingual support
• Cohere Rerank documentation — Guide to the Rerank API with examples and model selection advice
• Cohere RAG guide — End-to-end walkthrough of building a RAG pipeline with Command R
• MTEB Leaderboard — Independent benchmark comparing embedding models including Cohere Embed

See also

• Model providers
• RAG
• Embeddings
• Semantic search