Cohere Rerank 4

Hosted API · Cohere · Updated 1 Oct 2026

Cohere Rerank was one of the first commercial reranking APIs and remains among the most widely deployed. The current generation, Rerank 4 (released April 2026), ships as two variants — rerank-v4.0-pro for precision and rerank-v4.0-fast for throughput — both with a 32,768-token context window and 100+ languages, behind a clean REST + SDK interface.

Available models

Use rerank-v4.0-pro by default. Switch to rerank-v4.0-fast when throughput or tail latency matters more than the last point of precision — the two are priced close enough ($0.0025 vs $0.002 a search) that quality, not cost, should drive the choice.

Pricing

Prices from cohere.com/pricing ($2.50 and $2 per 1,000 searches) and Cohere’s rate-limit docs, verified 1 October 2026 and re-checked daily by this site’s source check. The 500-token rule matters: see the cost calculator for what it does to a real workload.

Quick start

Python

pip install cohere

import cohere

co = cohere.ClientV2("YOUR_API_KEY")

query = "How do I add reranking to my RAG pipeline?"
documents = [
    "Rerankers score each query-passage pair jointly with a cross-encoder.",
    "BM25 is a classical keyword-based retrieval method.",
    "London is the capital of the United Kingdom.",
    "Two-stage retrieval: retrieve 50 candidates, rerank to top 5.",
]

results = co.rerank(
    model="rerank-v4.0-pro",
    query=query,
    documents=documents,
    top_n=3,
)

for r in results.results:
    print(f"{r.relevance_score:.4f}  {documents[r.index][:80]}")

Node.js

import Cohere from "cohere-ai";
const co = new Cohere.Client({ token: "YOUR_API_KEY" });

const result = await co.rerank({
  model: "rerank-v4.0-pro",
  query: "How do I add reranking to my RAG pipeline?",
  documents: ["...", "..."],
  topN: 3,
});

result.results.forEach((r) => console.log(r.relevanceScore, documents[r.index]));

REST (curl)

curl https://api.cohere.com/v2/rerank \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "rerank-v4.0-pro",
    "query": "How do I add reranking to my RAG pipeline?",
    "documents": ["Rerankers score query-passage pairs...", "BM25 is..."],
    "top_n": 3
  }'

Pros and cons

Other models