# Cohere Rerank 5: Pricing, Context Focus and API Changes

Cohere's new reranker can select passages inside documents, but the useful test is how much evidence survives the smaller prompt.

By Paras Madan | 2026-10-21


Cohere released Rerank 5 on October 21, 2026, with Pro and Fast models and an optional Context Focus feature that identifies relevant passages inside documents. For retrieval-augmented generation, that creates another decision between finding a document and sending its contents to a language model.

A reranker takes a question and a set of candidate documents, then sorts those documents by relevance. It does not replace the search index or generate the final answer. The interesting change is that it can now help decide which parts of a retrieved document deserve space in the answer model's prompt.

## What changed in Rerank 5

The [release notes](https://docs.cohere.com/v2/changelog/rerank-v5) introduce rerank-v5.0-pro and rerank-v5.0-fast. Context Focus is optional and disabled by default. When enabled, results can include scored spans with character offsets inside each document.

Existing applications can change the model parameter without rebuilding their embeddings or reindexing their documents. That makes a model comparison relatively straightforward, but it should be separate from a Context Focus experiment. Otherwise, a change in answer quality could come from the new ranking, the reduced context, or both.

**Better document ranking and better passage selection are different tests.** A document can be relevant because it contains the answer, while a selected passage can still omit the qualification that makes the answer correct.

## Rerank 5 pricing and context window

Cohere's [launch announcement](https://cohere.com/blog/rerank-5) lists these prices for its API:

| Model | Price per million tokens | Context window |
| --- | --- | --- |
| Rerank 5 Pro | $0.05 | 32K tokens |
| Rerank 5 Fast | $0.02 | 32K tokens |

The [model catalog](https://docs.cohere.com/docs/models) documents the 32K context window. Both models support multilingual reranking and structured text, including records represented as YAML.

These are reranking charges, not the full cost of a retrieval pipeline. Embedding, search infrastructure and answer generation remain separate. A lower reranker bill can be outweighed by sending too many documents to an expensive generator, while aggressive passage filtering can save tokens at the cost of missing evidence.

For a useful cost comparison, keep the candidate documents and questions fixed. Measure the tokens billed for reranking and the resulting generator input separately, then compare cost per satisfactory answer rather than cost per API call.

## How Context Focus works in the API

The [v2 Rerank reference](https://docs.cohere.com/v2/reference/rerank) documents the relevant_spans request field. Enable its mode and configure a score threshold and maximum number of spans. The response associates spans with the ranked document and supplies start and end offsets.

That gives an application a way to extract a smaller evidence bundle while retaining the original document. Keep those offsets and source identifiers through the rest of the pipeline so the final answer can still be traced to its evidence.

Consider a support document that explains a refund rule and then lists exceptions. Extracting the general rule alone may produce a confident but incomplete answer. An application might include neighboring text, merge nearby spans, or fall back to the full document when the evidence is fragmented. Those are implementation choices to test, not benefits guaranteed by enabling the feature.

## What to test before switching

Cohere reports stronger retrieval results across its benchmark suite. Those are vendor evaluations, not proof that the same improvement will appear on a particular company's documents.

Start with questions where the existing system retrieves the right document but answers incorrectly. Include exceptions, tables and questions requiring evidence from more than one section. Compare the existing reranker, Rerank 5 without Context Focus, and Rerank 5 with passage selection.

Track whether the necessary evidence survives, whether citations point to the right material, and whether the answer model declines questions the documents cannot resolve. Measure latency under realistic document counts as well.

The useful outcome is a smaller prompt that still contains enough evidence to answer correctly. If a passage selector achieves that reliably, it changes how much retrieval context an application can afford to carry through every interaction.

I'm Paras. I work at the intersection of AI, engineering and distribution.

I help technical founders turn launches and complex product updates into clear stories that developers and founders want to read and share.

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