# Cohere’s Rerank 3.5 release changed both retrieval ranking and the API contract Cohere released Rerank v3.5 and a v2 Rerank API on December 2, 2024, adding a 4,096-token context limit and explicit model selection. Canonical: https://rangoon.ai/insights/archive-cohere-rerank-3-5-december-2024/ Author: Rangoon Editorial (https://rangoon.ai/insights/editorial/) Published: 2026-10-23 Updated: 2026-10-23 Event date: 2024-12-02 Tags: [Cohere](https://rangoon.ai/insights/tags/cohere/), [Rerank 3.5](https://rangoon.ai/insights/tags/rerank-3-5/), [retrieval](https://rangoon.ai/insights/tags/retrieval/), [search ranking](https://rangoon.ai/insights/tags/search-ranking/) Source note: Historical reporting: Cohere released Rerank v3.5 and a v2 Rerank API on December 2, 2024. Its release notes state a 4,096-token context length, required model selection, and replacement of max_chunks_per_doc with max_tokens_per_doc, defaulting to 4096. A maintained models overview is later context only. Cohere’s performance positioning is not treated as an independent relevance measurement. Image: Two-stage retrieval and reranking contract. Original Rangoon explanatory diagram: Source boundary: dated primary release reporting; original editorial analysis. No vendor interface, photograph, measurement result, or endorsement implied. ## Key takeaways - Rerank v3.5 scored a query against candidate documents after retrieval, with a stated 4,096-token context limit. - The v2 API made the model explicit and replaced a chunk-count control with a token-per-document control. - Reranking can improve ordering only within the candidate set a first-stage retriever supplied. ## The December event paired a model release with an interface change [source 1](https://docs.cohere.com/changelog/rerank-v3.5) Cohere announced its new Rerank model and v2 Rerank API on December 2, 2024. The release notes gave the model a 4,096-token context length and said that the v2 endpoint requires a model parameter. They also replaced max_chunks_per_doc with max_tokens_per_doc, whose default was 4096. This was more than a new name in a model catalog. An explicit model field makes model selection part of a caller’s contract, while a token-based document limit describes the input boundary differently from a count of chunks created elsewhere in an application. That change makes truncation policy visible at the integration boundary. ## Reranking acts after candidate retrieval, not before it [source 1](https://docs.cohere.com/changelog/rerank-v3.5) [source 2](https://docs.cohere.com/v1/docs/models) A typical retrieval path first gathers candidate passages using a lexical, vector, or hybrid index, then asks a reranker to order that smaller set against the query. The reranker can make a more detailed comparison than a broad index lookup, but it cannot return a relevant document that the first stage never supplied. Candidate recall and reranking precision are therefore linked but different things to evaluate. Cohere’s maintained overview later lists Rerank v3.5 as a model option and records its 4K context. That current catalog is useful context, not proof that a specific December integration used the same endpoint, candidate limit, or operating policy. ## Token limits make document segmentation part of ranking behavior [source 1](https://docs.cohere.com/changelog/rerank-v3.5) When a candidate is longer than the model’s effective input budget, an application must decide what portion reaches the ranker. Segmenting earlier can preserve several local passages, while sending a broad document can retain global context but consume more of the 4,096-token allowance. The v2 move to max_tokens_per_doc exposes that choice at the request boundary. An application migrating an old chunk-count setting cannot reuse the same integer as a token limit without changing how much of each document is examined. Neither a token limit nor a YAML-formatted semi-structured document guarantees the right ordering. A useful evaluation fixes the candidate generator, corpus version, query set, relevance labels, truncation rule, and ranking metric so a changed model or API parameter has a visible effect. ## A reranker release is not an end-to-end search result [source 1](https://docs.cohere.com/changelog/rerank-v3.5) [source 2](https://docs.cohere.com/v1/docs/models) Cohere’s release records a dated model and API evolution. It does not disclose a customer’s corpus, access rules, first-stage retriever, latency target, fallback behavior, or relevance labels. Those surrounding choices can outweigh a model change for a real search product. The narrow historical conclusion is that Cohere introduced Rerank version 3.5 and the stated v2 API changes on December 2. It does not prove a universal relevance gain, a lower cost, or a compliant retrieval workflow for another organization. ## Sources 1. [Cohere release notes: Rerank v3.5](https://docs.cohere.com/changelog/rerank-v3.5) 2. [Cohere documentation: An Overview of Cohere’s Models](https://docs.cohere.com/v1/docs/models) ## Related reading - [Voyage’s 3.5 embedding release put dimensions and quantization beside retrieval scores](https://rangoon.ai/insights/archive-voyage-3-5-embeddings-may-2025/) - [Google made Gemini Embedding stable while keeping vector size a retrieval choice](https://rangoon.ai/insights/archive-google-gemini-embedding-general-availability-july-2025/) - [Cohere released Rerank 4 with Pro and Fast variants for multilingual and JSON document ranking](https://rangoon.ai/insights/archive-cohere-rerank-4-december-2025/) ## More information - [Documentation index](https://rangoon.ai/llms.txt) - [AI access and policies](https://rangoon.ai/ai/) - [Source repository](https://github.com/hypler-dev/rangoon)