Cohere 发布 Rerank 5，新增段落级 Context Focus 与校准相关性分数

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Home Editors Pick Agentic AICohere Releases Rerank 5 With Passage-Level Context Focus and Calibrated Scores

Cohere Releases Rerank 5 With Passage-Level Context Focus and Calibrated Scores

Michal Sutter

October 22, 2026

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Cohere AI has released Rerank 5, a new reranker family in Pro and Fast tiers. Cohere Rerank 5 is a retrieval model that reorders search results before they reach an LLM. It adds 2 developer features: Context Focus, which returns relevant passages inside each document, and calibrated relevance scores. For RAG and agent builders, the message is simple. Send fewer, better tokens to the LLM.

TL;DR

Size: Parameter count not disclosed. 32K token context window. Two tiers: rerank-v5.0-pro and rerank-v5.0-fast.
Runs on: Hosted API, Model Vault, Microsoft Foundry, Amazon SageMaker, or private VPC and on-prem. Cohere benchmarks speed on a single NVIDIA A100. No open weights or quantized builds.
Performance: Pro ranks first on 39 of 46 composite datasets in Cohere’s own evaluation.
Best: Pro scores 82.2 on BRIGHT (RCP-nDCG@10), 5.8 points above Voyage AI Rerank 3.
Worst: Pro median latency is 11.7 s per 96-document request, versus 4.1 s for Fast.

What is new in Cohere Rerank 5?

Rerank 5 ships as two models. Pro targets ranking quality on reasoning-heavy queries and long, domain-specific documents. Fast targets low latency and high throughput for agents running many searches.

Open SourceOmniParseBench · by Datalab · Apache 2.0 Benchmark any document parser on tables, math, forms and handwriting.Yes/no tests per page across text, table and layout families. Score vendor parsers on the Hugging Face dataset or your own PDFs with score and predict primitives.★ Star on GitHub Multi-column, rotated text, degraded scans · tagged by script and language github.com/datalab-to/omni_parse_bench Fork it · Add your parser

Both models support over 100 languages and a 32K context window. They also rerank semi-structured data such as JSON, tables and YAML records. Upgrading needs no re-indexing. Developers change the model parameter and keep their existing keyword, vector or hybrid search.

How does Context Focus work?

Context Focus is an optional feature, off by default. A single Rerank call returns document scores plus scored character spans. Each span carries start and end offsets into the original text. Developers set it through the relevant_spans field, with a threshold and max_spans value.

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Cohere tested it on 30 QA datasets. Removing half the retrieved text kept 96% of answer quality. Removing three quarters kept 87%. Cohere says teams can push to 10x less context at some quality cost.

In a search-agent test across 17 datasets, BM25 alone scored 46% accuracy. Adding Rerank 5 Pro raised it to 49%. Context Focus at the default 0.1 threshold cut tokens per task by 19% and reached 57%. At 0.4, tokens fell 34% and accuracy hit 58%. Versus BM25 alone, the full stack used 35% fewer tokens. On 6 deep-search benchmarks, tokens fell 41% and accuracy rose from 39% to 52%.

What do calibrated relevance scores change?

Rerank 5 scores read as probabilities. A score of 0.7 means a 70% chance the document is useful.

Cohere team measured expected calibration error (ECE) against blind human graders and NIST TREC-DL assessors. Rerank 5 scores about 0.06 on both. Voyage AI Rerank 3 scores 0.234 and 0.307. Rerank 4 Pro scores 0.384 and 0.309. In practice, dev teams can filter by threshold instead of a fixed top-k. An agent can also detect when nothing relevant came back.

How does Rerank 5 perform on benchmarks?

All scores below are self-reported by Cohere using RCP-nDCG@10, a Cohere-developed variant of nDCG. Cohere ran the competitor models itself.

Pro leads all four main benchmarks. It scores 92.2 on ViDoRe V3, 90.4 on BEIR, 82.2 on BRIGHT and 87.3 on nanoMTEB. BRIGHT shows the largest jump: 12.4 points over Rerank 4 Pro.

Fast beats Voyage AI Rerank 3 Lite, Qwen3-Reranker-8B and Rerank 4 Pro on all 4. It ties Voyage AI Rerank 3 on ViDoRe V3 at 90.6. It trails it on BEIR, 88.1 versus 88.6.

On parsed PDFs, averaged over 47 datasets, Pro scores 89.9 and Fast 88.5. Voyage AI Rerank 3 scores 88.4. Pro ranks first on 14 of 19 non-English languages in Cohere’s internal suite.

How does Rerank 5 compare with competing rerankers?

Feature	Cohere Rerank 5 Pro	Cohere Rerank 5 Fast	Voyage AI rerank-3	Voyage AI rerank-3-lite	Qwen3-Reranker-8B
Parameters	Not disclosed	Not disclosed	Not disclosed	Not disclosed	8B
Context window	32K	32K	32,000	32,000	32K
Languages	100+	100+	Not disclosed	Not disclosed	100+
License / access	Proprietary API, VPC, on-prem	Proprietary API, VPC, on-prem	Proprietary API	Proprietary API	Apache 2.0 open weights
API price per 1M tokens	$0.05	$0.02	$0.05	$0.02	Self-hosted
Passage extraction	Context Focus	Context Focus	Not disclosed	Not disclosed	Not disclosed
BRIGHT*	82.2	77.5	76.4	69.4	69.8
ViDoRe V3*	92.2	90.6	90.6	87.9	85.1
BEIR*	90.4	88.1	88.6	86.1	83.8
nanoMTEB*	87.3	85.1	83.9	80.7	82.1
ECE, human judges* (lower is better)	0.058	0.058	0.234	0.226	Not disclosed

*Scores are RCP-nDCG@10 or ECE from Cohere’s Rerank 5 post, measured by Cohere. Not independently verified.

How can developers deploy Rerank 5?

Rerank 5 is generally available on the Cohere API and Model Vault. It also runs on Microsoft Foundry (Pro, Fast) and Amazon SageMaker (Pro, Fast). It is also available to all North users. API pricing is $0.05 per 1M tokens for Pro and $0.02 for Fast. Model Vault lists $4.00 per instance-hour for both tiers on its Medium tier.

Integrations include LangChain, Haystack, Elasticsearch and Weaviate, per Cohere’s integration docs.

Key Takeaways

Rerank 5 ships in Pro ($0.05) and Fast ($0.02) per 1M tokens, both with 32K context.
Pro scores 82.2 on BRIGHT, 5.8 points above Voyage AI Rerank 3.
Context Focus returns scored passages in 1 call, cutting agent tokens 35% to 41%.
Calibrated scores reach about 0.06 ECE, so 1 threshold works across domains.
All benchmarks are self-reported on Cohere’s RCP-nDCG@10 metric.

Check out the official announcement, the Rerank 5 changelog and the full results sheet. All credit goes to the researcher of this project. Also,feel free to follow us on**Twitterand don’t forget to join our150k+ML SubRedditand Subscribe toour Newsletter. Wait! are you on telegram?now you can join us on telegram as well.**

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Michal Sutter

+ posts Bio ⮌

Michal Sutter is a data science professional with a Master of Science in Data Science from the University of Padova. With a solid foundation in statistical analysis, machine learning, and data engineering, Michal excels at transforming complex datasets into actionable insights.