Where is the Rerank 4 Fast documentation?

The Rerank 4 Fast documentation on OpenRouter includes the Reranking in RAG: How to Fix Bad Retrieval with a Reranker blog post. The quickstart covers API keys and your first request, and the API reference lists every parameter.

How much does Rerank 4 Fast cost?

Rerank 4 Fast costs $0.002/search.

How much text can Rerank 4 Fast process at once?

Rerank 4 Fast accepts up to 32,768 tokens per query and document. Longer text has to be split into multiple documents.

What inputs and outputs does Rerank 4 Fast support?

Rerank 4 Fast accepts text as input and returns relevance-ranked documents.

Which providers serve Rerank 4 Fast?

Rerank 4 Fast is served by 1 provider on OpenRouter: Cohere.

What other reranking models does Cohere have?

Rerank 5 Fast, Rerank 5 Pro, Rerank 4 Pro and 1 more are other reranking models from Cohere.

When was Rerank 4 Fast released?

Rerank 4 Fast was released on April 6, 2026.

Cohere: Rerank 4 Fast

cohere/rerank-4-fast

Cohere's AI search foundation model for enhancing the relevance of information surfaced within search and RAG systems. Features a 32K context window, multilingual support across 100+ languages, no data pre-processing required, and high performance with lowest latency.

Modalities

Price

$0.002/search

Context

33K

Released

Apr 6, 2026

Providers

This model is hosted by one provider. OpenRouter forwards every request without routing.

Latency


	$0.002	0.31s	100.00%

Search $0.002
0.31s latency100.00% uptime

Pricing

The average price customers actually pay for this model, next to the prices providers post. Caching and discounts mean the price actually paid is often well below the listed one.

Weighted average

Input

$0/M tokens

Output

$0/M tokens

Rerank 4 Fast — Price history

Price history

No effective price data for this selection

		$0.00	$0.00	0.0%	—

Effective in $0.00Effective out $0.00
Token share —

Uptime

Uptime is the percentage of the past 3 days that at least one provider was responding to requests. Availability is the percentage of time that inference was successfully served. OpenRouter continuously monitors and uses the next-best provider when one returns an error.

Uptime (3d)

100.00%

Availability (3d)

99.99%

Availability over the last 3 days

Last 72 hours

Availability 99.99%

3 Days Ago2 Days AgoYesterdayNow

Availability over the last 24 hours

OpenRouter Availability

99.99%

When an error occurs in an upstream provider, we can recover by routing to another healthy provider, if your request filters allow it. You can access per-provider uptime data programmatically through the Endpoints API. Learn more about our load balancing and provider routing options.

Not currently available through EU in-region routing. In-region routing docs

More models from Cohere

Embed 5 Fast

Embed 5 Fast is the lower-latency, cost-efficient tier of Cohere's Embed 5 family of multimodal embedding models (about 1B parameters). It embeds text, images, and interleaved text and image content into a single vector space and is suited for high-volume query embedding, agent loops, and multilingual semantic search across 100+ languages.

It shares its embedding space with Embed 5 Pro, so documents indexed with Pro can be queried with Fast. Output dimensions are selectable from 256 to 2048, and input_type defaults to search_document when not set.

Embeddings$0.08/M tokens

Embed 5 Pro

Embed 5 Pro is the higher-accuracy tier of Cohere's Embed 5 family of multimodal embedding models (about 2.4B parameters). It embeds text, images, and interleaved text and image content into a single vector space and is suited for retrieval and RAG over long or domain-specific documents, visual and document search, and multilingual semantic search across 100+ languages.

It shares its embedding space with Embed 5 Fast, so a common pattern is to index a corpus with Pro and embed queries with Fast. Output dimensions are selectable from 256 to 2048, and input_type defaults to search_document when not set.

Embeddings$0.12/M tokens

Rerank 5 Fast

Rerank 5 Fast is the low-latency tier of Cohere's Rerank 5 family of reranking models. It scores and reorders candidate documents against a query and is suited for high-throughput retrieval such as high-volume search, agent tool loops, and first-pass filtering before a more expensive step.

Relevance scores are calibrated to a 0-1 range so a fixed threshold behaves consistently across queries. Rerank 5 Pro is the higher-accuracy sibling for complex queries and long or domain-specific documents.

Rerank$0.02/M tokens

Rerank 5 Pro

Rerank 5 Pro is the higher-accuracy tier of Cohere's Rerank 5 family of reranking models. It scores and reorders candidate documents against a query and is suited for retrieval pipelines where ranking quality matters most: complex or reasoning-heavy queries, long or domain-specific documents, and RAG systems that feed a small number of passages to a generator.

Relevance scores are calibrated to a 0-1 range so a fixed threshold behaves consistently across queries. Rerank 5 Fast is the lower-latency sibling for high-volume search and agent loops.

Rerank$0.05/M tokens

Command A+

Command A+ is Cohere's flagship model for enterprise agentic workflows. It accepts text and image inputs with a 192K context window, supports native tool calling with strict tool schemas, structured outputs (JSON object and JSON schema), optional reasoning, and configurable safety modes.

Text192K context$0.30 / $1.50

North Mini Code (free)

North Mini Code is Cohere's first agentic coding model and the debut of its North family. A sparse mixture-of-experts model with 30B total parameters and 3B active, it is optimized for code generation, agentic software engineering, and terminal tasks, and is trained to generalize across agent harnesses such as OpenCode and SWE-Agent.

It offers a 256K-token context window with up to 64K tokens of output, supports interleaved reasoning and tool use via JSON schema, and is released open-weight under the Apache 2.0 license. Its small active-parameter footprint enables low-latency inference, including on local hardware.

Rerank v3.5 is designed to reorder search results for improved relevance. It supports multi-aspect and semi-structured data reranking over 100+ languages. Ideal for refining results from semantic or keyword search pipelines.

Rerank$0.002/search

Command A

Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding use cases. Compared to other leading proprietary and open-weights models Command A delivers maximum performance with minimum hardware costs, excelling on business-critical agentic and multilingual tasks.

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Command R7B

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