Cohere Rerank V5 FastPricing
- Input Tokens: $0.02 /M tokens
- Output Tokens: $0.02 /M tokens
Input Modalities
- Text
Providers
Performance for cohere-rerank-v5-fast
Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).
Frequently asked questions
More models from Cohere
See all Cohere models →cohere-rerank-v5-pro is Cohere's enterprise search and retrieval-augmented generation (RAG) reranking model. It can reorder retrieval results based on query relevance, supports multiple languages, and provides stable relevance scoring to help filter out irrelevant content. Its Context Focus feature can also extract the most relevant passages from documents, reducing the language model's token consumption and response latency.
Developed by Cohere, north-mini-code-free is the debut model of the North family and Cohere's first agentic coding model. This sparse mixture-of-experts model features 30B total parameters and 3B active parameters, designed and optimized for high performance. With an expansive context length of 256,000 tokens, it is well-suited for handling complex developer workflows and large codebases.
Cohere's stronger command model for multilingual agents and enterprise workflows
Rerank 4 is the most advanced set of reranker models available today, purpose-built to meet the realities and challenges of enterprise AI search. It delivers best-in-class retrieval, outperforming the likes of MongoDB’s Voyage models and ElasticSearch’s Jina rerankers in overall search relevance, as well as improved latency, flexible deployment options, deep customizability and robust multilingual performance. Designed for business-critical applications across key industries and domains, Rerank 4 sets a new standard for accuracy and adaptability in enterprise search.
Rerank 4 is the most advanced set of reranker models available today, purpose-built to meet the realities and challenges of enterprise AI search. It delivers best-in-class retrieval, outperforming the likes of MongoDB’s Voyage models and ElasticSearch’s Jina rerankers in overall search relevance, as well as improved latency, flexible deployment options, deep customizability and robust multilingual performance. Designed for business-critical applications across key industries and domains, Rerank 4 sets a new standard for accuracy and adaptability in enterprise search.
- Input: $ 0.12 /M
- Output: $ 0 /M
- Input Image: $3.92/M tokens
Cohere’s Embed 4 is a multilingual multimodal embedding model. It is capable of transforming different modalities such as images, texts, and interleaved images and texts into a single vector representation. Embed 4 offers state-of-the-art performance across all modalities (texts, images, interleaved texts and image) and in both English and multilingual settings. Embed 4 supports a 128k context length and an images can have a maximum of 2MM pixels.
