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Command R+

Cohere · 2024-04

Cohere's enterprise RAG-optimized model designed for scalable retrieval-augmented generation.

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Quick Facts

Parameters

Undisclosed (estimated ~100B)

Context Window

128K tokens

Modalities

text

Open Source

No

Pricing

API from $2.50/1M input tokens

Released

2024-04

Developer

Cohere

About

Command R+ is Cohere's flagship enterprise model, specifically optimized for retrieval-augmented generation (RAG) workflows — making it the best model available for applications that need to ground AI responses in external knowledge sources. Unlike general-purpose models optimized for conversational ability or creative writing, Command R+ is engineered from the ground up for enterprise use cases where accuracy, citations, and grounding in verified information are paramount. The model features a 128K token context window and supports multilingual capabilities across 10+ languages. What makes Command R+ unique is its architecture optimized for RAG: it excels at incorporating retrieved information into its responses, generating precise citations automatically, and handling tool use for complex multi-step retrieval workflows. This makes it ideal for enterprise search applications, knowledge management systems, document Q&A, customer support with retrieval augmentation, and any scenario where answers must be grounded in specific documents or databases. Cohere also provides a dedicated RAG evaluation framework and tools for optimizing retrieval pipelines. Available through Cohere's platform at USD 2.50 per 1M input tokens, with enterprise options for dedicated deployment. For enterprises building knowledge management systems, customer support platforms that need accurate citation of source materials, and any organization where AI accuracy is critical and hallucination cannot be tolerated, Command R+ offers specialized capabilities that general-purpose models don't match. Compared to GPT-4o or Claude for RAG tasks, Command R+ consistently produces more accurate citations and better-grounded responses. The main limitations are less capability for creative and general conversation compared to general-purpose models, and the need for RAG infrastructure to achieve best results.

Strengths

  • +Best-in-class RAG performance for enterprise use
  • +Automatic citation generation for grounded responses
  • +Strong multilingual retrieval across 10+ languages
  • +Enterprise-grade security and compliance features

Weaknesses

  • Not available as open-weight model
  • Less capable for creative and general conversation
  • Requires RAG infrastructure for best results

Best For

Enterprise search and knowledge management

Document Q&A with citation grounding

Customer support with retrieval augmentation

Compliance-regulated AI deployments

Pricing

API (Cohere Platform)

From $2.50/1M input tokens

  • RAG-optimized
  • 128K context
  • Citations
  • Tool use
  • Multilingual

Enterprise

Custom pricing

  • Dedicated deployment
  • SLA
  • Compliance
  • Custom fine-tuning

Benchmarks

BenchmarkCommand R+Competitor
RAG (HHEM)92.3%GPT-4: 88.1%

Technical Specs

Parameters

Undisclosed (estimated ~100B)

Context Window

128K tokens

Modalities

text

Languages

EnglishFrenchGermanSpanishItalian+5

Open Source

No

Developer

Cohere

Released: 2024-04

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