LLM📅 Released: 2024-04-04

Command R+

Command R+ is Cohere's flagship model optimized for RAG and enterprise tool use.

Overview

Command R+ is Cohere's frontier-class model specifically optimized for Retrieval-Augmented Generation (RAG) and enterprise tool-use. It is designed to bridge the gap between high-end reasoning and efficient, scalable deployment. With a focus on accuracy and reliability in business workflows, it handles multi-step tool use and complex citations across massive datasets natively.

Unique Factor

Industry-leading RAG performance and citation accuracy, ensuring that every claim is grounded in provided data.

Key Capabilities

●

RAG optimized

●

Multilingual

●

Tool use

Benchmarks

MMLU Score

81.2%

HumanEval (Coding)

75%

GPQA Diamond

65%

MATH Benchmark

70.5%

Top Use Cases

Enterprise Knowledge Search

Building a company-wide assistant that answers questions based on internal wikis and PDFs.

Example: “Based on our Q3 security reports and the new compliance PDF, what are the top 3 risks we need to address?”

Automated Business Workflows

Orchestrating tools to handle customer returns, scheduling, and billing updates.

Example: “Check the customer's order status in the database, then send an email explaining the delay and offering a discount.”

Detailed Features

RAG-First Architecture: Native optimization for scanning and synthesizing information from large external document stores.

Advanced Tool-Use: Reliable execution of multi-step API workflows and database interactions.

Multi-Step Reasoning: Can plan and execute complex tasks involving multiple distinct actions.

128K Context Window: Support for long-form document processing and repository-wide context.

Enterprise Data Privacy: Designed with Cohere's commitment to data residency and security.

High Multilingual Accuracy: Superior performance in 10+ business-critical languages.

✓ Strengths & Pros

• Best-in-class RAG accuracy
• Reliable multi-step tool use
• Permissive commercial license (CC-BY-NC)

✕ Limitations & Cons

• Reasoning on abstract math is slightly below GPT-4o
• Context window is smaller than Gemini's

Ideal Usage & Target Audience

Best For

Enterprise developers, businesses needing reliable RAG, and teams focusing on tool-use automation.

Not Recommended For

Users requiring pure creative storytelling or extreme logic benchmarks (use o3/Opus).

API Implementation

python

import cohere
co = cohere.Client('YOUR_API_KEY')
response = co.chat(
  model='command-r-plus',
  message='Summarize these 5 documents.',
  documents=[{'title': 'Doc 1', 'snippet': '...'}]
)
print(response.text)

Check the official documentation for full SDK details.