Retrieval-Augmented Generation
Grounds model responses in external knowledge sources by retrieving and citing relevant documents, reducing hallucinations in enterprise workflows.
Cohere
Command R
Command R is an instruction-following conversational model developed by Cohere, designed for enterprise language tasks with a focus on reliability and scalability. It is available through Amazon Bedrock and carries a knowledge cutoff of March 2024. The model is purpose-built for retrieval-augmented generation (RAG) and tool use, making it well-suited for workflows that require grounding responses in external data sources or integrating with external APIs and functions. One of Command R's defining characteristics is its 128,000-token context window, which allows it to process long documents, extended multi-turn conversations, and complex inputs in a single pass. It also supports multilingual tasks and is tagged for low-latency performance, making it a practical choice for organizations building scalable AI applications where response speed and contextual accuracy matter. It is best suited for enterprise use cases such as document analysis, agentic pipelines, and knowledge-grounded question answering.
Aug 30, 2024
128,000 context
4,000 tokens output
Retrieval-Augmented Generation
Tool Use & Function Calling
Long Context Understanding
Multilingual Support
Low Latency Responses
Instruction Following
Model Overview
High-signal model metadata in a structured two-column overview table.
Provider
The entity that provides this model.
Cohere
Model ID
The routed model identifier exposed by upstream providers.
cohere/command-r-08-2024
Input Context Window
The number of tokens supported by the input context window.
128,000
tokens
Maximum Output Tokens
The number of tokens that can be generated by the model in a single request.
4,000 tokens
tokens
Open Source
Whether the model's code is available for public use.
No
Release Date
When the model was first released.
Aug 30, 2024
1 year ago
Knowledge Cut-off Date
When the model's knowledge was last updated.
2024-03-31
API Providers
The providers that offer this model. This is not an exhaustive list.
Cohere
Modalities
Types of data this model can process.
Text
What is Command R
A fuller summary of positioning, capabilities, and source-specific details for Command R.
Command R is an instruction-following conversational model developed by Cohere, designed for enterprise language tasks with a focus on reliability and scalability. It is available through Amazon Bedrock and carries a knowledge cutoff of March 2024. The model is purpose-built for retrieval-augmented generation (RAG) and tool use, making it well-suited for workflows that require grounding responses in external data sources or integrating with external APIs and functions.
One of Command R's defining characteristics is its 128,000-token context window, which allows it to process long documents, extended multi-turn conversations, and complex inputs in a single pass. It also supports multilingual tasks and is tagged for low-latency performance, making it a practical choice for organizations building scalable AI applications where response speed and contextual accuracy matter. It is best suited for enterprise use cases such as document analysis, agentic pipelines, and knowledge-grounded question answering.
Capabilities
What Command R supports
Tool Use & Function Calling
Enables agentic workflows by allowing the model to call external tools and APIs, supporting multi-step task execution.
Long Context Understanding
Processes up to 128,000 tokens in a single pass, enabling analysis of long documents and extended multi-turn conversations.
Multilingual Support
Handles tasks across multiple languages, making it suitable for globally distributed enterprise applications.
Low Latency Responses
Optimized for speed in production environments, supporting real-time or near-real-time application requirements.
Instruction Following
Follows detailed natural language instructions reliably, supporting structured enterprise use cases such as summarization, classification, and Q&A.
Pricing for Command R
Primary API pricing shown in the same “quick compare” spirit as the reference page.
Input tokens
$0.50
Per million tokens
Output tokens
$0.60
Per million tokens
Price Comparison
Additional usage-cost dimensions synced into the project for this model.
maxTemperature
1
maxResponseSize
4,000 tokens
API Access & Providers
Places where this model is available, based on the synced detail-page metadata.
Cohere
Provider Endpoints
Endpoint-level provider data currently available for this model.
Cohere
Model Performance
Benchmark scores synced from the current model source and normalized into the local catalog.
| Benchmark | Score |
|---|---|
|
AIME 2024
American math olympiad problems
|
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GPQA Diamond
PhD-level science questions (biology, physics, chemistry)
|
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HLE
Questions that challenge frontier models across many domains
|
|
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LiveCodeBench
Real-world coding tasks from recent competitions
|
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MATH-500
Undergraduate and competition-level math problems
|
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MMLU-Pro
Expert knowledge across 14 academic disciplines
|
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SciCode
Scientific research coding and numerical methods
|
Resources & Documentation
Official model cards, release notes, docs, and other references synced from the source page.
Community discussion
What people think about Command R
Command R discussions are most active in r/LocalLLaMA. The strongest match in this snapshot has 479 upvotes and 213 comments.
What's new in 1.5:
* Up to 50% higher throughput and 25% lower latency
* Cut hardware requirements in half for Command R 1.5
* Enhanced multilingual capabilities with improved retrieval-augmented generation
* Better tool selection and usage
* Increased strengths in data analysis and creation
* More robustness to non-semantic prompt changes
* Declines to answer unsolvable questions
* Introducing configurable Safety Modes for nuanced content filtering
* Command R+ 1.5 priced at $2.50/M input tokens, $10/M output tokens
* Command R 1.5 priced at $0.15/M input tokens, $0.60/M output tokens
Blog link: [https://docs.cohere.com/changelog/command-gets-refreshed](https://docs.cohere.com/changelog/command-gets-refreshed)
Huggingface links:
Command R: [https://huggingface.co/CohereForAI/c4ai-command-r-08-2024](https://huggingface.co/CohereForAI/c4ai-command-r-08-2024)
Command R+: [https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024](https://huggingface.co/CohereForAI/c4ai-command-r-plus-08-2024)
FAQ
Common questions about Command R
What is the context window size for Command R?
Command R supports a context window of 128,000 tokens, allowing it to process long documents and extended conversations in a single pass.
What is the knowledge cutoff date for Command R?
Command R has a training knowledge cutoff of March 2024, as noted in the model metadata.
How is Command R priced on Amazon Bedrock?
Command R is available through Amazon Bedrock, and pricing is determined by AWS. You can find current pricing details on the Amazon Bedrock Pricing page.
What is Command R best suited for?
Command R is purpose-built for retrieval-augmented generation (RAG) and tool use, making it well-suited for enterprise workflows that require grounding responses in external knowledge sources or building agentic pipelines.
Does Command R support multiple languages?
Yes, Command R is tagged as multilingual and is designed to handle tasks across multiple languages, supporting globally distributed enterprise applications.
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