What is Command R 08-2024?
Command R 08-2024 is a timestamped version of Cohere’s Command R language model, released on August 30, 2024. It is a text-generation model intended for production applications that need to work with supplied documents, answer questions using retrieved information, interact with external tools, or generate structured business content.
The model is especially relevant to retrieval-augmented generation, commonly called RAG. In a RAG system, an application finds relevant passages from a document collection and places them in the model’s context before asking for an answer. Command R 08-2024 can use that supplied material to produce grounded responses and citations instead of relying only on its internal training knowledge.
Cohere positions the model for conversational applications, document question answering, enterprise search, agent workflows, and multilingual business use. It is not presented as a frontier reasoning model, and it does not generate images, audio, or video.
Where it fits in Cohere’s current lineup
Command R 08-2024 belongs to Cohere’s Command family of generative models. Cohere’s current model catalog lists command-r-08-2024 as live, while recommending newer Command A models for most new use cases. That positioning makes Command R 08-2024 a compatibility-focused and cost-conscious option rather than Cohere’s newest general recommendation.
The model can still be a practical choice when an application is already built around Command R behavior, needs low token pricing, uses multilingual RAG, or depends on its established tool-use workflow. Teams starting a new project should compare it with the newer options in Cohere’s catalog, particularly if stronger reasoning or newer capabilities matter more than compatibility and cost.
Capabilities, context, and output limits
Command R 08-2024 has a verified context window of 128,000 tokens. The context window is the amount of text the model can consider in a request, including instructions, conversation history, retrieved passages, and other supplied content. This large limit is useful for document-heavy applications, although the effective amount of usable context will depend on the application’s prompt design and retrieval strategy.
The model can generate up to 4,000 output tokens in a response. That is sufficient for answers, summaries, extracted records, reports, and many conversational interactions, but it may be restrictive for applications that expect very long generated documents or extended reasoning traces.
According to the supplied model documentation, the model supports:
- Text input and text output
- Retrieval-augmented generation using supplied documents
- Document-grounded answers with citations
- Tool and function use
- Structured output generation
- Long-context conversations and document analysis
- Multilingual generation across 10 optimized languages
The August 2024 refresh improved instruction following, structured data analysis, multilingual RAG, length and formatting control, and the model’s ability to decide when tools should be used. Cohere also reported improvements in throughput and latency compared with the earlier Command R version. Those improvements are provider claims; actual speed depends on the endpoint, deployment, request size, concurrency, and infrastructure.
Supported modalities and output types
Command R 08-2024 is text-only. It accepts text and returns text. It does not natively accept image, audio, or video input, and it does not produce image, audio, video, music, or speech output.
This distinction matters for system design. An application can still combine the model with separate document-processing, OCR, speech, or vision components, but those capabilities are not native features of Command R 08-2024 itself. Its strength is language processing over text supplied directly or retrieved by the surrounding application.
Reasoning, coding, and tool use
Command R 08-2024 is designed for practical instruction following, grounded generation, extraction, and agent-style workflows. It can decide when to use supported tools and can incorporate returned tool information into a response. This makes it suitable for assistants that query business systems, retrieve records, or perform actions through application-defined functions.
Tool use should not be confused with built-in code execution. The supplied research confirms tool and function support, but does not identify a native code-execution environment. Developers therefore need to provide and secure the external tools themselves.
The model can generate code and structured data as part of its text output, making it useful for coding assistance, schema-based extraction, and automation logic. However, the supplied research does not provide a benchmark demonstrating frontier-level programming performance. Its coding suitability should therefore be evaluated against the requirements of the specific software task.
The research assigns a reasoning score of 6 out of 10 and a coding score of 6 out of 10. These are editorial or database evaluations, not scores published by Cohere, and should be treated as comparative guidance rather than verified benchmark results. The model’s primary advantage is its balance of grounding, context size, multilingual support, and cost—not maximum open-ended reasoning ability.
Pricing and availability
Cohere lists Command R 08-2024 at $0.15 per million input tokens and $0.60 per million output tokens. Input tokens are the text sent to the model, while output tokens are the text it generates. A request that supplies extensive retrieved documents can therefore incur more input-token usage even when the final answer is short.
The model is available through Cohere’s API and is documented for selected cloud and enterprise deployment options. Cohere’s broader deployment model also includes private and enterprise environments, but the supplied research does not establish that every deployment option offers identical pricing or operational behavior. Teams should confirm endpoint-specific terms before committing to a production architecture.
The model’s editorial database scores its speed at 8 out of 10 and cost at 9 out of 10. These are editorial assessments, not provider-published ratings. They reflect the model’s intended trade-off: relatively economical, production-oriented text generation with useful context and tool capabilities, rather than maximum reasoning depth.
Best use cases
Command R 08-2024 is a strong candidate for applications where the answer should be based on business documents or retrieved records. Practical examples include:
- Enterprise question answering: answering employee or customer questions using policies, manuals, contracts, or product documentation.
- Multilingual search assistants: creating conversational interfaces over information that users may access in supported languages.
- RAG pipelines: combining a search or retrieval system with grounded natural-language answers and citations.
- Document processing: extracting fields, classifying content, summarizing records, or converting unstructured text into a defined schema.
- Tool-using agents: allowing a model-driven assistant to call approved business functions, retrieve data, or coordinate workflow steps.
- Long-context analysis: reviewing large supplied documents or extended conversations without immediately splitting all material into small requests.
Its combination of a 128,000-token context window and low token prices can be particularly useful when a workload repeatedly processes large amounts of text. Applications should still use retrieval carefully: sending a large document collection on every request may increase cost and noise, even when the context limit allows it.
Limitations and trade-offs
The most important limitation is that Command R 08-2024 is text-only. It is not the appropriate primary model for direct image understanding, audio transcription, video analysis, or image and video generation. Those workflows require other models or preprocessing services.
Its maximum output is 4,000 tokens, which may be insufficient for unusually long reports or applications that need extensive generated content in one response. The model also has a knowledge cutoff of June 1, 2024. It should not be expected to know later events or changing business information unless current information is supplied through retrieval, documents, or tools.
Structured output support is documented, but a separate legacy JSON-mode capability is not verified in the supplied research. Developers should test the exact response-format behavior of the Cohere endpoint and model configuration they plan to use rather than assuming that structured outputs and JSON mode are interchangeable.
Finally, the model is not the best fit when the central requirement is frontier-level reasoning, the strongest possible coding performance, or a native multimodal interface. A newer Command A model may be more appropriate for many new Cohere projects, while a specialized vision, audio, or reasoning model may be preferable for those specific tasks.
When to choose this model
Choose Command R 08-2024 when you need an economical enterprise text model with a large context window, multilingual RAG, citations, structured generation, and tool use. It is especially sensible when compatibility with the Command R family matters or when the workload values throughput and cost control more than the newest reasoning capabilities.
Choose another option when you need native image or audio processing, very long output, current knowledge without external grounding, or the strongest available reasoning and coding performance. For a new Cohere deployment, compare the model with the provider’s newer Command A models. For any choice, validate quality with representative documents, languages, tool calls, and failure cases from the intended production workload.