Command R+ 08-2024

What is Command R+ 08-2024?

Command R+ 08-2024 is a large language model provided by Cohere. The model is intended primarily for enterprise conversational applications, retrieval-augmented generation (RAG), long-context analysis, structured data work, and agentic workflows that require several tool calls.

RAG means giving the model relevant documents or retrieved passages at the time of a request so that it can base its response on that supplied material. Command R+ 08-2024 can use documents for grounded answers and citations, but document retrieval does not extend the model’s underlying training knowledge. The canonical Cohere model identifier is command-r-plus-08-2024.

The “08-2024” designation identifies this as the August 2024 refresh of the Command R+ family. Cohere’s current model catalog lists it as live, although the provider recommends newer Command A models for most new use cases. That makes Command R+ 08-2024 most relevant to teams evaluating an established enterprise model, maintaining an existing integration, or requiring its documented combination of long context, RAG, structured output, and tool use.

Key specifications

The 128K context window is useful for processing lengthy document collections, reports, transcripts, or conversation history in a single request, subject to the application’s own prompt construction and token budget. The 4,000-token output limit is more restrictive than the context limit: the model may read a large amount of material, but a single generated response cannot exceed the documented maximum.

Capabilities for enterprise workflows

RAG and citations

Command R+ 08-2024 is designed for complex RAG workloads. An application can supply documents or retrieved passages, ask a question, and request an answer grounded in those materials. When used with supplied documents, the model supports citations, making it more suitable for enterprise search, research assistants, document question-answering, and internal knowledge systems than a model that can only produce an ungrounded response.

Grounding still depends on the quality and relevance of the material provided by the application. The model does not independently browse the web according to the supplied research, and its knowledge cutoff remains June 1, 2024 in the current specification. An organization therefore needs its own retrieval process when answers must reflect newer or private information.

Tool use and agentic workflows

The model supports tool use, which allows an application to describe functions or external operations that the model can select during a task. Examples include looking up information in a business database, retrieving documents, calling an internal service, or performing a multi-step workflow. The model produces text instructions or structured tool calls; the application is responsible for executing the tools and returning their results.

Cohere’s August 2024 update specifically improved tool-selection decisions. This is useful when a task requires more than one operation, such as identifying the right data source, retrieving relevant records, and then composing a response. Tool use does not mean that Command R+ 08-2024 directly controls an organization’s systems without integration and authorization logic around the model.

Structured output and data tasks

Command R+ 08-2024 supports structured outputs through the response_format feature. This can help applications request machine-readable responses for tasks such as extracting fields from documents, classifying records, or returning a predictable object for downstream software.

Structured output is still text-based output formatted according to a requested structure. It does not make the model an image, audio, or video generator. The refresh also improved structured-data analysis and following system-message instructions, according to Cohere’s model documentation.

Language and conversational use

The model supports multilingual generation and is intended for conversational applications. Its combination of long context, document grounding, citations, and tool use makes it a candidate for enterprise assistants that answer questions across internal material rather than only handling short, isolated prompts.

Modalities and technical limits

Command R+ 08-2024 is text-only. It accepts text input and produces text output. It does not natively accept images, audio, or video, and it does not generate images, audio, or video. An application can potentially place extracted text from another system into a prompt, but that does not give the model native multimodal understanding.

The context window is 128,000 tokens, while the maximum generated output is 4,000 tokens. These limits serve different purposes: the former concerns the amount of conversational or document material supplied to the model, and the latter limits the length of its response. A long report can therefore fit in the input context while the requested answer still needs to be summarized or divided into multiple steps.

The current model page lists June 1, 2024 as the knowledge cutoff. Older Cohere documentation reportedly describes the refreshed models as trained with data through February 2023. Because these statements may use different cutoff definitions or reflect documentation revisions, the current model specification is the value used here. Applications requiring current facts should use supplied documents or external retrieval rather than relying on model memory.

Pricing and access

Cohere’s documentation lists Command R+ 08-2024 at $2.50 per 1 million input tokens and $10 per 1 million output tokens. This is usage-based API pricing rather than a monthly consumer subscription. Input and output tokens are priced separately, so a workload that sends large documents repeatedly may incur substantial input costs even when its generated answers are short.

The model is available through Cohere’s Chat API. The supplied research also documents deployment options through platforms including Azure AI Foundry, Amazon-related deployment options, and Oracle Cloud Infrastructure. Availability, commercial terms, and deployment controls can vary by platform and enterprise agreement, so teams should verify the terms of their chosen hosting route.

Reasoning, coding, speed, and cost

Command R+ 08-2024 is intended for complex reasoning over retrieved information, structured data, and multi-step tool workflows. It is not presented in the supplied research as a dedicated reasoning model with a separate visible reasoning mode. Its practical reasoning value comes from its ability to follow instructions, analyze structured information, use tools, and synthesize long-context material.

It can assist with code generation and structured technical tasks, but the research does not establish it as a specialist software-engineering model. An editorial evaluation in the supplied record rates its reasoning and coding capabilities at 7 out of 10. Those scores are editorial assessments, not benchmarks or provider-published specifications.

The same editorial assessment rates speed at 7 out of 10 and cost at 4 out of 10. In practical terms, the model is positioned as a capable enterprise option rather than the lowest-cost choice for high-volume generation. Smaller Command models may be more economical for straightforward classification, simple extraction, or basic RAG. Command R+ 08-2024 is more defensible when the additional context capacity, complex tool use, and response quality justify the higher token price.

Main strengths and limitations

Strengths

• Large 128K-token context window for lengthy documents and extended conversations.
• Strong fit for complex enterprise RAG and document-grounded responses.
• Support for citations when working with supplied documents.
• Tool use for multi-step agentic workflows.
• Structured response support for downstream data processing.
• Multilingual conversational generation.
• Documented availability through Cohere’s API and selected cloud deployment options.

Limitations

• Text-only input and output, with no native image, audio, or video capability.
• Maximum generated output of 4,000 tokens.
• Higher cost than smaller Command models.
• No independently verified current-world knowledge beyond the model’s stated cutoff unless the application supplies retrieved information.
• Tool execution, retrieval, permissions, and application safety remain the developer’s responsibility.
• Fine-tuning is unverified for this exact model in the supplied research; documentation specifically confirms fine-tuning for Command R 08-2024 rather than Command R+ 08-2024.

When to choose Command R+ 08-2024

Choose Command R+ 08-2024 when the central problem is complex enterprise language work rather than image or media generation. It is a reasonable fit for internal knowledge assistants, document analysis, citation-based research, multilingual support agents, long reports, structured extraction, and workflows in which the model must decide which business tools to call.

Its long context is especially useful when splitting documents into many small pieces would make an application harder to build or could remove important surrounding context. Its structured output and citation support are also useful when responses need to be consumed by software or audited by people.

Consider a smaller Command model when the task is simple, the request volume is high, or token cost is the main constraint. Consider a newer Command A model when starting a new integration and the current Cohere catalog recommends it for most new use cases. Choose another type of model when native image, audio, or video input and output are requirements, or when the application needs responses longer than 4,000 tokens in one generation.

Bottom line

Command R+ 08-2024 is a text-focused Cohere model built around enterprise RAG, long-context processing, citations, structured responses, and tool-assisted workflows. Its 128K context window gives it room to work across substantial source material, while its 4,000-token output limit and usage pricing require careful application design. It is best understood as a relatively capable enterprise workhorse for grounded, tool-connected language applications—not as a general multimodal model or the cheapest option for routine generation.