Cohere: Command R (08-2024)

Command R (08-2024) is a text generation model from Cohere, identified on api14 as cohere/command-r-08-2024. It is an update to the earlier Command R model, released by the provider on 30 August 2024, and it keeps the same overall role in Cohere's lineup while improving on the original. The update focuses on better performance for multilingual retrieval-augmented generation (RAG) and tool use, and it is also stronger at math, code and reasoning than the version it replaces.

The model works with text in and text out only. It accepts text prompts and returns text completions, with no image or audio input. The context window is 128,000 tokens, which is large enough to hold long documents, multi-turn conversations or several retrieved passages alongside the instructions. The maximum output is 4,000 tokens per response, and the knowledge cutoff is 31 March 2024, so anything after that date has to come from the prompt or from a tool call rather than from the model's own training data.

Those capabilities point to a few practical uses. Because multilingual RAG is a stated strength, the model suits question answering over a document collection where queries and sources may be in different languages, with retrieved chunks placed in the long context window. Tool use and structured outputs make it a reasonable fit for agents and workflows that need to call functions, extract fields into a schema or route a request to another system. The improvements in math, code and reasoning also make it usable for analysis tasks, code explanation and step-by-step problem solving, though the 4,000 token output cap means long generated files or very detailed reports need to be split across calls.

You call it through api14's OpenAI-compatible API using the model id cohere/command-r-08-2024, so existing OpenAI SDK code works with a change of base URL and model name. The request parameters it supports are frequency_penalty, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, tool_choice, tools, top_k and top_p. In practice that covers sampling control (temperature, top_p, top_k), repetition control (frequency and presence penalties), output length (max_tokens), stop sequences, deterministic-ish sampling via seed, and both JSON-style response_format and structured_outputs for schema-constrained results. Tool use is configured with tools and tool_choice.

A few limits are worth knowing before you commit. Input and output are text only, so if you need vision or audio, this model is not the right choice. The 4,000 token output ceiling rules it out for tasks that must emit very long single responses. The knowledge cutoff of 31 March 2024 means recent information must be supplied in the prompt or fetched through tools. And since the model is positioned around RAG, tool use and reasoning rather than as a general flagship, a different model may fit better when your priority is something outside those areas.