Cohere: Command R7B (12-2024)

Command R7B (12-2024) is a text model from Cohere, released by the provider on 14 December 2024. It is described as a small, fast update of the Command R+ model, which places it in Cohere's Command family of language models aimed at practical, tool-driven workloads rather than general chat alone. If you already know the Command line, this is the compact, quicker variant to consider when you want the same style of task coverage without a large model.

The model takes text in and returns text out, with a context window of 128,000 tokens and a maximum output of 4,000 tokens. Its knowledge cutoff is 31 August 2024, so anything after that date is outside what it learned during training. Cohere positions it as strong at retrieval augmented generation, tool use, agents, and similar tasks that require complex reasoning. Those are the capabilities to plan around: long inputs, structured or tool-oriented work, and multi-step reasoning rather than pure creative writing.

That combination points to a fairly specific set of use cases. RAG pipelines are the obvious fit, since the 128k context lets you pack in retrieved documents alongside the question without aggressive trimming. Tool use and agent loops follow from the same strengths, for example a model that decides which function to call, reads the result, and continues. Because structured outputs are supported, you can also use it for extraction and classification tasks where the response needs to conform to a schema. The 4,000 token output ceiling is worth noting: it suits answers, summaries and tool calls, but not long document generation in a single response.

You call it through api14's OpenAI-compatible API using the model id "cohere/command-r7b-12-2024". Because the interface follows the OpenAI chat completions shape, existing OpenAI SDKs and clients generally work with only the base URL and model id changed. The request parameters it supports are frequency_penalty, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, top_k and top_p. The presence of seed is useful when you want reproducible sampling, and response_format plus structured_outputs are the ones to reach for when downstream code needs parseable output.

On limits, the main constraints given are the 4,000 token maximum output and the 31 August 2024 knowledge cutoff. If your task depends on very recent information, you will need to supply it in the prompt or through retrieval rather than relying on the model's own knowledge. If you need image or audio input, this model does not accept them, since it is text only. Within those bounds it is a reasonable default for RAG, agents and structured text tasks where speed and a long input window matter more than long-form generation.