Cohere: Command A

Command A is a text generation model from Cohere, released by the provider on 13 March 2025. It is an open-weights model with 111 billion parameters, positioned by Cohere for agentic, multilingual and coding work. On api14 it appears under the model id cohere/command-a and sits in the text category, so it is intended for language tasks rather than image, audio or video handling.

The model accepts text as input and returns text as output. Its context window is 256,000 tokens, which allows very long prompts, large documents or extended conversation histories to be sent in a single request. The maximum output length is 8,192 tokens, so responses are capped well below the input capacity. Its knowledge cutoff is 31 August 2024, meaning events and information after that date are not part of what the model learned during training.

Those characteristics point to a few practical uses. The large context window suits tasks that involve reading and reasoning over long inputs, such as summarising lengthy reports, answering questions against a large body of text, or working through multi step agentic workflows where the model must track state across many turns. The multilingual focus makes it a reasonable choice for applications serving users in more than one language, including translation style tasks and cross lingual question answering. The coding emphasis means it can be used for code generation, explanation and review within a text based pipeline. Because only text is supported, it fits chat assistants, document processing and developer tooling rather than anything requiring vision or audio.

You call the model through api14's OpenAI-compatible API using the model id cohere/command-a. The supported request parameters are frequency_penalty, max_tokens, presence_penalty, response_format, seed, stop, structured_outputs, temperature, top_k and top_p. That set covers the usual sampling controls (temperature, top_p, top_k), repetition controls (frequency and presence penalties), output length via max_tokens, stopping conditions via stop, deterministic sampling via seed, and output shaping through response_format and structured_outputs. If you already use an OpenAI style client, you can point it at api14 and change the model id.

Two limits are worth keeping in mind when choosing. The model is text only, so if your task depends on images or audio, a different model is needed. The 8,192 token output cap also means very long generations must be split across multiple requests or handled with a model that allows longer responses. Finally, the knowledge cutoff of 31 August 2024 means questions about more recent events should be answered with retrieved context rather than relying on the model's internal knowledge.