How to call Cohere Command R through the API

To call Cohere Command R through an API, you need a Cohere API key and an exact model identifier confirmed for your account. Cohere documents an OpenAI-compatible endpoint, but its current compatibility example↗ (opens in a new tab) uses Command A+, so do not treat that example as confirmation of a live Command R identifier. Start with one small request and check the response before building a larger workflow. fenbs can keep the tested recipe and findings outside the application, without storing the key.

What should you check before making the request?

Separate three choices: the model, the API route, and the application using it. A model family name is not enough for a reproducible request. Record the exact identifier you intend to send, and verify that Cohere currently makes it available through your chosen route and key.

1. Read the current Cohere documentation linked below and confirm the model identifier for your account. If Command R availability is unclear, resolve that before writing application code around it.
2. Create a key using Cohere’s API keys page, linked from its key documentation. Keep the value in your application’s approved secret storage.
3. Choose a small, nonconfidential sample with an answer you can check yourself.
4. Define success before sending: an authenticated response from the intended model and an answer supported by the sample.

The broader Cohere model guide covers model-family selection. This guide focuses on making and checking the application request. If the names themselves are unfamiliar, what an LLM is explains the model layer in plain language.

How do you configure the documented endpoint?

Cohere’s compatibility guide configures an OpenAI SDK client with the base URL shown below and a Cohere API key. An SDK is a library that handles requests for your application. The endpoint is a Cohere service; using this library does not mean you should supply an OpenAI credential.

import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.cohere.ai/compatibility/v1",
    api_key=os.environ["COHERE_API_KEY"],
)

response = client.chat.completions.create(
    model=os.environ["COHERE_MODEL_ID"],
    messages=[{
        "role": "user",
        "content": (
            "Source A: The sample office closes at 6 p.m. on Friday. "
            "Using only Source A, state Friday closing time and "
            "quote the words that support it. If the source does "
            "not answer, say so."
        ),
    }],
)

print(response.choices[0].message.content)

The two environment-variable names above are names you choose for local configuration, not buttons in Cohere. Set them through your normal development environment. The model value deliberately has no guessed default: copy a currently verified identifier, then keep that identifier with your test results.

The vendor’s example uses the same request method with a specific Command A+ model. If you use that model to test authentication while checking Command R access, label the result accurately. A successful call to another model does not validate Command R.

How do you verify an answer against a source?

Use a source you can read in seconds. The sample office statement above is invented test data, so the expected answer is easy to inspect: Friday closes at 6 p.m. The supporting quotation should come from the supplied sentence. This tests evidence use without requiring a document collection or outside search.

• Check that the answer matches the source, including the day and time.
• Check the quotation against the actual text. A convincing quotation is useful only when it exists in the source.
• Ask a question the source cannot answer, such as Saturday hours. The model should acknowledge the missing information.
• Change one source fact and repeat the request. Confirm that the answer follows the new input rather than the earlier result.

These are recommended acceptance tests, not a claim that one prompt guarantees accuracy. Asking for a source label is also different from enabling a vendor citation feature. Keep the original input with your expected answer so someone else can repeat the check.

What changes when you add conversation history or tools?

Cohere documents conversation history in the messages array, streaming responses, structured outputs, and function tools in its compatibility guide. Introduce these one at a time after plain text works. Otherwise a failed request leaves several possible causes: the credential, the model, the message format, or an added feature.

For a conversation test, include the earlier turns you want the application to send. Do not assume a new request knows the previous exchange. For a tool test, write down what the tool may change and inspect the requested action before allowing it to affect real work.

An API integration and an editor integration need separate checks. Using Command in Cline addresses that boundary; passing this request does not establish that every coding client accepts the same configuration.

What should you do when the request fails?

Return to the smallest request and inspect the service’s error. Confirm the endpoint, whether your application supplied a key, and the exact model identifier. Keep the error wording with secrets removed. Repeatedly changing the prompt will not resolve uncertainty about credentials or model access.

Check Cohere’s key and rate-limit documentation↗ (opens in a new tab) before increasing traffic. Evaluation and production keys have different usage rules, and newer Command variants can retain evaluation-style restrictions even with a production key. Do not copy another model’s allowance into your deployment plan.

API access is also separate from downloading model weights. If you are considering operating a model yourself, the open-source model guide is a separate starting point. This request recipe does not establish local hardware requirements or permission to deploy a particular model.

How to keep your tested API recipe with fenbs

Keep enough to carry on from exactly where you left off, on any device and in another AI. Save the request shape, verified model identifier, expected result, and what still failed. You do not need to save every experimental response to make the next session useful.

1. Create a free account.
2. Add a task named “Check the Cohere request” and write the small result you need.
3. Save the working endpoint, model identifier, safe sample input, and checks that passed. Leave out the API key.
4. Before stopping, write what worked and the next thing to try. Read it when you return or give it to your next assistant.

fenbs keeps the work record; it does not host Cohere models or run your API calls. For a disposable test, a local note may be enough. When several sessions or people need the same verified recipe, how fenbs works explains the lasting record. Find more model-selection guides in the AI models hub.