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Command A API: toman pricing and code

cohere/command-a

json
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Input · per 1M tokens
725,313 toman
Output · per 1M tokens
2,901,250 toman
One 1,000-word request ≈
4,715 toman

You are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups

Command A example: JSON output against a schema

The example is picked from this model's own capabilities. Drop in your key and it runs as is.

from openai import OpenAI
import json

client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")

schema = {
    "name": "ticket",
    "schema": {
        "type": "object",
        "properties": {
            "category": {"type": "string", "enum": ["fani", "mali", "forush"]},
            "priority": {"type": "integer", "minimum": 1, "maximum": 5},
            "summary": {"type": "string"},
        },
        "required": ["category", "priority", "summary"],
        "additionalProperties": False,
    },
    "strict": True,
}

resp = client.chat.completions.create(
    model="cohere/command-a",
    messages=[{"role": "user", "content": "Ticket: "For two days I cannot download my invoice and I was charged twice.""}],
    response_format={"type": "json_schema", "json_schema": schema},
)
print(json.loads(resp.choices[0].message.content))

What is Command A good for?

Command A is one of Cohere's models. Put "cohere/command-a" in the model field and the rest of your code stays as it is. Command A keeps 256,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 8,192 tokens. On price it sits in the "mid-range" band — cheaper than 322 and dearer than 84 of the other paid models in the catalogue.

Capabilities available on this id: it returns schema-valid JSON through response_format, ready to hand to your code. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper.

For a back-of-envelope figure: about 4,715 toman per 1,000-word exchange (725,313 in, 2,901,250 out, per 1M tokens). You are always charged for the usage the request actually reported, never for the estimate, and a failed request costs nothing.

The closest alternative with the same capabilities from a different provider is Sonar Deep Research: Command A works out roughly 1.3× more expensive, and its context window is larger. Both run on the same key and the same code, so trying the other one is a single string change.

To make the figure concrete: 100,000 toman of credit buys roughly 21 thousand-word requests on Command A, and every 1,000 toman is about 212 words of round trip. A job with one million input tokens and one million output tokens comes to 3,626,563 toman in total. Filling this model's 256,000-token window costs 185,680 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "cohere/command-r7b-12-2024", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out 66.4× dearer (4,715 against 71 toman). The context windows differ too: 256,000 against 128,000 tokens. Maximum answer length differs as well: 8,192 against 4,000 tokens.

Cohere has 5 models in our catalogue; the cheapest is Command R7B (12-2024) at 71 toman per thousand words and the dearest Command R+ (08-2024) at 4,715. Among the less common parameters it accepts top_k — all through the standard request body. It does not support include_reasoning, reasoning, tool_choice, tools, which most models here do, so test before switching if your code relies on them. By context size the nearest option from another provider is Codestral 2508 at 256,000 tokens.

What to use it for: product chatbots and internal assistants, where cost and quality have to balance, extracting data against a fixed schema, analysing a long document or codebase in one request.

Provider's own description

Command A is an open-weights 111B parameter model with a 256k context window focused on delivering great performance across agentic, multilingual, and coding use cases. Compared to other leading proprietary...

Three real jobs, priced on this model

Each figure is derived from the prices above and moves when they do.

JobTokensCost
One chat turn with a medium history1,500 in + 400 out2,248 toman
Summarising a ten-page document4,000 in + 600 out4,642 toman
Classifying a thousand short rows120,000 in + 20,000 out145,063 toman

Frequently asked

How do I call Command A from Iran?
Sign up with your mobile number, top the wallet up in toman, create an API key, then in the official OpenAI SDK point base_url at https://api.uttapen.ir/v1 and set model to "cohere/command-a". Nothing else in your code changes.
What does Command A cost in toman?
725,313 toman per 1M input tokens and 2,901,250 toman per 1M output tokens; a 1,000-word request is around 4,715 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Command A take?
Up to 256,000 tokens per request, roughly 192k words. A single answer can reach 8,192 tokens.
Can I stream Command A's output?
Yes — with stream=true you get SSE events as the tokens are produced. This model has no tool calling and no image input, so pick a different one if you need either.