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Mistral Nemo API: toman pricing and code

mistralai/mistral-nemo

Tool callingJSON output
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Input · per 1M tokens
10,040 toman
Output · per 1M tokens
10,387 toman
One 1,000-word request ≈
27 toman

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

Mistral Nemo example: tool calling

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-…")

tools = [{
    "type": "function",
    "function": {
        "name": "check_stock",
        "description": "Returns the stock level of a product",
        "parameters": {
            "type": "object",
            "properties": {"sku": {"type": "string"}},
            "required": ["sku"],
        },
    },
}]

messages = [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}]
first = client.chat.completions.create(model="mistralai/mistral-nemo", messages=messages, tools=tools)
call = first.choices[0].message.tool_calls[0]

# you run the function yourself — the model never touches the database
args = json.loads(call.function.arguments)
result = {"sku": args["sku"], "qty": 7}

messages += [first.choices[0].message, {"role": "tool", "tool_call_id": call.id, "content": json.dumps(result)}]
final = client.chat.completions.create(model="mistralai/mistral-nemo", messages=messages, tools=tools)
print(final.choices[0].message.content)
import OpenAI from "openai";

const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });

const tools = [{
  type: "function",
  function: {
    name: "check_stock",
    description: "Returns the stock level of a product",
    parameters: { type: "object", properties: { sku: { type: "string" } }, required: ["sku"] },
  },
}];

const messages = [{ role: "user", content: "How many of the Nike NK-42 shoe are in stock?" }];
const first = await client.chat.completions.create({ model: "mistralai/mistral-nemo", messages, tools });
const call = first.choices[0].message.tool_calls[0];

const args = JSON.parse(call.function.arguments);
const result = { sku: args.sku, qty: 7 };

messages.push(first.choices[0].message, { role: "tool", tool_call_id: call.id, content: JSON.stringify(result) });
const final = await client.chat.completions.create({ model: "mistralai/mistral-nemo", messages, tools });
console.log(final.choices[0].message.content);
curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "mistralai/mistral-nemo",
    "messages": [{"role": "user", "content": "How many of the Nike NK-42 shoe are in stock?"}],
    "tools": [{
      "type": "function",
      "function": {
        "name": "check_stock",
        "parameters": {"type": "object", "properties": {"sku": {"type": "string"}}, "required": ["sku"]}
      }
    }]
  }'

What is Mistral Nemo good for?

Mistral Nemo comes from Mistral; in uttapen you reach it with the model id "mistralai/mistral-nemo". It accepts up to 131,072 tokens of input per call, so a long document or several source files fit in a single request. A single response can run to 16,384 tokens. On price it sits in the "very cheap" band — cheaper than 393 and dearer than 5 of the other paid models in the catalogue.

What you get on top of text in, text out: it supports tool calling, so it can invoke your own functions with valid arguments; 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.

Input runs at 10,040 toman per 1M tokens and output at 10,387 — output costs 1× input, so trimming the answer saves more than trimming the prompt. 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 Ling 3.0 Tiny, and Mistral Nemo costs about the same, and its context window is smaller. 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 3,704 thousand-word requests on Mistral Nemo, and every 1,000 toman is about 37,037 words of round trip. A job with one million input tokens and one million output tokens comes to 20,427 toman in total. Filling this model's 131,072-token window costs 1,316 toman on the input side alone, which is the real reason to keep conversation history short.

Its nearest relative in the catalogue is "mistralai/voxtral-small-24b-2507", and choosing between those two is where most people hesitate. On a thousand-word request this variant works out about 6.7× cheaper (27 against 180 toman). The context windows differ too: 131,072 against 32,768 tokens. Maximum answer length differs as well: 16,384 against 26,214 tokens. On parameters, this one takes logit_bias, logprobs, min_p, repetition_penalty.

Mistral has 21 models in our catalogue; the cheapest is Mistral Nemo at 27 toman per thousand words and the dearest Mistral Medium 3.5 at 4,051. Among the less common parameters it accepts logit_bias, logprobs, min_p, presence_penalty, repetition_penalty, top_k — all through the standard request body. It does not support include_reasoning, reasoning, 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 Aion-2.0 at 131,072 tokens.

Where it makes sense: high-volume work such as classification, tagging and bulk summarising, agents that reach out to APIs and databases, extracting data against a fixed schema.

Provider's own description

A 12B parameter model with a 128k token context length built by Mistral in collaboration with NVIDIA. The model is multilingual, supporting English, French, German, Spanish, Italian, Portuguese, Chinese, Japanese,...

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 out19 toman
Summarising a ten-page document4,000 in + 600 out46 toman
Classifying a thousand short rows120,000 in + 20,000 out1,413 toman

Price history

DateInput (toman/1M)Output (toman/1M)
9 Oct 202610,04010,387
7 Sep 20266,57810,387

Every price change for this model is recorded. Toman figures use today's rate.

Frequently asked

How do I call Mistral Nemo 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 "mistralai/mistral-nemo". Nothing else in your code changes.
What does Mistral Nemo cost in toman?
10,040 toman per 1M input tokens and 10,387 toman per 1M output tokens; a 1,000-word request is around 27 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
How much input does Mistral Nemo take?
Up to 131,072 tokens per request, roughly 98k words. A single answer can reach 16,384 tokens.
Does Mistral Nemo support streaming and tool calling?
Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape. Structured output through response_format works too.