MiniMax M1 API: toman pricing and code
minimax/minimax-m1
toolsreasoningYou are billed for the usage the request actually reported. Prices follow the market.Pricing and top-ups
MiniMax M1 example: a multi-step problem with reasoning
The example is picked from this model's own capabilities. Drop in your key and it runs as is.
from openai import OpenAI
client = OpenAI(base_url="https://api.uttapen.ir/v1", api_key="sk-up-…")
resp = client.chat.completions.create(
model="minimax/minimax-m1",
messages=[{"role": "user", "content": (
"A shop has 3 warehouses. A ships 120 orders a day, B ships 85 and C ships 40. "
"If C closes and its load is split between A and B in proportion to their capacity, how many does each ship a day? "
"Work through it step by step and give just the two numbers at the end."
)}],
reasoning_effort="medium", # low | medium | high
)
print(resp.choices[0].message.content)
# reasoning tokens count as output tokens too:
print(resp.usage.completion_tokens_details)curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "minimax/minimax-m1",
"messages": [{"role": "user", "content": "120 and 85 daily orders split across two warehouses; work through it step by step."}],
"reasoning": {"effort": "medium"}
}'import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const resp = await client.chat.completions.create({
model: "minimax/minimax-m1",
messages: [{ role: "user", content: "120 and 85 daily orders are split between two warehouses; work through it step by step and give just the two numbers at the end." }],
reasoning_effort: "medium",
});
console.log(resp.choices[0].message.content);
console.log(resp.usage.completion_tokens_details);What is MiniMax M1 good for?
MiniMax M1 is one of MiniMax's models. Put "minimax/minimax-m1" in the model field and the rest of your code stays as it is. MiniMax M1 keeps 1,000,000 tokens in view at once, and that number is what caps the conversation history in your product. A single response can run to 40,000 tokens. On price it sits in the "mid-range" band — cheaper than 215 and dearer than 191 of the other paid models in the catalogue.
Capabilities available on this id: it supports tool calling, so it can invoke your own functions with valid arguments; it has a reasoning mode that pays off on multi-step problems, maths and debugging. All of it works through the standard parameters of the official OpenAI SDK — no custom client, no wrapper. Keep in mind that reasoning tokens are output tokens and do appear on the bill.
For a back-of-envelope figure: about 1,037 toman per 1,000-word exchange (159,569 in, 638,275 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 o3 Mini (batch): MiniMax M1 works out roughly 1× 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 96 thousand-word requests on MiniMax M1, and every 1,000 toman is about 964 words of round trip. A job with one million input tokens and one million output tokens comes to 797,844 toman in total. Filling this model's 1,000,000-token window costs 159,569 toman on the input side alone, which is the real reason to keep conversation history short.
MiniMax has 11 models in our catalogue; the cheapest is MiniMax M2 at 481 toman per thousand words and the dearest MiniMax M1 at 1,037. Among the less common parameters it accepts repetition_penalty, top_k — all through the standard request body. It does not support response_format, structured_outputs, 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 Nova 2 Lite at 1,000,000 tokens.
What to use it for: product chatbots and internal assistants, where cost and quality have to balance, logic puzzles and code review, agents that reach out to APIs and databases, analysing a long document or codebase in one request.
MiniMax-M1 is a large-scale, open-weight reasoning model designed for extended context and high-efficiency inference. It leverages a hybrid Mixture-of-Experts (MoE) architecture paired with a custom "lightning attention" mechanism, allowing it...
Three real jobs, priced on this model
Each figure is derived from the prices above and moves when they do.
| Job | Tokens | Cost |
|---|---|---|
| One chat turn with a medium history | 1,500 in + 400 out | 495 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 1,021 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 31,914 toman |
Frequently asked
- How do I call MiniMax M1 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 "minimax/minimax-m1". Nothing else in your code changes.
- What does MiniMax M1 cost in toman?
- 159,569 toman per 1M input tokens and 638,275 toman per 1M output tokens; a 1,000-word request is around 1,037 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does MiniMax M1 take?
- Up to 1,000,000 tokens per request, roughly 750k words. A single answer can reach 40,000 tokens.
- Does MiniMax M1 support streaming and tool calling?
- Streaming (stream=true) works on every model here. This one supports tool calling in the standard OpenAI shape.