MiniMax M2-her API: toman pricing and code
minimax/minimax-m2-her
You are billed for the usage the request actually reported. Prices follow the market. Cached input: 8,704 toman / 1M.Pricing and top-ups
MiniMax M2-her example: writing product copy
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-…")
stream = client.chat.completions.create(
model="minimax/minimax-m2-her",
messages=[{"role": "user", "content": "Write a 40-word product description for a 1.7-litre stainless steel electric kettle."}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="", flush=True)import OpenAI from "openai";
const client = new OpenAI({ baseURL: "https://api.uttapen.ir/v1", apiKey: "sk-up-…" });
const stream = await client.chat.completions.create({
model: "minimax/minimax-m2-her",
messages: [{ role: "user", content: "Write a 40-word product description for a 1.7-litre stainless steel electric kettle." }],
stream: true,
});
for await (const chunk of stream) process.stdout.write(chunk.choices[0]?.delta?.content ?? "");curl https://api.uttapen.ir/v1/chat/completions \
-H "Authorization: Bearer sk-up-…" \
-H "Content-Type: application/json" \
-d '{
"model": "minimax/minimax-m2-her",
"messages": [{"role": "user", "content": "Write a 40-word product description for a 1.7-litre stainless steel electric kettle."}],
"stream": true
}'What is MiniMax M2-her good for?
The id for MiniMax M2-her in our API is "minimax/minimax-m2-her", served from MiniMax. Context is 65,536 tokens; past that you have to summarise the history yourself. A single response can run to 2,048 tokens. On price it sits in the "cheap" band — cheaper than 151 and dearer than 255 of the other paid models in the catalogue.
MiniMax M2-her is text in, text out: no tool calling, no image input. For chat, rewriting, translation and summarising that is all you need, and it costs less than a model carrying features you never call.
Input runs at 87,038 toman per 1M tokens and output at 348,150 — output costs 4× input, so trimming the answer saves more than trimming the prompt. It supports cached input: repeated context is billed at 8,704 toman per 1M, which matters a lot if your system prompt is long. 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 Morph V3 Fast: MiniMax M2-her works out roughly 1.3× cheaper, 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 177 thousand-word requests on MiniMax M2-her, and every 1,000 toman is about 1,767 words of round trip. A job with one million input tokens and one million output tokens comes to 435,188 toman in total. Filling this model's 65,536-token window costs 5,704 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. It does not support include_reasoning, reasoning, response_format, tool_choice, 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 Nano Banana Pro (Gemini 3 Pro Image Preview) at 65,536 tokens.
Where it makes sense: high-volume work such as classification, tagging and bulk summarising.
MiniMax M2-her is a dialogue-first large language model built for immersive roleplay, character-driven chat, and expressive multi-turn conversations. Designed to stay consistent in tone and personality, it supports rich message...
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 | 270 toman |
| Summarising a ten-page document | 4,000 in + 600 out | 557 toman |
| Classifying a thousand short rows | 120,000 in + 20,000 out | 17,408 toman |
Frequently asked
- How do I call MiniMax M2-her 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-m2-her". Nothing else in your code changes.
- What does MiniMax M2-her cost in toman?
- 87,038 toman per 1M input tokens and 348,150 toman per 1M output tokens; a 1,000-word request is around 566 toman. You pay for the usage that request actually reported, and a failed request costs nothing.
- How much input does MiniMax M2-her take?
- Up to 65,536 tokens per request, roughly 49k words. A single answer can reach 2,048 tokens.
- Can I stream MiniMax M2-her'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.