Which is cheaper, MiniMax M3 or MiniMax M2-her?

MiniMax M3 is the cheaper of the two: one 1,000-word request costs about 566 toman.

Which one has the larger context window?

MiniMax M3, with 1,048,576 tokens.

MiniMax M3 vs MiniMax M2-her

Both sit behind the same key and the same code on uttapen — the only thing that changes is the model string, so you can run each of them against your own workload without touching anything else.

Feature	MiniMax M3	MiniMax M2-her
Provider	MiniMax	MiniMax
Model id	minimax/minimax-m3	minimax/minimax-m2-her
Context	1,048,576 tokens	65,536 tokens
Max output	512,000 tokens	2,048 tokens
Input / 1M tokens	87,038 toman	87,038 toman
Output / 1M tokens	348,150 toman	348,150 toman
≈ one 1,000-word request	566 toman	566 toman
Input caching	17,408 toman / 1M	8,704 toman / 1M
Image input	Yes	No
File input	No	No
Tool calling	Yes	No
JSON output	Yes	No
Reasoning mode	Yes	No

Which one for what?

MiniMax M3

Multi-step problems, maths, and tracking down a bug
Whole documents or a codebase in a single request
Reading screenshots, invoices, and scanned forms
Agents that call your own APIs and database

566 toman per 1,000 words · Model page

MiniMax M2-her

Chat, summarising, and everyday text generation

566 toman per 1,000 words · Model page

Run both with the same code

Swap the model value between the two ids and send the same request twice; what each answer cost comes back in the X-Uttapen-Cost-Toman header.

curl https://api.uttapen.ir/v1/chat/completions \
  -H "Authorization: Bearer sk-up-…" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "minimax/minimax-m3",
    "messages": [{"role": "user", "content": "Introduce yourself in one sentence."}],
    "stream": true
  }'

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-m3",
    messages=[{"role": "user", "content": "Introduce yourself in one sentence."}],
    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-m3",
  messages: [{ role: "user", content: "Introduce yourself in one sentence." }],
  stream: true,
});
for await (const chunk of stream) {
  process.stdout.write(chunk.choices[0]?.delta?.content ?? "");
}

<?php
// composer require openai-php/client guzzlehttp/guzzle
$client = OpenAI::factory()
    ->withBaseUri('https://api.uttapen.ir/v1')
    ->withApiKey('sk-up-…')
    ->make();

$result = $client->chat()->create([
    'model' => 'minimax/minimax-m3',
    'messages' => [['role' => 'user', 'content' => 'Introduce yourself in one sentence.']],
]);
echo $result->choices[0]->message->content;

package main

import (
	"context"
	"fmt"

	"github.com/openai/openai-go"
	"github.com/openai/openai-go/option"
)

func main() {
	client := openai.NewClient(
		option.WithBaseURL("https://api.uttapen.ir/v1"),
		option.WithAPIKey("sk-up-…"),
	)
	resp, err := client.Chat.Completions.New(context.Background(), openai.ChatCompletionNewParams{
		Model:    "minimax/minimax-m3",
		Messages: []openai.ChatCompletionMessageParamUnion{openai.UserMessage("Introduce yourself in one sentence.")},
	})
	if err != nil {
		panic(err)
	}
	fmt.Println(resp.Choices[0].Message.Content)
}

model = "minimax/minimax-m2-her"

More comparisons: all pairs · model rankings · full catalogue

base_url = https://api.uttapen.ir/v1