Overview

Minimax M3 stands out within the Minimax-m3 family as a specialized thinking model designed for complex reasoning tasks. With a massive 524,288 token context window, it handles extensive documentation and multi-step analysis without losing coherence. Developers can integrate this capability immediately via our standard API endpoints, ensuring your first successful call happens within minutes. The architecture prioritizes logical deduction and code generation, making it an ideal choice for building sophisticated agents that require deep contextual understanding alongside rapid execution.

Accessible from the starter tier, Minimax M3 offers production-ready stability with a 2x credit multiplier relative to the base rate, balancing advanced performance with cost efficiency. This structure allows researchers to benchmark complex reasoning against alternatives while providing business leaders predictable pricing models for scaling operations. The model fully supports Arabic and English linguistic tasks, ensuring seamless deployment for regional applications without requiring additional configuration. Choose Minimax M3 on LLM Resayil to leverage high-volume context processing and advanced cognitive capabilities without infrastructure overhead.

Specifications

Display Name minimax m3

Family Minimax-m3

Category Thinking

Context Window 524,288 tokens

Min Tier Starter

Status Available

Pricing

2×

credits per token

1K 2,000 Credits

10K 20,000 Credits

100K 200,000 Credits

View Pricing Plans

Code Examples

from openai import OpenAI

client = OpenAI(
    base_url="https://llmapi.resayil.io/v1/",
    api_key="YOUR_API_KEY"
)

response = client.chat.completions.create(
    model="minimax-m3",
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(response.choices[0].message.content)

import anthropic

client = anthropic.Anthropic(
    base_url="https://llmapi.resayil.io/v1",
    api_key="YOUR_API_KEY"
)

message = client.messages.create(
    model="minimax-m3",
    max_tokens=1024,
    messages=[
        {"role": "user", "content": "Hello!"}
    ]
)

print(message.content[0].text)

const response = await fetch(
    "https://llmapi.resayil.io/v1/chat/completions",
    {
        method: "POST",
        headers: {
            "Content-Type": "application/json",
            "Authorization": "Bearer YOUR_API_KEY"
        },
        body: JSON.stringify({
            model: "minimax-m3",
            messages: [
                { role: "user", content: "Hello!" }
            ]
        })
    }
);

const data = await response.json();
console.log(data.choices[0].message.content);

curl https://llmapi.resayil.io/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "minimax-m3",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

Use Cases

Analyzing extensive legal contracts for compliance issues

Summarizing lengthy technical documentation and research papers

Debugging complex codebases with large context windows

Advanced strategic planning through multi-step reasoning processes

Processing long conversation histories for consistent responses

In-Depth Guide

Full Guide

Complete Guide to minimax m3 — LLM Resayil

minimax m3