What is the model architecture of MiniMax-M2.1?

It is a Multilingual Transformer-based LLM with a Hybrid dense-attention architecture that features optimized code tokenization.

What is the context window size for MiniMax-M2.1?

The model supports a context window of 204,800 tokens for input and output combined.

Which programming languages does MiniMax-M2.1 support?

It supports Rust, Java, Go, C++, TypeScript, JavaScript, Python, and SQL.

How is the performance of MiniMax-M2.1 measured?

Performance is evaluated using rigorous internal frameworks such as OctoCodingbench and SWE Review, with results averaged over 4 runs.

What is the API pricing for MiniMax-M2.1?

Input costs $0.315 per 1 million tokens, and output costs $1.26 per 1 million tokens.

What are the key features of MiniMax-M2.1?

Key features include: Multilingual Coding Mastery across 6+ languages, Agentic Reasoning for coherent multi-turn tasks, Concise & Clean Outputs, optimization for Real-Time Developer Workflows, and being fully Open & Deployable with open-source weights.

What are the core use cases for MiniMax-M2.1?

Core use cases include: Cross-Language Code Migration, Code Review & Refactoring, Automated Documentation generation, Intelligent Debugging, and integration into Developer Tools and IDEs like VSCode and JetBrains.

How does MiniMax-M2.1 compare to Claude Sonnet 4.5?

M2.1 matches or exceeds Claude Sonnet 4.5 in coding-specific benchmarks while using fewer activated parameters. It offers significantly lower inference cost and latency, making it ideal for high-throughput coding agents.

How does MiniMax-M2.1 compare to DeepSeek-Coder?

M2.1 demonstrates stronger instruction following in complex, multi-step coding scenarios (e.g., full-stack implementation). It excels in real-world tool integration and stateful reasoning, which is critical for IDE plugins and autonomous agents.

MiniMax-M2.1 — API Pricing and Benchmarks

Name: MiniMax-M2.1 API
Brand: MiniMax

Overview

Multilingual Code Generation & Refactoring AI Model

MiniMax-M2.1 is a cutting-edge large language model built for high-performance code generation, refactoring, and cross-language reasoning. Optimized for real-world developer workflows, it supports languages such as Rust, Java, Go, C++, TypeScript, and JavaScript, offering fast, clean, and reliable outputs.

Technical Specifications

Model type: Multilingual Transformer-based LLM
Architecture: Hybrid dense-attention model with optimized code tokenization
Context window: 204,800 tokens (input + output)
Supported languages: Rust, Go, Java, C++, TypeScript, JavaScript, Python, SQL

Performance Benchmarks

Evaluated using rigorous internal frameworks (e.g., OctoCodingbench, SWE Review), with results averaged over 4 runs.

API Pricing

Input: $0.39/ 1M tokens
Output: $1.56 / 1M tokens

Key Features

Multilingual Coding Mastery: Excels across 6+ major programming languages with syntax-aware generation and refactoring
Agentic Reasoning: Maintains coherent reasoning between turns, critical for tool use, IDE integration, and long-horizon tasks
Concise & Clean Outputs: Minimizes verbosity while preserving functional correctness and style consistency
Real-Time Developer Workflows: Optimized for low latency and high throughput in coding assistants and CI/CD pipelines
Open & Deployable: Fully open-source weights enable on-prem, edge, or custom deployment scenarios

Core Use Cases

Cross-Language Code Migration: Seamlessly rewrite applications between Rust, Go, and JavaScript without losing logic integrity.
Code Review & Refactoring: Automate code readability enhancements, style consistency, and optimization opportunities.
Automated Documentation: Generate aligned docstrings, inline comments, and technical documentation for complex repositories.
Intelligent Debugging: Detect potential bugs and suggest fixes within a single inference cycle.
Developer Tool Integration: Connect via SDKs or APIs to augment IDEs such as VSCode, JetBrains, or Neovim with real-time AI assistance.

Model Comparison

vs. Claude Sonnet 4.5: M2.1 matches or exceeds Sonnet 4.5 in coding-specific benchmarks while using far fewer activated parameters. Offers significantly lower inference cost and latency, making it ideal for high-throughput coding agents.

vs. DeepSeek-Coder: M2.1 demonstrates stronger instruction following in complex, multi-step coding scenarios (e.g., full-stack feature implementation). Excels in real-world tool integration and stateful reasoning, critical for IDE plugins and autonomous agents.

Uptime and performance

Uptime (7d): 100%

Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.

Use cases

Cross-Language Code Migration: Seamlessly rewrite applications between Rust, Go, and JavaScript without losing logic integrity.
Code Review & Refactoring: Automate code readability enhancements, style consistency, and optimization opportunities.
Automated Documentation: Generate aligned docstrings, inline comments, and technical documentation for complex repositories.
Intelligent Debugging: Detect potential bugs and suggest fixes within a single inference cycle.
Developer Tool Integration: Connect via SDKs or APIs to augment IDEs such as VSCode, JetBrains, or Neovim with real-time AI assistance.

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How to use MiniMax-M2.1 API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to minimax/m2-1.

import requests

r = requests.post(
    "https://api.aimlapi.com/v1/chat/completions",
    headers={"Authorization": "Bearer " + AIMLAPI_KEY},
    json={
      "model": "minimax/m2-1",
      "messages": [
        {
          "role": "user",
          "content": "Hello!"
        }
      ]
    },
)
print(r.json())

const r = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "minimax/m2-1",
    "messages": [
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }),
});
console.log(await r.json());

curl -X POST https://api.aimlapi.com/v1/chat/completions \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"minimax/m2-1","messages":[{"role":"user","content":"Hello!"}]}'

OpenAI-compatible — swap the base URL and it works with your existing SDK.