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.

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 — One API 400+ AI Models

Q: What is MiniMax-M2.1?

MiniMax-M2.1 is a cutting-edge large language model specifically built for high-performance code generation, refactoring, and cross-language reasoning. It is optimized for real-world developer workflows and supports multiple programming languages.

MiniMax-M2.1

Lightweight. Code-Optimized. Agentic-Ready.

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.

Example H2

Try it now