MiniMax M2.1: Significantly Enhanced Multi-Language Programming, Built for Real-World Complex Tasks

MiniMax has been continuously transforming itself in a more AI-native way. The core driving forces of this process are models, Agent scaffolding, and organization. Today we are releasing updates to the model component, namely MiniMax M2.1, hoping to help more enterprises and individuals find more AI-native ways of working (and living) sooner.

In M2, we primarily addressed issues of model cost and model accessibility. In M2.1, we are committed to improving performance in real-world complex tasks: focusing particularly on usability across more programming languages and office scenarios, and achieving the best level in this domain.

Key Highlights of MiniMax M2.1

Exceptional Multi-Programming Language Capabilities

Many models in the past primarily focused on Python optimization, but real-world systems are often the result of multi-language collaboration. In M2.1, we have systematically enhanced capabilities in Rust, Java, Golang, C++, Kotlin, Objective-C, TypeScript, JavaScript, and other languages. The overall performance on multi-language tasks has reached industry-leading levels, covering the complete chain from low-level system development to application layer development.

WebDev and AppDev: A Comprehensive Leap in Capability and Aesthetics

Addressing the widely recognized weakness in mobile development across the industry, M2.1 significantly strengthens native Android and iOS development capabilities. Meanwhile, we have systematically enhanced the model's design comprehension and aesthetic expression in Web and App scenarios, enabling excellent construction of complex interactions, 3D scientific scene simulations, and high-quality visualization, making vibe coding a sustainable and deliverable production practice.

Enhanced Composite Instruction Constraints, Enabling Office Scenarios

As one of the first open-source model series to systematically introduce Interleaved Thinking, M2.1's systematic problem-solving capabilities have been further upgraded. The model not only focuses on code execution correctness but also emphasizes integrated execution of composite instruction constraints, providing higher usability in real office scenarios.

More Concise and Efficient Responses

Compared to M2, MiniMax-M2.1 delivers more concise model responses and thought chains. In practical programming and interaction experiences, response speed has significantly improved and token consumption has notably decreased, resulting in smoother and more efficient performance in AI Coding and Agent-driven continuous workflows.

Outstanding Agent/Tool Scaffolding Generalization Capabilities

M2.1 demonstrates excellent performance across various programming tools and Agent frameworks. It exhibits consistent and stable results in tools such as Claude Code, Droid (Factory AI), Cline, Kilo Code, Roo Code, and BlackBox, while providing reliable support for Context Management mechanisms including Skill.md, Claude.md/agent.md/cursorrule, and Slash Commands.

High-Quality Dialogue and Writing

M2.1 is no longer just stronger in coding capabilities. In everyday conversation, technical documentation, and writing scenarios, it also provides more detailed and structured responses.

Benchmarks

MiniMax-M2.1 delivers a significant leap over M2 on core software engineering leaderboards. It shines particularly bright in multilingual scenarios, where it outperforms Claude Sonnet 4.5 and closely approaches Claude Opus 4.5.

We also evaluated MiniMax-M2.1 on SWE-bench Verified across a variety of coding agent frameworks. The results highlight the model's exceptional framework generalization and robust stability.

Furthermore, across specific benchmarks—including test case generation, code performance optimization, code review, and instruction following—MiniMax-M2.1 demonstrates comprehensive improvements over M2. In these specialized domains, it consistently matches or exceeds the performance of Claude Sonnet 4.5.

To evaluate the model's full-stack capability to architect complete, functional applications from zero to one, we established a novel benchmark: VIBE (Visual & Interactive Benchmark for Execution). This suite encompasses five core subsets: Web, Simulation, Android, iOS, and Backend.

MiniMax-M2.1 delivers outstanding performance on the VIBE aggregate benchmark, achieving an average score of 88.6—demonstrating robust full-stack development capabilities.

Showcases

Multilingual Coding

3D Interactive Animation

MiniMax M2.1 built a 3D Dreamy Christmas Tree based on React Three Fiber and InstancedMesh, successfully rendering over 7,000 instances. It supports gesture interaction and complex particle animation, demonstrating advanced 3D rendering capabilities.

Avant-Garde Web UI Design

M2.1 generated a minimalist photographer's personal homepage using an asymmetrical layout and a black-white-red contrasting color scheme. By combining immersive imagery with brutalist typography, it achieved a high-impact visual effect.

Website - Skincare Brand

M2.1 designed a landing page for a high-end organic skincare brand. Adopting a Clean & Minimalist style, it accurately presented the brand's premium identity and international visual appeal.

Web 3D Lego Sandbox

M2.1 developed a high-freedom 3D brick building application based on Three.js, implementing precise grid snapping algorithms and collision detection mechanisms. The project perfectly replicates the glossy texture of plastic bricks, supporting multi-angle rotation, drag-and-drop assembly, and instant color switching.

Native App Development - Android

M2.1 used Kotlin to develop a native Android gravity sensor simulator. Utilizing the gyroscope for a silky-smooth control experience, it features clever visual easter eggs that elegantly present the MERRY XMAS MiniMax M2.1 message through natural UI transitions and collision effects.

Native App Development - iOS

M2.1 wrote an interactive iOS Home Screen widget, designing a Sleeping Santa click-to-wake mechanism. The logic is complete with native-level animation effects.

Web Audio Simulation Development

M2.1 developed a 16-step drum machine simulator based on the Web Audio API. It integrates synthesized drum sounds, non-linear rhythm algorithms, and real-time glitch sound effects, providing an avant-garde electronic music experience.

Rust TUI

M2.1 built a powerful Linux security audit tool with dual CLI + TUI modes using Rust, supporting one-click low-level scanning and intelligent risk rating for critical items such as processes, networks, and SSH.

Python Data Dashboard

M2.1 created a Web3 cryptocurrency price dashboard in the style of The Matrix. Using Python for real-time price API fetching, HTML structure, and CSS with Matrix aesthetic: green digital rain on black background, monospaced font, glowing neon green text, terminal-like UI.

C++ Image Rendering

M2.1 utilized C++ and GLSL to implement complex light transport algorithms, accurately rendering the physical refraction of a crystal ball, detailed SDF modeling of a snowman, and shimmering snow effects in a real-time environment.

Java Real-time Danmaku

M2.1 implemented a high-performance real-time Danmaku (bullet chat) system based on Java, a clean and intuitive user interface, and millisecond-level response capabilities.

SVG Generation

M2.1 generated an interactive isometric SVG island map, constructing a detailed miniature world that supports one-click zooming to freely explore four major themed areas.

Agentic Tool Use

M2.1 demonstrated its tool-use capabilities by autonomously invoking Excel and Yahoo Finance to complete an end-to-end task, ranging from market research data cleaning and analysis to chart generation.

Digital Employee

The Digital Employee is a key feature of the MiniMax M2.1 model. M2.1 accepts web content presented in text form and controls mouse clicks and keyboard inputs via text-based commands. It can complete end-to-end tasks in daily office scenarios across administration, data science, finance, human resources, and software development.

Local Deployment Guide

Download the model from the HuggingFace repository. We recommend using the following inference frameworks to serve the model: SGLang, vLLM, Transformers, and Ktransformers.

Recommended inference parameters: temperature=1.0, top_p=0.95, top_k=40

How to Use

The MiniMax-M2.1 API is now live on the MiniMax Open Platform. Our product MiniMax Agent, built on MiniMax-M2.1, is now publicly available. The MiniMax-M2.1 model weights are now open-source, allowing for local deployment and use.