MiniMax: MiniMax M2.1

Text input Text output
Author's Description

MiniMax-M2.1 is a lightweight, state-of-the-art large language model optimized for coding, agentic workflows, and modern application development. With only 10 billion activated parameters, it delivers a major jump in real-world capability while maintaining exceptional latency, scalability, and cost efficiency. Compared to its predecessor, M2.1 delivers cleaner, more concise outputs and faster perceived response times. It shows leading multilingual coding performance across major systems and application languages, achieving 49.4% on Multi-SWE-Bench and 72.5% on SWE-Bench Multilingual, and serves as a versatile agent “brain” for IDEs, coding tools, and general-purpose assistance. To avoid degrading this model's performance, MiniMax highly recommends preserving reasoning between turns. Learn more about using reasoning_details to pass back reasoning in our [docs](https://openrouter.ai/docs/use-cases/reasoning-tokens#preserving-reasoning-blocks).

Key Specifications
Cost
$$$$$
Context
204K
Parameters
10B (Rumoured)
Released
Dec 22, 2025
Speed
Ability
Reliability
Supported Parameters

This model supports the following parameters:

Top P Reasoning Tool Choice Temperature Response Format Max Tokens Tools Include Reasoning
Features

This model supports the following features:

Response Format Reasoning Tools
Performance Summary

MiniMax-M2.1, a lightweight large language model, demonstrates a strong performance profile, particularly in specialized areas. While its speed and pricing are moderate, ranking in the 22nd and 24th percentiles respectively, it exhibits exceptional reliability with a 99% success rate, indicating consistent and dependable operation. The model excels in coding, achieving a remarkable 95.0% accuracy on the Coding (Baseline) benchmark, placing it in the 94th percentile. This is further supported by its leading multilingual coding performance (49.4% on Multi-SWE-Bench and 72.5% on SWE-Bench Multilingual). It also shows strong capabilities in General Knowledge (99.5% accuracy, 75th percentile), Email Classification (99.0% accuracy, 81st percentile), and Reasoning (94.0% accuracy, 84th percentile). Its ability to handle complex reasoning tasks and classify information accurately are notable strengths. However, M2.1 shows some areas for improvement. Its hallucination rate, while not severe, is 90.0% accuracy (40th percentile), suggesting occasional difficulty in acknowledging uncertainty. Instruction Following is also an average performer at 54.0% accuracy (54th percentile). Despite its overall strong performance, the model's duration for Mathematics is notably high, placing it in the 10th percentile for speed on that specific benchmark. Its recommended use of reasoning preservation between turns highlights a potential nuance in optimizing its performance.

Model Pricing

Current Pricing

Feature Price (per 1M tokens)
Prompt $0.3
Completion $1.2
Input Cache Read $0.03
Input Cache Write $0.375

Price History

Available Endpoints
Provider Endpoint Name Context Length Pricing (Input) Pricing (Output)
Minimax
Minimax | minimax/minimax-m2.1 204K $0.3 / 1M tokens $1.2 / 1M tokens
Minimax
Minimax | minimax/minimax-m2.1 204K $0.3 / 1M tokens $2.4 / 1M tokens
Benchmark Results
Benchmark Category Reasoning Strategy Free Executions Accuracy Cost Duration
Other Models by minimax