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Model details

MiniMax M2.1

MiniMax M2.1 represents a second-generation approach from MiniMax, designed as a lightweight yet capable language model that punches above its weight class. With 10 billion activated parameters, it was built specifically for developers who need strong coding accuracy, reliable instruction following, and the ability to manage long-horizon planning tasks. The model targets agentic workflows and modern application development, areas where maintaining coherent task execution across many steps matters most. MiniMax positioned M2.1 as a state-of-the-art option for developers seeking powerful capabilities without the computational overhead of larger models.

The lineage traces back through MiniMax's open-source commitment, with model weights released on Hugging Face for community access and experimentation. The predecessor M2 had already gained traction, ranking at number three on coding leaderboards, suggesting a solid foundation to build upon. Benchmarks such as PinchBench show real-world performance on multi-step execution and tool use, reflecting the model's focus on practical coding scenarios rather than abstract evaluations. This combination of efficient architecture, open accessibility, and demonstrated coding capability makes M2.1 particularly well-suited for developers integrating AI assistance into their development workflows.

MeganovaMiniMaxAI/MiniMax-M2.1minimax

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Provider
Meganova
Model key
MiniMaxAI/MiniMax-M2.1
Release date
Dec 23, 2025
Last updated
Dec 23, 2025
Input modalities
Output modalities
Capabilities

Cost

Input token cost
$0.28
Output token cost
$1.20

Limits

Output tokens
131,072 tokens
Context window
196,608 tokens

Transparent token rates

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Rates are shown per one million tokens. Combined means one million input plus one million output tokens.

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Latest news about MiniMax M2.1

Vercel AI Gateway

Official sourceAnnouncement

On January 22, 2026, MiniMax published a follow-up post on minimax.io titled "MiniMax M2.1: Post-Training Experience and Insights for Agent Models," explicitly naming MiniMax M2.1 as the subject and describing it as the latest flagship open-source model built on further post-training optimization over the M2 generation The same post breaks down M2.1's agentic data synthesis into three categories: real-data-driven synthesis called SWE Scaling, expert-driven synthesis for AppDev, and virtual long-horizon task synthesis called WebExplorer. SWE Scaling is described as leveraging GitHub pull requests and commits as a structured data sourc

Vercel AI Gateway

CoverageBenchmark

Kilo Code's product page at kilo.ai/models/minimax-minimax-m2-1 explicitly names MiniMax-M2.1 as a lightweight, state-of-the-art large language model optimized for coding, agentic workflows, and modern application development, noting that with only 10 billion activated parameters it delivers a major jump in real-world The same page reports Kilo Code's PinchBench (OpenClaw) results for MiniMax-M2.1: an average score of 82.7% across 17 of 50 official models, average time of 19m 51s across 33 runs per OpenClaw task, and average cost of $0.168 per benchmark run. Category breakdown shows the best verified PinchBench v2 run scoring 95.1%

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