MiniMax-M1: China's open-source AI with 1M context tokens

Chinese AI startup MiniMax has made a bold move in the global AI race with the release of its newest large language model, MiniMax-M1. Known in the West for its photorealistic AI video model Hailuo, the company is now turning heads with a large language model that not only pushes technical boundaries but is also fully open source under the Apache 2.0 license.

For enterprises and developers, that means no licensing fees, no usage restrictions, and full freedom to customize and deploy the model as they see fit.

Redefining Long-Context Capabilities

The standout feature of MiniMax-M1 is its massive context window: up to 1 million tokens, with a maximum output of 80,000 tokens. In practical terms, this allows the model to hold and reason over an enormous amount of information — from entire codebases to large document collections — in a single session. For comparison:

• OpenAI’s GPT-4o: 128,000-token context window
• Google Gemini 2.5 Pro: 1 million tokens
• MiniMax-M1: 1 million tokens (input) and 80,000 tokens (output)

This positions M1 as a frontrunner for tasks that demand deep contextual memory and long-range reasoning — areas where even top-tier models have historically struggled.

Efficiency Meets Innovation

MiniMax-M1 isn't just big — it’s efficient. Built on a hybrid Mixture-of-Experts (MoE) architecture and optimized with a lightning attention mechanism, M1 is designed to reduce compute overhead. Its efficiency shows: the M1 model reportedly consumes only 25% of the FLOPs required by DeepSeek R1 when generating 100,000 tokens.

Perhaps most impressively, the model was trained for just $534,700, a fraction of what competitors have spent:

• DeepSeek R1: $5–6 million
• OpenAI GPT-4: Over $100 million

This low-cost training is made possible by a novel reinforcement learning algorithm called CISPO, which clips importance sampling weights rather than token updates — a strategy that yields higher efficiency without sacrificing performance.

Model Specs and Variants

MiniMax-M1 comes in two variants:

• M1-40k: Output budget of 40,000 tokens
• M1-80k: Output budget of 80,000 tokens

Under the hood, the architecture features 456 billion parameters, with 45.9 billion activated per token, based on the company’s earlier MiniMax-Text-01 foundation model.

Competitive Benchmarking

On several established benchmarks, MiniMax-M1 demonstrates strong performance in reasoning, coding, and long-context tasks:

• AIME 2024 (Math reasoning): 86.0%
• LiveCodeBench: 65.0%
• SWE-bench Verified: 56.0%
• TAU-bench: 62.8%
• OpenAI MRCR (4-needle version): 73.4%

These results outpace many other open-weight models like DeepSeek-R1 and Qwen3-235B-A22B, and begin to close the gap with leading closed-weight systems like GPT-4o and Gemini 2.5 Pro.

Developer-Friendly Deployment

MiniMax supports standard deployment tools such as:

• vLLM — recommended for memory efficiency and large batch handling
• Transformers library — for compatibility with existing model pipelines

The M1 also supports structured function calling and includes APIs for chatbot deployment, online search, image/video generation, speech synthesis, and voice cloning, enabling a wide range of agentic applications.

Why It Matters for Enterprises

For CTOs, engineering managers, and AI leads, MiniMax-M1 offers an unusually attractive mix of scalability, performance, and cost-efficiency:

• Engineering teams can build copilots, agents, and reasoning tools using a model with rich context capabilities and high throughput — without budget blowouts.
• Data infrastructure teams benefit from open deployment options and toolchain compatibility.
• Security-conscious organizations can explore on-premises use cases, avoiding cloud dependencies and minimizing data exposure.
• Innovation teams get a frontier-grade LLM at a fraction of typical development or licensing costs.

A New Era of Accessible Frontier Models

The release of MiniMax-M1 signals a major step forward in the democratization of high-performance AI. With its combination of open access, long-context processing, and cost-efficient architecture, MiniMax is setting a new benchmark for what’s possible — and affordable — in open-source AI.

As MiniMaxWeek unfolds, with more product releases expected, the company appears poised to become a global force in both enterprise AI and developer ecosystems.

Whether you're building internal copilots, customer-facing assistants, or complex decision-support systems, MiniMax-M1 offers a compelling, enterprise-grade foundation — without the proprietary lock-in.