MiniMax M1 80K is China's long-context specialist — processing up to 1 million tokens with MiniMax's Lightning Attention, purpose-built for tasks that require reading entire books, codebases, and document libraries.
MiniMax-M1 (80k) is an AI model released May 2025 by MiniMax, a leading organization in large language model research. MiniMax M1 reasoning model — linear attention architecture enabling very long context at scale with strong reasoning. With 456B total (45.9B active via MoE) and a 1M tokens context window, MiniMax-M1 (80k) is designed to excel across a range of demanding tasks — from software engineering and mathematical problem-solving to multilingual content generation and long-document analysis.
MiniMax-M1 (80k) uses a dense transformer architecture with 456B total (45.9B active via MoE) of parameters, all of which are active for every input token. Dense architectures provide predictable, consistent behavior across diverse task types — there are no "cold" experts to warm up, and quality is uniform across prompts. MiniMax's training pipeline combined large-scale pretraining on Large-scale multilingual pretraining with reinforcement learning with supervised fine-tuning and alignment techniques (RLHF or DPO), producing MiniMax-M1 (80k)'s characteristic balance of capability, helpfulness, and safety.
As of 2026-10-16, MiniMax-M1 (80k) by MiniMax demonstrates measurable performance on standard AI evaluation benchmarks. On SWE-bench Verified — which tests ability to resolve real GitHub issues in production Python repositories — it achieves 56.4%. For software engineering tasks, MiniMax-M1 (80k) scores 56.4% on SWE-bench Verified, which tests ability to resolve real GitHub issues in production Python repositories. In mathematical reasoning, MiniMax-M1 (80k) records 76.0% on AIME 2025. Across all 4 benchmarks tracked, the model shows consistent capability in its primary task domains. These scores place MiniMax-M1 (80k) — at 456B total (45.9B active via MoE) of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
The primary strengths of MiniMax-M1 (80k) are: 1 million token context — entire software repositories in one session; Lightning Attention: linear-complexity for 1M tokens without quadratic memory costs; 56.4% SWE-bench at long-context scale; MiniMax Model License for research and commercial use. These make it particularly well-suited for Entire-repository code analysis, Multi-book research synthesis, Long-running agentic workflows. Users working on complex, multi-step problems benefit most from MiniMax-M1 (80k)'s ability to maintain coherence across long inputs and produce structured, verifiable outputs.
Like all current AI systems, MiniMax-M1 (80k) has limitations: Lightning Attention can underperform for precise long-range dependencies; 1M context at scale needs specialized infrastructure. For production deployments, validating outputs against authoritative sources — especially for domain-specific or time-sensitive information — remains best practice. MiniMax continues to improve MiniMax-M1 (80k) through ongoing research.
MiniMax-M1 (80k) is available on LITHOVEX AI with a free account. Unlike direct API access — which requires billing setup, API key management, and per-token charges — LITHOVEX AI provides a seamless browser interface with no setup required, making MiniMax-M1 (80k) accessible to students, researchers, developers, and early-stage teams globally.
As of 2026-10-16, MiniMax-M1 (80k) by MiniMax demonstrates measurable performance on standard AI evaluation benchmarks. On SWE-bench Verified — which tests ability to resolve real GitHub issues in production Python repositories — it achieves 56.4%. For software engineering tasks, MiniMax-M1 (80k) scores 56.4% on SWE-bench Verified, which tests ability to resolve real GitHub issues in production Python repositories. In mathematical reasoning, MiniMax-M1 (80k) records 76.0% on AIME 2025. Across all 4 benchmarks tracked, the model shows consistent capability in its primary task domains. These scores place MiniMax-M1 (80k) — at 456B total (45.9B active via MoE) of parameters — in a competitive position within its model class. Note that benchmark scores reflect controlled evaluation conditions; real-world performance varies by task, prompt design, and domain specificity.
Hybrid: Lightning Attention + Softmax Attention, 456B total / 45.9B active
The training pipeline for MiniMax-M1 (80k) involves multiple stages: large-scale pretraining on Large-scale multilingual pretraining with reinforcement learning to develop broad world knowledge, followed by supervised fine-tuning on instruction-response pairs, and alignment techniques such as RLHF or DPO. Each stage refines the model's helpfulness, accuracy, and safety. MiniMax's research team — based in China — has optimized MiniMax-M1 (80k) for the specific capability profile described above.
Inference efficiency: MiniMax-M1 (80k) at 456B total (45.9B active via MoE) is designed to deliver quality outputs at practical serving speeds. Its 1M tokens context window enables processing entire documents, multi-file codebases, and extended conversation histories — particularly valuable for contract review, technical documentation analysis, and codebase-level AI assistance.
Getting started with MiniMax-M1 (80k) on LITHOVEX AI takes under two minutes. Follow these steps:
For best results with MiniMax-M1 (80k): provide specific, context-rich prompts; include examples when generating code; specify output format when structure matters; break complex tasks into focused sub-questions. Since MiniMax-M1 (80k) supports extended reasoning, prompting it to "think step by step" or "show your reasoning" activates deeper chain-of-thought analysis for math, logic, and complex problems.
Processing entire legal case files and case libraries
Full codebase analysis for large software projects
Book-length document summarization and analysis
Long-term conversation memory for enterprise AI assistants
Research synthesis across hundreds of academic papers
MiniMax is a Shanghai-based AI company founded in 2021, notable for pioneering "Lightning Attention" — a linear-complexity attention mechanism enabling processing of up to 1 million tokens without the quadratic memory costs of standard attention. The MiniMax-M1 and M2 model families leverage this architecture for combined long-context understanding and strong reasoning. MiniMax operates consumer AI products in China and has positioned itself as a technical innovator in attention mechanism design. The company has raised over $600 million in funding and serves millions of users through its Talkie and other consumer applications.
Funding: Raised $600M+; Tencent, HongShan
Founded: 2021, headquartered in China.
MiniMax-M1 (80k) by MiniMax competes in a field with several strong alternatives. Below is a detailed side-by-side comparison against commonly listed alternatives, using benchmark data and capability profiles in the catalog.
Both MiniMax-M1 (80k) and the models above are available free on LITHOVEX AI — test them side by side on your real tasks before committing to a subscription with any provider.
MiniMax-M1 (80k) is released under MiniMax Model License, which means you can download and run it on your own hardware. Below are the quickest ways to get started.
LITHOVEX AI runs MiniMax-M1 (80k) on cloud infrastructure, so you can use it instantly in your browser without downloading anything, configuring drivers, or managing VRAM. Create a free account and start in under 60 seconds.
MiniMax M1 is built on Lightning Attention — a linearized attention mechanism that makes 1M token contexts practical without the quadratic memory costs of standard transformers. This makes it uniquely accessible in Asia, where long legal, government, and business documents are the norm.
MiniMax-M1 (80k) by MiniMax has the following version history and notable updates:
For the complete changelog and latest release notes, visit the official MiniMax model page.