--- title: "MiniMax-M1: a 456B reasoning model with a 1-million-token window trained for $534,700" date: 2026-10-21 category: Foundation Models site: NeuroAI canonical: https://neuroai.site/a/na-model-minimax-m1-hybrid-reasoning language: en --- # MiniMax-M1: a 456B reasoning model with a 1-million-token window trained for $534,700 > Shanghai's MiniMax (稀宇科技) open-sourced M1, a 456B hybrid-attention reasoning model with a 1M-token context whose reinforcement-learning phase cost about US$534,700. A lawyer pastes an entire 800-page contract into a model and asks it to trace every clause that contradicts a later amendment. The model reads the whole document in one pass, no chunking, no lost context. That single-shot long read is the headline trick of MiniMax-M1 — and the part that is genuinely hard to copy. ## The bet: long context without a compute blow-up MiniMax-M1 was released by MiniMax (稀宇科技), a Shanghai lab, in mid-June 2025, and open-sourced under the Apache-2.0 license. MiniMax calls it the **first open-weight, large-scale hybrid-attention reasoning model**. The architecture is a **mixture-of-experts (混合专家, MoE)** system: - 456 billion total parameters, about 45.9 billion active per token. - 32 experts with top-2 routing. - A hybrid of **Lightning Attention** (a linear-attention mechanism) and standard softmax attention. The hybrid is the point. Pure linear attention scales cheaply with length but can lose recall; pure softmax attention recalls well but scales badly. MiniMax's design keeps one softmax layer every eight layers so the model remembers, while the linear layers carry the long-range load. The payoff is a **1-million-token context window** — eight times DeepSeek-R1's — with an 80,000-token reasoning output. ## The number everyone quotes: $534,700 The detail that drew engineers' attention was the training cost. MiniMax says the entire reinforcement-learning phase ran on **512 NVIDIA H800 GPUs for three weeks at a rental cost of about US$534,700** — an order of magnitude below what many assumed a frontier-scale RL run would require. Two innovations drove that: - **Lightning Attention** cut the compute needed for long-context reasoning. MiniMax reports that generating 80,000 tokens of reasoning needs only about **30% of the compute of DeepSeek-R1**. - **CISPO**, a reinforcement-learning algorithm (Clipped IS-weight Policy Optimization) that clips importance-sampling weights rather than full policy updates. MiniMax says it converged about twice as fast as alternatives such as ByteDance's DAPO on AIME math tests. The technical report, "MiniMax-M1: Scaling Test-Time Compute Efficiently with Lightning Attention," lays out the method. The efficiency story is credible because it is architectural, not just a smaller model. ## What it is good at MiniMax evaluated M1 on 17 benchmark sets and reported: - **SWE-bench Verified: 55.6% (40k thinking budget) and 56.0% (80k)** — slightly below DeepSeek-R1-0528's 57.6% but above other open-weight models at the time. - **Long-context understanding: ranked second globally** behind Gemini 2.5 Pro, ahead of OpenAI o3 and Claude 4 Opus, per MiniMax's runs. - **τ-bench (agent tool use): led all open-weight models** and beat Gemini 2.5 Pro in MiniMax's evaluation. The pattern is consistent: M1 is strongest where length and tool use matter — reading whole codebases, whole books, or long multi-turn agent sessions — and merely competitive on short math sprints. ## Access and price M1 ships as two checkpoints, M1-40k and M1-80k (different thinking budgets), on Hugging Face (MiniMaxAI/MiniMax-M1-80k) and GitHub under Apache-2.0. Inference is supported by vLLM and SGLang. On MiniMax's API the launch pricing was: - **US$0.40 per million input tokens and US$2.20 per million output tokens** for inputs up to 200K. - **US$1.30 per million input tokens** for the 200K–1M tier (output unchanged) — a tier DeepSeek-R1 did not even support. - Unlimited free use in the MiniMax app and web interface. In RMB terms that is roughly 0.8 to 24 yuan per million tokens depending on length. ## The catch: you still need the memory Efficiency at training and per-token inference does not mean a small model. All 456 billion parameters must sit in GPU memory. Community estimates put a full-precision deployment near 912 GB of VRAM, or about 228 GB quantized to 4-bit — still a multi-GPU, datacenter-class machine. The "cheap" claim is about the math of long context, not the size of the box. ## Honest limitations - **Benchmarks are vendor-reported.** The SWE-bench and τ-bench numbers come from MiniMax's own runs and scaffolding; independent reproduction is limited, and methodology notes show the SWE-bench result used a 486-task subset rather than the full 500. - **Still behind R1 on coding.** At 56.0% versus 57.6% on SWE-bench Verified, M1 trails DeepSeek-R1-0528 on the most-watched coding test. - **Huge to self-host.** 456B parameters mean datacenter hardware regardless of attention efficiency. - **Long context is the differentiator, not raw reasoning.** On short reasoning sprints, several open and closed models score higher; M1 wins on length and agent loops. - **It has been succeeded.** MiniMax has since shipped the M2 family, which is smaller and cheaper to serve; M1 remains the lab's long-context flagship but not its newest model. ## What readers can do now - **Use the free tier for long-document work.** Paste a whole book, contract, or codebase into the MiniMax app or API and test the 1M-token window on a task that breaks shorter-context models. - **Pull the weights for self-hosting.** Grab MiniMaxAI/MiniMax-M1-80k from Hugging Face and serve it through vLLM or SGLang on a multi-GPU box; budget for the 228 GB+ quantized memory footprint. - **Benchmark the cost claim yourself.** Run an 80K-token reasoning task on M1 and a comparable model and compare GPU-hours — the efficiency thesis is the one worth verifying, not the leaderboard scores. --- Published by NeuroAI (https://neuroai.site/) — https://neuroai.site/a/na-model-minimax-m1-hybrid-reasoning Free to quote with attribution and a link to the original.