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M1 is an open-weight providing that units new requirements in long-context reasoning, agentic instrument use, and environment friendly compute efficiency. It’s out there immediately on the AI code sharing group Hugging Face and Microsoft’s rival code sharing group GitHub, the primary launch of what the corporate dubbed as “MiniMaxWeek” from its social account on X — with additional product bulletins anticipated.
MiniMax-M1 distinguishes itself with a context window of 1 million enter tokens and as much as 80,000 tokens in output, positioning it as one of the expansive fashions out there for long-context reasoning duties.
The “context window” in massive language fashions (LLMs) refers back to the most variety of tokens the mannequin can course of at one time — together with each enter and output. Tokens are the essential models of textual content, which can embody whole phrases, components of phrases, punctuation marks, or code symbols. These tokens are transformed into numerical vectors that the mannequin makes use of to signify and manipulate that means by way of its parameters (weights and biases). They’re, in essence, the LLM’s native language.
For comparability, OpenAI’s GPT-4o has a context window of solely 128,000 tokens — sufficient to change a few novel’s value of knowledge between the person and the mannequin in a single forwards and backwards interplay. At 1 million tokens, MiniMax-M1 might change a small assortment or ebook sequence’ value of knowledge. Google Gemini 2.5 Professional presents a token context higher restrict of 1 million, as effectively, with a reported 2 million window within the works.
However M1 has one other trick up its sleeve: it’s been skilled utilizing reinforcement studying in an progressive, resourceful, extremely environment friendly method. The mannequin is skilled utilizing a hybrid Combination-of-Consultants (MoE) structure with a lightning consideration mechanism designed to cut back inference prices.
In accordance with the technical report, MiniMax-M1 consumes solely 25% of the floating level operations (FLOPs) required by DeepSeek R1 at a technology size of 100,000 tokens.
Structure and variants
The mannequin is available in two variants—MiniMax-M1-40k and MiniMax-M1-80k—referring to their “thinking budgets” or output lengths.
The structure is constructed on the corporate’s earlier MiniMax-Textual content-01 basis and contains 456 billion parameters, with 45.9 billion activated per token.
A standout characteristic of the discharge is the mannequin’s coaching price. MiniMax studies that the M1 mannequin was skilled utilizing large-scale reinforcement studying (RL) at an effectivity hardly ever seen on this area, with a complete price of $534,700.
This effectivity is credited to a customized RL algorithm referred to as CISPO, which clips significance sampling weights relatively than token updates, and to the hybrid consideration design that helps streamline scaling.
That’s an astonishingly “cheap” quantity for a frontier LLM, as DeepSeek skilled its hit R1 reasoning mannequin at a reported price of $5-$6 million, whereas the coaching price of OpenAIs’ GPT-4 — a greater than two-year-old mannequin now — was stated to exceed $100 million. This price comes from each the worth of graphics processing models (GPUs), the massively parallel computing {hardware} primarily manufactured by firms like Nvidia, which might price $20,000–$30,000 or extra per module, and from the power required to run these chips repeatedly in large-scale information facilities.
Benchmark efficiency
MiniMax-M1 has been evaluated throughout a sequence of established benchmarks that take a look at superior reasoning, software program engineering, and tool-use capabilities.
On AIME 2024, a arithmetic competitors benchmark, the M1-80k mannequin scores 86.0% accuracy. It additionally delivers sturdy efficiency in coding and long-context duties, attaining:
65.0% on LiveCodeBench
56.0% on SWE-bench Verified
62.8% on TAU-bench
73.4% on OpenAI MRCR (4-needle model)
These outcomes place MiniMax-M1 forward of different open-weight opponents comparable to DeepSeek-R1 and Qwen3-235B-A22B on a number of complicated duties.
Whereas closed-weight fashions like OpenAI’s o3 and Gemini 2.5 Professional nonetheless prime some benchmarks, MiniMax-M1 narrows the efficiency hole significantly whereas remaining freely accessible underneath an Apache-2.0 license.
For deployment, MiniMax recommends vLLM because the serving backend, citing its optimization for giant mannequin workloads, reminiscence effectivity, and batch request dealing with. The corporate additionally supplies deployment choices utilizing the Transformers library.
MiniMax-M1 contains structured perform calling capabilities and is packaged with a chatbot API that includes on-line search, video and picture technology, speech synthesis, and voice cloning instruments. These options intention to assist broader agentic habits in real-world functions.
Implications for technical decision-makers and enterprise patrons
MiniMax-M1’s open entry, long-context capabilities, and compute effectivity handle a number of recurring challenges for technical professionals answerable for managing AI methods at scale.
For engineering leads answerable for the complete lifecycle of LLMs — comparable to optimizing mannequin efficiency and deploying underneath tight timelines — MiniMax-M1 presents a decrease operational price profile whereas supporting superior reasoning duties. Its lengthy context window might considerably cut back preprocessing efforts for enterprise paperwork or log information that span tens or lots of of hundreds of tokens.
For these managing AI orchestration pipelines, the flexibility to fine-tune and deploy MiniMax-M1 utilizing established instruments like vLLM or Transformers helps simpler integration into present infrastructure. The hybrid-attention structure might assist simplify scaling methods, and the mannequin’s aggressive efficiency on multi-step reasoning and software program engineering benchmarks presents a high-capability base for inside copilots or agent-based methods.
From an information platform perspective, groups answerable for sustaining environment friendly, scalable infrastructure can profit from M1’s assist for structured perform calling and its compatibility with automated pipelines. Its open-source nature permits groups to tailor efficiency to their stack with out vendor lock-in.
Safety leads can also discover worth in evaluating M1’s potential for safe, on-premises deployment of a high-capability mannequin that doesn’t depend on transmitting delicate information to third-party endpoints.
Taken collectively, MiniMax-M1 presents a versatile choice for organizations trying to experiment with or scale up superior AI capabilities whereas managing prices, staying inside operational limits, and avoiding proprietary constraints.
The discharge alerts MiniMax’s continued concentrate on sensible, scalable AI fashions. By combining open entry with superior structure and compute effectivity, MiniMax-M1 might function a foundational mannequin for builders constructing next-generation functions that require each reasoning depth and long-range enter understanding.
We’ll be monitoring MiniMax’s different releases all through the week. Keep tuned!
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