\n
![](\"https:\/\/images.ctfassets.net\/jdtwqhzvc2n1\/5ooQmDHIK8joIBWGcWySzH\/e0d4c547081630465c4b8862570d0fd1\/cfr0z3n_extremely_small_tiny_figurine_of_a_humanoid_robot_weari_47d6d5f6-f57a-4685-b6aa-d28c2657eef8.png?w=300&q=30\")
<\/p>\n
\n<\/p>\n
Watch out, DeepSeek and Qwen! There's a new king of open source large language models (LLMs), especially when it comes to something enterprises are increasingly valuing: agentic tool use \u2014 that is, the ability to go off and use other software capabilities like web search or bespoke applications \u2014 without much human guidance. <\/p>\n
That model is none other than MiniMax-M2<\/b>, the latest LLM from the Chinese startup of the same name. And in a big win for enterprises globally, the model is available under a permissive, enterprise-friendly MIT License, meaning it is made available freely for developers to take, deploy, retrain, and use how they see fit \u2014 even for commercial purposes. It can be found on Hugging Face, GitHub and ModelScope, as well as through MiniMax's API here. It supports OpenAI and Anthropic API standards, as well, making it easy for customers of said proprietary AI startups to shift out their models to MiniMax's API, if they want.<\/p>\n
According to independent evaluations by Artificial Analysis, a third-party generative AI model benchmarking and research organization, M2 now ranks first among all open-weight systems worldwide on the Intelligence Index\u2014a composite measure of reasoning, coding, and task-execution performance. <\/p>\n
In agentic benchmarks that measure how well a model can plan, execute, and use external tools\u2014skills that power coding assistants and autonomous agents\u2014MiniMax\u2019s own reported results, following the Artificial Analysis methodology, show \u03c4\u00b2-Bench 77.2, BrowseComp 44.0, and FinSearchComp-global 65.5. <\/p>\n
These scores place it at or near the level of top proprietary systems like GPT-5 (thinking) and Claude Sonnet 4.5, making MiniMax-M2 the highest-performing open model yet released for real-world agentic and tool-calling tasks.<\/b><\/p>\nWhat It Means For Enterprises and the AI Race<\/b><\/h3>\n
Built around an efficient Mixture-of-Experts (MoE) architecture, MiniMax-M2 delivers high-end capability for agentic and developer workflows while remaining practical for enterprise deployment.<\/p>\n
For technical decision-makers, the release marks an important turning point for open models in business settings. MiniMax-M2 combines frontier-level reasoning with a manageable activation footprint\u2014just 10 billion active parameters out of 230 billion total. <\/p>\n
This design enables enterprises to operate advanced reasoning and automation workloads on fewer GPUs, achieving near-state-of-the-art results without the infrastructure demands or licensing costs associated with proprietary frontier systems.<\/p>\n
Artificial Analysis\u2019 data show that MiniMax-M2\u2019s strengths go beyond raw intelligence scores. The model leads or closely trails top proprietary systems such as GPT-5 (thinking) and Claude Sonnet 4.5 across benchmarks for end-to-end coding, reasoning, and agentic tool use. <\/p>\n
Its performance in \u03c4\u00b2-Bench, SWE-Bench, and BrowseComp indicates particular advantages for organizations that depend on AI systems capable of planning, executing, and verifying complex workflows\u2014key functions for agentic and developer tools inside enterprise environments.<\/p>\n
As LLM engineer Pierre-Carl Langlais aka Alexander Doria posted on X: "MiniMax [is] making a case for mastering the technology end-to-end to get actual agentic automation."<\/p>\n
MiniMax-M2\u2019s technical architecture is a sparse Mixture-of-Experts model with 230 billion total parameters and 10 billion active per inference. <\/p>\n
This configuration significantly reduces latency and compute requirements while maintaining broad general intelligence. <\/p>\n
The design allows for responsive agent loops\u2014compile\u2013run\u2013test or browse\u2013retrieve\u2013cite cycles\u2014that execute faster and more predictably than denser models.<\/p>\n
For enterprise technology teams, this means easier scaling, lower cloud costs, and reduced deployment friction. <\/b>According to Artificial Analysis, the model can be served efficiently on as few as four NVIDIA H100 GPUs at FP8 precision<\/b>, a setup well within reach for mid-size organizations or departmental AI clusters.<\/p>\nBenchmark Leadership Across Agentic and Coding Workflows<\/b><\/h3>\n
MiniMax\u2019s benchmark suite highlights strong real-world performance across developer and agent environments. The figure below, released with the model, compares MiniMax-M2 (in red) with several leading proprietary and open models, including GPT-5 (thinking), Claude Sonnet 4.5, Gemini 2.5 Pro, and DeepSeek-V3.2.<\/p>\n
MiniMax-M2 achieves top or near-top performance in many categories:<\/p>\n
SWE-bench Verified: 69.4 \u2014 close to GPT-5\u2019s 74.9<\/p>\n<\/li>\n
ArtifactsBench: 66.8 \u2014 above Claude Sonnet 4.5 and DeepSeek-V3.2<\/p>\n<\/li>\n
\u03c4\u00b2-Bench: 77.2 \u2014 approaching GPT-5\u2019s 80.1<\/p>\n<\/li>\n
GAIA (text only): 75.7 \u2014 surpassing DeepSeek-V3.2<\/p>\n<\/li>\n
BrowseComp: 44.0 \u2014 notably stronger than other open models<\/p>\n<\/li>\n
FinSearchComp-global: 65.5 \u2014 best among tested open-weight systems<\/p>\n<\/li>\n<\/ul>\n
These results show MiniMax-M2\u2019s capability in executing complex, tool-augmented tasks across multiple languages and environments\u2014skills increasingly relevant for automated support, R&D, and data analysis inside enterprises.<\/p>\n
The model\u2019s overall intelligence profile is confirmed in the latest Artificial Analysis Intelligence Index v3.0<\/b>, which aggregates performance across ten reasoning benchmarks including MMLU-Pro, GPQA Diamond, AIME 2025, IFBench, and \u03c4\u00b2-Bench Telecom.<\/p>\n
MiniMax-M2 scored 61 points<\/b>, ranking as the highest open-weight model globally and following closely behind GPT-5 (high) and Grok 4. <\/p>\n
Artificial Analysis highlighted the model\u2019s balance between technical accuracy, reasoning depth, and applied intelligence across domains. For enterprise users, this consistency indicates a reliable model foundation suitable for integration into software engineering, customer support, or knowledge automation systems.<\/p>\n
MiniMax engineered M2 for end-to-end developer workflows, enabling multi-file code edits, automated testing, and regression repair directly within integrated development environments or CI\/CD pipelines. <\/p>\n
The model also excels in agentic planning\u2014handling tasks that combine web search, command execution, and API calls while maintaining reasoning traceability.<\/p>\n
These capabilities make MiniMax-M2 especially valuable for enterprises exploring autonomous developer agents, data analysis assistants, or AI-augmented operational tools. <\/p>\n
Benchmarks such as Terminal-Bench and BrowseComp demonstrate the model\u2019s ability to adapt to incomplete data and recover gracefully from intermediate errors, improving reliability in production settings.<\/p>\n
A distinctive aspect of MiniMax-M2 is its interleaved thinking format, which maintains visible reasoning traces between <think>…<\/think> tags.<\/p>\n
This enables the model to plan and verify steps across multiple exchanges, a critical feature for agentic reasoning. MiniMax advises retaining these segments when passing conversation history to preserve the model\u2019s logic and continuity.<\/p>\n
The company also provides a Tool Calling Guide on Hugging Face, detailing how developers can connect external tools and APIs via structured XML-style calls. <\/p>\n
This functionality allows MiniMax-M2 to serve as the reasoning core for larger agent frameworks, executing dynamic tasks such as search, retrieval, and computation through external functions.<\/p>\n
Enterprises can access the model through the MiniMax Open Platform API and MiniMax Agent interface (a web chat similar to ChatGPT), both currently free for a limited time.<\/p>\n
MiniMax recommends SGLang and vLLM for efficient serving, each offering day-one support for the model\u2019s unique interleaved reasoning and tool-calling structure. <\/p>\n
Deployment guides and parameter configurations are available through MiniMax\u2019s documentation.<\/p>\n
As Artificial Analysis noted, MiniMax\u2019s API pricing is set at $0.30 per million input tokens<\/b> and $1.20 per million output tokens<\/b>, among the most competitive in the open-model ecosystem. <\/p>\n Provider<\/b><\/p>\n<\/td>\n Model (doc link)<\/b><\/p>\n<\/td>\n Input $\/1M<\/b><\/p>\n<\/td>\n Output $\/1M<\/b><\/p>\n<\/td>\n Notes<\/b><\/p>\n<\/td>\n<\/tr>\n MiniMax<\/p>\n<\/td>\n MiniMax-M2<\/p>\n<\/td>\n $0.30<\/b><\/p>\n<\/td>\n $1.20<\/b><\/p>\n<\/td>\n Listed under \u201cChat Completion v2\u201d for M2. <\/p>\n<\/td>\n<\/tr>\n OpenAI<\/p>\n<\/td>\n GPT-5<\/p>\n<\/td>\n $1.25<\/b><\/p>\n<\/td>\n $10.00<\/b><\/p>\n<\/td>\n Flagship model pricing on OpenAI\u2019s API pricing page. <\/p>\n<\/td>\n<\/tr>\n OpenAI<\/p>\n<\/td>\n GPT-5 mini<\/p>\n<\/td>\n $0.25<\/b><\/p>\n<\/td>\n $2.00<\/b><\/p>\n<\/td>\n Cheaper tier for well-defined tasks. <\/p>\n<\/td>\n<\/tr>\n Anthropic<\/p>\n<\/td>\n Claude Sonnet 4.5<\/p>\n<\/td>\n $3.00<\/b><\/p>\n<\/td>\n $15.00<\/b><\/p>\n<\/td>\n Anthropic\u2019s current per-MTok list; long-context (>200K input) uses a premium tier. <\/p>\n<\/td>\n<\/tr>\n Google<\/p>\n<\/td>\n Gemini 2.5 Flash (Preview)<\/p>\n<\/td>\n $0.30<\/b><\/p>\n<\/td>\n $2.50<\/b><\/p>\n<\/td>\n Prices include \u201cthinking tokens\u201d; page also lists cheaper Flash-Lite and 2.0 tiers. <\/p>\n<\/td>\n<\/tr>\n xAI<\/p>\n<\/td>\n Grok-4 Fast (reasoning)<\/p>\n<\/td>\n $0.20<\/b><\/p>\n<\/td>\n $0.50<\/b><\/p>\n<\/td>\n \u201cFast\u201d tier; xAI also lists Grok-4 at $3 \/ $15. <\/p>\n<\/td>\n<\/tr>\n DeepSeek<\/p>\n<\/td>\n DeepSeek-V3.2 (chat)<\/p>\n<\/td>\n $0.28<\/b><\/p>\n<\/td>\n $0.42<\/b><\/p>\n<\/td>\n Cache-hit input is $0.028; table shows per-model details. <\/p>\n<\/td>\n<\/tr>\n Qwen (Alibaba)<\/p>\n<\/td>\n qwen-flash (Model Studio)<\/p>\n<\/td>\n from $0.022<\/b><\/p>\n<\/td>\n from $0.216<\/b><\/p>\n<\/td>\n Tiered by input size (\u2264128K, \u2264256K, \u22641M tokens); listed \u201cInput price \/ Output price per 1M\u201d. <\/p>\n<\/td>\n<\/tr>\n Cohere<\/p>\n<\/td>\n Command R+ (Aug 2024)<\/p>\n<\/td>\n $2.50<\/b><\/p>\n<\/td>\n $10.00<\/b><\/p>\n<\/td>\n First-party pricing page also lists Command R ($0.50 \/ $1.50) and others. <\/p>\n<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n Notes & caveats (for readers):<\/b><\/p>\n Prices are USD per million<\/b> tokens and can change; check linked pages for updates and region\/endpoint nuances (e.g., Anthropic long-context >200K input, Google Live API variants, cache discounts). <\/p>\n<\/li>\n Vendors may bill extra for server-side tools (web search, code execution) or offer batch\/context-cache discounts. <\/p>\n<\/li>\n<\/ul>\n While the model produces longer, more explicit reasoning traces, its sparse activation and optimized compute design help maintain a favorable cost-performance balance\u2014an advantage for teams deploying interactive agents or high-volume automation systems.<\/p>\n MiniMax has quickly become one of the most closely watched names in China\u2019s fast-rising AI sector. <\/p>\n Backed by Alibaba and Tencent, the company moved from relative obscurity to international recognition within a year\u2014first through breakthroughs in AI video generation, then through a series of open-weight large language models (LLMs) aimed squarely at developers and enterprises.<\/p>\n The company first captured global attention in late 2024 with its AI video generation tool, \u201cvideo-01,\u201d which demonstrated the ability to create dynamic, cinematic scenes in seconds. VentureBeat described how the model\u2019s launch sparked widespread interest after online creators began sharing lifelike, AI-generated footage\u2014most memorably, a viral clip of a Star Wars<\/i> lightsaber duel that drew millions of views in under two days. <\/p>\n CEO Yan Junjie emphasized that the system outperformed leading Western tools in generating human movement and expression, an area where video AIs often struggle. The product, later commercialized through MiniMax\u2019s Hailuo<\/i> platform, showcased the startup\u2019s technical confidence and creative reach, helping to establish China as a serious contender in generative video technology.<\/p>\n By early 2025, MiniMax had turned its attention to long-context language modeling, unveiling the MiniMax-01 series, including MiniMax-Text-01 and MiniMax-VL-01. These open-weight models introduced an unprecedented 4-million-token context window, doubling the reach of Google\u2019s Gemini 1.5 Pro and dwarfing OpenAI\u2019s GPT-4o by more than twentyfold. <\/p>\n The company continued its rapid cadence with the MiniMax-M1 release in June 2025, a model focused on long-context reasoning and reinforcement learning efficiency. M1 extended context capacity to 1 million tokens and introduced a hybrid Mixture-of-Experts design trained using a custom reinforcement-learning algorithm known as CISPO. Remarkably, VentureBeat reported that MiniMax trained M1 at a total cost of about $534,700, roughly one-tenth of DeepSeek\u2019s R1 and far below the multimillion-dollar budgets typical for frontier-scale models. <\/p>\n For enterprises and technical teams, MiniMax\u2019s trajectory signals the arrival of a new generation of cost-efficient, open-weight models designed for real-world deployment. Its open licensing\u2014ranging from Apache 2.0 to MIT\u2014gives businesses freedom to customize, self-host, and fine-tune without vendor lock-in or compliance restrictions. <\/p>\n Features such as structured function calling, long-context retention, and high-efficiency attention architectures directly address the needs of engineering groups managing multi-step reasoning systems and data-intensive pipelines.<\/p>\n As MiniMax continues to expand its lineup, the company has emerged as a key global innovator in open-weight AI, combining ambitious research with pragmatic engineering. <\/p>\n The release of MiniMax-M2 reinforces the growing leadership of Chinese AI research groups in open-weight model development. <\/p>\n Following earlier contributions from DeepSeek, Alibaba\u2019s Qwen series, and Moonshot AI, MiniMax\u2019s entry continues the trend toward open, efficient systems designed for real-world use. <\/p>\n Artificial Analysis observed that MiniMax-M2 exemplifies a broader shift in focus toward agentic capability and reinforcement-learning refinement, prioritizing controllable reasoning and real utility over raw model size.<\/p>\n For enterprises, this means access to a state-of-the-art open model that can be audited, fine-tuned, and deployed internally with full transparency. <\/p>\n By pairing strong benchmark performance with open licensing and efficient scaling, MiniMaxAI positions MiniMax-M2 as a practical foundation for intelligent systems that think, act, and assist with traceable logic\u2014making it one of the most enterprise-ready open AI models available today.<\/p>\n\n\n
\n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n \n
Background on MiniMax \u2014 an Emerging Chinese Powerhouse<\/b><\/h3>\n
Open-Weight Leadership and Industry Context<\/b><\/h3>\n