Be careful, DeepSeek and Qwen! There's a brand new king of open supply massive language fashions (LLMs), particularly in the case of one thing enterprises are more and more valuing: agentic instrument use — that’s, the power to go off and use different software program capabilities like internet search or bespoke functions — with out a lot human steerage.
That mannequin is none apart from MiniMax-M2, the most recent LLM from the Chinese language startup of the identical title. And in an enormous win for enterprises globally, the mannequin is offered beneath a permissive, enterprise-friendly MIT License, which means it’s made obtainable freely for builders to take, deploy, retrain, and use how they see match — even for business functions. It may be discovered on Hugging Face, GitHub and ModelScope, in addition to by way of MiniMax's API right here. It helps OpenAI and Anthropic API requirements, as nicely, making it straightforward for purchasers of stated proprietary AI startups to shift out their fashions to MiniMax's API, if they need.
Based on impartial evaluations by Synthetic Evaluation, a third-party generative AI mannequin benchmarking and analysis group, M2 now ranks first amongst all open-weight methods worldwide on the Intelligence Index—a composite measure of reasoning, coding, and task-execution efficiency.
In agentic benchmarks that measure how nicely a mannequin can plan, execute, and use exterior instruments—abilities that energy coding assistants and autonomous brokers—MiniMax’s personal reported outcomes, following the Synthetic Evaluation methodology, present τ²-Bench 77.2, BrowseComp 44.0, and FinSearchComp-global 65.5.
These scores place it at or close to the extent of prime proprietary methods like GPT-5 (considering) and Claude Sonnet 4.5, making MiniMax-M2 the highest-performing open mannequin but launched for real-world agentic and tool-calling duties.
What It Means For Enterprises and the AI Race
Constructed round an environment friendly Combination-of-Consultants (MoE) structure, MiniMax-M2 delivers high-end functionality for agentic and developer workflows whereas remaining sensible for enterprise deployment.
For technical decision-makers, the discharge marks an vital turning level for open fashions in enterprise settings. MiniMax-M2 combines frontier-level reasoning with a manageable activation footprint—simply 10 billion lively parameters out of 230 billion whole.
This design permits enterprises to function superior reasoning and automation workloads on fewer GPUs, attaining near-state-of-the-art outcomes with out the infrastructure calls for or licensing prices related to proprietary frontier methods.
Synthetic Evaluation’ information present that MiniMax-M2’s strengths transcend uncooked intelligence scores. The mannequin leads or carefully trails prime proprietary methods resembling GPT-5 (considering) and Claude Sonnet 4.5 throughout benchmarks for end-to-end coding, reasoning, and agentic instrument use.
Its efficiency in τ²-Bench, SWE-Bench, and BrowseComp signifies specific benefits for organizations that rely on AI methods able to planning, executing, and verifying advanced workflows—key features for agentic and developer instruments inside enterprise environments.
As LLM engineer Pierre-Carl Langlais aka Alexander Doria posted on X: "MiniMax [is] making a case for mastering the expertise end-to-end to get precise agentic automation."
Compact Design, Scalable Efficiency
MiniMax-M2’s technical structure is a sparse Combination-of-Consultants mannequin with 230 billion whole parameters and 10 billion lively per inference.
This configuration considerably reduces latency and compute necessities whereas sustaining broad common intelligence.
The design permits for responsive agent loops—compile–run–take a look at or browse–retrieve–cite cycles—that execute quicker and extra predictably than denser fashions.
For enterprise expertise groups, this implies simpler scaling, decrease cloud prices, and diminished deployment friction. Based on Synthetic Evaluation, the mannequin might be served effectively on as few as 4 NVIDIA H100 GPUs at FP8 precision, a setup nicely inside attain for mid-size organizations or departmental AI clusters.
Benchmark Management Throughout Agentic and Coding Workflows
MiniMax’s benchmark suite highlights robust real-world efficiency throughout developer and agent environments. The determine beneath, launched with the mannequin, compares MiniMax-M2 (in pink) with a number of main proprietary and open fashions, together with GPT-5 (considering), Claude Sonnet 4.5, Gemini 2.5 Professional, and DeepSeek-V3.2.
MiniMax-M2 achieves prime or near-top efficiency in lots of classes:
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SWE-bench Verified: 69.4 — near GPT-5’s 74.9
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ArtifactsBench: 66.8 — above Claude Sonnet 4.5 and DeepSeek-V3.2
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τ²-Bench: 77.2 — approaching GPT-5’s 80.1
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GAIA (textual content solely): 75.7 — surpassing DeepSeek-V3.2
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BrowseComp: 44.0 — notably stronger than different open fashions
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FinSearchComp-global: 65.5 — finest amongst examined open-weight methods
These outcomes present MiniMax-M2’s functionality in executing advanced, tool-augmented duties throughout a number of languages and environments—abilities more and more related for automated assist, R&D, and information evaluation inside enterprises.
Sturdy Exhibiting in Synthetic Evaluation’ Intelligence Index
The mannequin’s total intelligence profile is confirmed within the newest Synthetic Evaluation Intelligence Index v3.0, which aggregates efficiency throughout ten reasoning benchmarks together with MMLU-Professional, GPQA Diamond, AIME 2025, IFBench, and τ²-Bench Telecom.
MiniMax-M2 scored 61 factors, rating as the very best open-weight mannequin globally and following carefully behind GPT-5 (excessive) and Grok 4.
Synthetic Evaluation highlighted the mannequin’s stability between technical accuracy, reasoning depth, and utilized intelligence throughout domains. For enterprise customers, this consistency signifies a dependable mannequin basis appropriate for integration into software program engineering, buyer assist, or data automation methods.
Designed for Builders and Agentic Methods
MiniMax engineered M2 for end-to-end developer workflows, enabling multi-file code edits, automated testing, and regression restore straight inside built-in growth environments or CI/CD pipelines.
The mannequin additionally excels in agentic planning—dealing with duties that mix internet search, command execution, and API calls whereas sustaining reasoning traceability.
These capabilities make MiniMax-M2 particularly helpful for enterprises exploring autonomous developer brokers, information evaluation assistants, or AI-augmented operational instruments.
Benchmarks resembling Terminal-Bench and BrowseComp reveal the mannequin’s potential to adapt to incomplete information and recuperate gracefully from intermediate errors, bettering reliability in manufacturing settings.
Interleaved Considering and Structured Instrument Use
A particular side of MiniMax-M2 is its interleaved considering format, which maintains seen reasoning traces between <suppose>…</suppose> tags.
This allows the mannequin to plan and confirm steps throughout a number of exchanges, a important function for agentic reasoning. MiniMax advises retaining these segments when passing dialog historical past to protect the mannequin’s logic and continuity.
The corporate additionally gives a Instrument Calling Information on Hugging Face, detailing how builders can join exterior instruments and APIs by way of structured XML-style calls.
This performance permits MiniMax-M2 to function the reasoning core for bigger agent frameworks, executing dynamic duties resembling search, retrieval, and computation by way of exterior features.
Open Supply Entry and Enterprise Deployment Choices
Enterprises can entry the mannequin by way of the MiniMax Open Platform API and MiniMax Agent interface (an internet chat just like ChatGPT), each at the moment free for a restricted time.
MiniMax recommends SGLang and vLLM for environment friendly serving, every providing day-one assist for the mannequin’s distinctive interleaved reasoning and tool-calling construction.
Deployment guides and parameter configurations can be found by way of MiniMax’s documentation.
Value Effectivity and Token Economics
As Synthetic Evaluation famous, MiniMax’s API pricing is ready at $0.30 per million enter tokens and $1.20 per million output tokens, among the many best within the open-model ecosystem.
|
Supplier |
Mannequin (doc hyperlink) |
Enter $/1M |
Output $/1M |
Notes |
|
MiniMax |
$0.30 |
$1.20 |
Listed beneath “Chat Completion v2” for M2. |
|
|
OpenAI |
$1.25 |
$10.00 |
Flagship mannequin pricing on OpenAI’s API pricing web page. |
|
|
OpenAI |
$0.25 |
$2.00 |
Cheaper tier for well-defined duties. |
|
|
Anthropic |
$3.00 |
$15.00 |
Anthropic’s present per-MTok record; long-context (>200K enter) makes use of a premium tier. |
|
|
|
$0.30 |
$2.50 |
Costs embody “considering tokens”; web page additionally lists cheaper Flash-Lite and a couple of.0 tiers. |
|
|
xAI |
$0.20 |
$0.50 |
“Quick” tier; xAI additionally lists Grok-4 at $3 / $15. |
|
|
DeepSeek |
$0.28 |
$0.42 |
Cache-hit enter is $0.028; desk exhibits per-model particulars. |
|
|
Qwen (Alibaba) |
from $0.022 |
from $0.216 |
Tiered by enter measurement (≤128K, ≤256K, ≤1M tokens); listed “Enter value / Output value per 1M”. |
|
|
Cohere |
$2.50 |
$10.00 |
First-party pricing web page additionally lists Command R ($0.50 / $1.50) and others. |
Notes & caveats (for readers):
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Costs are USD per million tokens and might change; examine linked pages for updates and area/endpoint nuances (e.g., Anthropic long-context >200K enter, Google Reside API variants, cache reductions).
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Distributors could invoice additional for server-side instruments (internet search, code execution) or provide batch/context-cache reductions.
Whereas the mannequin produces longer, extra specific reasoning traces, its sparse activation and optimized compute design assist preserve a positive cost-performance stability—a bonus for groups deploying interactive brokers or high-volume automation methods.
Background on MiniMax — an Rising Chinese language Powerhouse
MiniMax has rapidly develop into some of the carefully watched names in China’s fast-rising AI sector.
Backed by Alibaba and Tencent, the corporate moved from relative obscurity to worldwide recognition inside a yr—first by way of breakthroughs in AI video technology, then by way of a collection of open-weight massive language fashions (LLMs) aimed squarely at builders and enterprises.
The corporate first captured international consideration in late 2024 with its AI video technology instrument, “video-01,” which demonstrated the power to create dynamic, cinematic scenes in seconds. VentureBeat described how the mannequin’s launch sparked widespread curiosity after on-line creators started sharing lifelike, AI-generated footage—most memorably, a viral clip of a Star Wars lightsaber duel that drew tens of millions of views in beneath two days.
CEO Yan Junjie emphasised that the system outperformed main Western instruments in producing human motion and expression, an space the place video AIs typically wrestle. The product, later commercialized by way of MiniMax’s Hailuo platform, showcased the startup’s technical confidence and artistic attain, serving to to ascertain China as a severe contender in generative video expertise.
By early 2025, MiniMax had turned its consideration to long-context language modeling, unveiling the MiniMax-01 collection, together with MiniMax-Textual content-01 and MiniMax-VL-01. These open-weight fashions launched an unprecedented 4-million-token context window, doubling the attain of Google’s Gemini 1.5 Professional and dwarfing OpenAI’s GPT-4o by greater than twentyfold.
The corporate continued its fast cadence with the MiniMax-M1 launch in June 2025, a mannequin centered on long-context reasoning and reinforcement studying effectivity. M1 prolonged context capability to 1 million tokens and launched a hybrid Combination-of-Consultants design educated utilizing a customized reinforcement-learning algorithm generally known as CISPO. Remarkably, VentureBeat reported that MiniMax educated M1 at a complete price of about $534,700, roughly one-tenth of DeepSeek’s R1 and much beneath the multimillion-dollar budgets typical for frontier-scale fashions.
For enterprises and technical groups, MiniMax’s trajectory alerts the arrival of a brand new technology of cost-efficient, open-weight fashions designed for real-world deployment. Its open licensing—starting from Apache 2.0 to MIT—provides companies freedom to customise, self-host, and fine-tune with out vendor lock-in or compliance restrictions.
Options resembling structured operate calling, long-context retention, and high-efficiency consideration architectures straight tackle the wants of engineering teams managing multi-step reasoning methods and data-intensive pipelines.
As MiniMax continues to develop its lineup, the corporate has emerged as a key international innovator in open-weight AI, combining formidable analysis with pragmatic engineering.
Open-Weight Management and Business Context
The discharge of MiniMax-M2 reinforces the rising management of Chinese language AI analysis teams in open-weight mannequin growth.
Following earlier contributions from DeepSeek, Alibaba’s Qwen collection, and Moonshot AI, MiniMax’s entry continues the development towards open, environment friendly methods designed for real-world use.
Synthetic Evaluation noticed that MiniMax-M2 exemplifies a broader shift in focus towards agentic functionality and reinforcement-learning refinement, prioritizing controllable reasoning and actual utility over uncooked mannequin measurement.
For enterprises, this implies entry to a state-of-the-art open mannequin that may be audited, fine-tuned, and deployed internally with full transparency.
By pairing robust benchmark efficiency with open licensing and environment friendly scaling, MiniMaxAI positions MiniMax-M2 as a sensible basis for clever methods that suppose, act, and help with traceable logic—making it some of the enterprise-ready open AI fashions obtainable right now.
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