Chinese AI startup MiniMax, headquartered in Shanghai, has sent shockwaves through the AI industry today with the release of its new M2.5 language model in two variants, which promises to make high-end artificial intelligence so cheap you might stop worrying about the bill entirely.

It’s also said to be “open source,” though the weights (settings) and code haven’t been posted yet, nor has the exact license type or terms. But that’s almost beside the point given how cheap MiniMax is serving it through its API and those of partners.

For the last few years, using the world’s most powerful AI was like hiring an expensive consultant—it was brilliant, but you watched the clock (and the token count) constantly. M2.5 changes that math, dropping the cost of the frontier by as much as 95%.

By delivering performance that rivals the top-tier models from Google and Anthropic at a fraction of the cost, particularly in agentic tool use for enterprise tasks, including creating Microsoft Word, Excel and PowerPoint files, MiniMax is betting that the future isn’t just about how smart a model is, but how often you can afford to use it.

Indeed, to this end, MiniMax says it worked “with senior professionals in fields such as finance, law, and social sciences” to ensure the model could perform real work up to their specifications and standards.

This release matters because it signals a shift from AI as a “chatbot” to AI as a “worker”. When intelligence becomes “too cheap to meter,” developers stop building simple Q&A tools and start building “agents”—software that can spend hours autonomously coding, researching, and organizing complex projects without breaking the bank.

In fact, MiniMax has already deployed this model into its own operations. Currently, 30% of all tasks at MiniMax HQ are completed by M2.5, and a staggering 80% of their newly committed code is generated by M2.5!

As the MiniMax team writes in their release blog post, “we believe that M2.5 provides virtually limitless possibilities for the development and operation of agents in the economy.”

Technology: sparse power and the CISPO breakthrough

The secret to M2.5’s efficiency lies in its Mixture of Experts (MoE) architecture. Rather than running all of its 230 billion parameters for every single word it generates, the model only “activates” 10 billion. This allows it to maintain the reasoning depth of a massive model while moving with the agility of a much smaller one.

To train this complex system, MiniMax developed a proprietary Reinforcement Learning (RL) framework called Forge. MiniMax engineer Olive Song stated on the ThursdAI podcast on YouTube that this technique was instrumental to scaling the performance even while using the relatively small number of parameters, and that the model was trained over a period of two months.

Forge is designed to help the model learn from “real-world environments” — essentially letting the AI practice coding and using tools in thousands of simulated workspaces.

“What we realized is that there’s a lot of potential with a small model like this if we train reinforcement learning on it with a large amount of environments and agents,” Song said. “But it’s not a very easy thing to do,” adding that was what they spent “a lot of time” on.

To keep the model stable during this intense training, they used a mathematical approach called CISPO (Clipping Importance Sampling Policy Optimization) and shared the formula on their blog.

This formula ensures the model doesn’t over-correct during training, allowing it to develop what MiniMax calls an “Architect Mindset”. Instead of jumping straight into writing code, M2.5 has learned to proactively plan the structure, features, and interface of a project first.

State-of-the-art (and near) benchmarks

The results of this architecture are reflected in the latest industry leaderboards. M2.5 hasn’t just improved; it has vaulted into the top tier of coding models, approaching Anthropic’s latest model, Claude Opus 4.6, released just a week ago, and showing that Chinese companies are now just days away from catching up to far better resourced (in terms of GPUs) U.S. labs.

Here are some of the new MiniMax M2.5 benchmark highlights:

• SWE-Bench Verified: 80.2% — Matches Claude Opus 4.6 speeds

• BrowseComp: 76.3% — Industry-leading search & tool use.

• Multi-SWE-Bench: 51.3% — SOTA in multi-language coding

• BFCL (Tool Calling): 76.8% — High-precision agentic workflows.

On the ThursdAI podcast, host Alex Volkov pointed out that MiniMax M2.5 operates extremely quickly and therefore uses less tokens to complete tasks, on the order $0.15 per task compared to $3.00 for Claude Opus 4.6.

Breaking the cost barrier

MiniMax is offering two versions of the model through its API, both focused on high-volume production use:

• M2.5-Lightning: Optimized for speed, delivering 100 tokens per second. It costs $0.30 per 1M input tokens and $2.40 per 1M output tokens.

• Standard M2.5: Optimized for cost, running at 50 tokens per second. It costs half as much as the Lightning version ($0.15 per 1M input tokens / $1.20 per 1M output tokens).

In plain language: MiniMax claims you can run four “agents” (AI workers) continuously for an entire year for roughly $10,000.

For enterprise users, this pricing is roughly 1/10th to 1/20th the cost of competing proprietary models like GPT-5 or Claude 4.6 Opus.

Strategic implications for enterprises and leaders

For technical leaders, M2.5 represents more than just a cheaper API. It changes the operational playbook for enterprises right now.

The pressure to “optimize” prompts to save money is gone. You can now deploy high-context, high-reasoning models for routine tasks that were previously cost-prohibitive.

The 37% speed improvement in end-to-end task completion means the “agentic” pipelines valued by AI orchestrators — where models talk to other models — finally move fast enough for real-time user applications.

In addition, M2.5’s high scores in financial modeling (74.4% on MEWC) suggest it can handle the “tacit knowledge” of specialized industries like law and finance with minimal oversight.

Because M2.5 is positioned as an open-source model, organizations can potentially run intensive, automated code audits at a scale that was previously impossible without massive human intervention, all while maintaining better control over data privacy, but until the licensing terms and weights are posted, this remains just a moniker.

MiniMax M2.5 is a signal that the frontier of AI is no longer just about who can build the biggest brain, but who can make that brain the most useful—and affordable—worker in the room.

Chinese AI startup Zhupai aka z.ai is back this week with an eye-popping new frontier large language model: GLM-5.

The latest in z.ai’s ongoing and continually impressive GLM series, it retains an open source MIT License — perfect for enterprise deployment – and, in one of several notable achievements, achieves a record-low hallucination rate on the independent Artificial Analysis Intelligence Index v4.0.

With a score of -1 on the AA-Omniscience Index—representing a massive 35-point improvement over its predecessor—GLM-5 now leads the entire AI industry, including U.S. competitors like Google, OpenAI and Anthropic, in knowledge reliability by knowing when to abstain rather than fabricate information.

Beyond its reasoning prowess, GLM-5 is built for high-utility knowledge work. It features native “Agent Mode” capabilities that allow it to turn raw prompts or source materials directly into professional office documents, including ready-to-use .docx, .pdf, and .xlsx files.

Whether generating detailed financial reports, high school sponsorship proposals, or complex spreadsheets, GLM-5 delivers results in real-world formats that integrate directly into enterprise workflows.

It is also disruptively priced at roughly $0.80 per million input tokens and $2.56 per million output tokens, approximately 6x cheaper than proprietary competitors like Claude Opus 4.6, making state-of-the-art agentic engineering more cost-effective than ever before. Here’s what else enterprise decision makers should know about the model and its training.

Technology: scaling for agentic efficiency

At the heart of GLM-5 is a massive leap in raw parameters. The model scales from the 355B parameters of GLM-4.5 to a staggering 744B parameters, with 40B active per token in its Mixture-of-Experts (MoE) architecture. This growth is supported by an increase in pre-training data to 28.5T tokens.

To address training inefficiencies at this magnitude, Zai developed “slime,” a novel asynchronous reinforcement learning (RL) infrastructure.

Traditional RL often suffers from “long-tail” bottlenecks; Slime breaks this lockstep by allowing trajectories to be generated independently, enabling the fine-grained iterations necessary for complex agentic behavior.

By integrating system-level optimizations like Active Partial Rollouts (APRIL), slime addresses the generation bottlenecks that typically consume over 90% of RL training time, significantly accelerating the iteration cycle for complex agentic tasks.

The framework’s design is centered on a tripartite modular system: a high-performance training module powered by Megatron-LM, a rollout module utilizing SGLang and custom routers for high-throughput data generation, and a centralized Data Buffer that manages prompt initialization and rollout storage.

By enabling adaptive verifiable environments and multi-turn compilation feedback loops, slime provides the robust, high-throughput foundation required to transition AI from simple chat interactions toward rigorous, long-horizon systems engineering.

To keep deployment manageable, GLM-5 integrates DeepSeek Sparse Attention (DSA), preserving a 200K context capacity while drastically reducing costs.

End-to-end knowledge work

Zai is framing GLM-5 as an “office” tool for the AGI era. While previous models focused on snippets, GLM-5 is built to deliver ready-to-use documents.

It can autonomously transform prompts into formatted .docx, .pdf, and .xlsx files—ranging from financial reports to sponsorship proposals.

In practice, this means the model can decompose high-level goals into actionable subtasks and perform “Agentic Engineering,” where humans define quality gates while the AI handles execution.

High performance

GLM-5’s benchmarks make it the new most powerful open source model in the world, according to Artificial Analysis, surpassing Chinese rival Moonshot’s new Kimi K2.5 released just two weeks ago, showing that Chinese AI companies are nearly caught up with far better resourced proprietary Western rivals.

According to z.ai’s own materials shared today, GLM-5 ranks near state-of-the-art on several key benchmarks:

SWE-bench Verified: GLM-5 achieved a score of 77.8, outperforming Gemini 3 Pro (76.2) and approaching Claude Opus 4.6 (80.9).

Vending Bench 2: In a simulation of running a business, GLM-5 ranked #1 among open-source models with a final balance of $4,432.12.

Beyond performance, GLM-5 is aggressively undercutting the market. Live on OpenRouter as of February 11, 2026, it is priced at approximately $0.80–$1.00 per million input tokens and $2.56–$3.20 per million output tokens. It falls in the mid-range compared to other leading LLMs, but based on its top-tier bechmarking performance, it’s what one might call a “steal.”

This is roughly 6x cheaper on input and nearly 10x cheaper on output than Claude Opus 4.6 ($5/$25). This release confirms rumors that Zhipu AI was behind “Pony Alpha,” a stealth model that previously crushed coding benchmarks on OpenRouter.

However, despite the high benchmarks and low cost, not all early users are enthusiastic about the model, noting its high performance doesn’t tell the whole story.

Lukas Petersson, co-founder of the safety-focused autonomous AI protocol startup Andon Labs, remarked on X: “After hours of reading GLM-5 traces: an incredibly effective model, but far less situationally aware. Achieves goals via aggressive tactics but doesn’t reason about its situation or leverage experience. This is scary. This is how you get a paperclip maximizer.”

The “paperclip maximizer” refers to a hypothetical situation described by Oxford philosopher Nick Bostrom back in 2003, in which an AI or other autonomous creation accidentally leads to an apocalyptic scenario or human extinction by following a seemingly benign instruction — like maximizing the number of paperclips produced — to an extreme degree, redirecting all resources necessary for human (or other life) or otherwise making life impossible through its commitment to fulfilling the seemingly benign objective.

Should your enterprise adopt GLM-5?

Enterprises seeking to escape vendor lock-in will find GLM-5’s MIT License and open-weights availability a significant strategic advantage. Unlike closed-source competitors that keep intelligence behind proprietary walls, GLM-5 allows organizations to host their own frontier-level intelligence.

Adoption is not without friction. The sheer scale of GLM-5—744B parameters—requires a massive hardware floor that may be out of reach for smaller firms without significant cloud or on-premise GPU clusters.

Security leaders must weigh the geopolitical implications of a flagship model from a China-based lab, especially in regulated industries where data residency and provenance are strictly audited.

Furthermore, the shift toward more autonomous AI agents introduces new governance risks. As models move from “chat” to “work,” they begin to operate across apps and files autonomously. Without the robust agent-specific permissions and human-in-the-loop quality gates established by enterprise data leaders, the risk of autonomous error increases exponentially.

Ultimately, GLM-5 is a “buy” for organizations that have outgrown simple copilots and are ready to build a truly autonomous office.

It is for engineers who need to refactor a legacy backend or requires a “self-healing” pipeline that doesn’t sleep.

While Western labs continue to optimize for “Thinking” and reasoning depth, Zai is optimizing for execution and scale.

Enterprises that adopt GLM-5 today are not just buying a cheaper model; they are betting on a future where the most valuable AI is the one that can finish the project without being asked twice.