z.ai's open supply GLM-5 achieves document low hallucination price and leverages new RL 'slime' approach

Chinese language AI startup Zhupai aka z.ai is again this week with an eye-popping new frontier massive language mannequin: GLM-5.

The newest in z.ai's ongoing and regularly spectacular GLM sequence, it retains an open supply MIT License — good for enterprise deployment – and, in considered one of a number of notable achievements, achieves a record-low hallucination price on the impartial Synthetic Evaluation Intelligence Index v4.0.

With a rating of -1 on the AA-Omniscience Index—representing an enormous 35-point enchancment over its predecessor—GLM-5 now leads your entire AI business, together with U.S. rivals like Google, OpenAI and Anthropic, in information reliability by understanding when to abstain slightly than fabricate data.

Past its reasoning prowess, GLM-5 is constructed for high-utility information work. It options native "Agent Mode" capabilities that permit it to show uncooked prompts or supply supplies immediately into skilled workplace paperwork, together with ready-to-use .docx, .pdf, and .xlsx recordsdata.

Whether or not producing detailed monetary stories, highschool sponsorship proposals, or complicated spreadsheets, GLM-5 delivers ends in real-world codecs that combine immediately into enterprise workflows.

It’s also disruptively priced at roughly $0.80 per million enter tokens and $2.56 per million output tokens, roughly 6x cheaper than proprietary rivals like Claude Opus 4.6, making state-of-the-art agentic engineering less expensive than ever earlier than. Right here's what else enterprise determination makers ought to know in regards to the mannequin and its coaching.

Know-how: scaling for agentic effectivity

On the coronary heart of GLM-5 is an enormous leap in uncooked parameters. The mannequin scales from the 355B parameters of GLM-4.5 to a staggering 744B parameters, with 40B lively per token in its Combination-of-Specialists (MoE) structure. This development is supported by a rise in pre-training knowledge to twenty-eight.5T tokens.

To handle coaching inefficiencies at this magnitude, Zai developed "slime," a novel asynchronous reinforcement studying (RL) infrastructure.

Conventional RL usually suffers from "long-tail" bottlenecks; Slime breaks this lockstep by permitting trajectories to be generated independently, enabling the fine-grained iterations vital for complicated agentic conduct.

By integrating system-level optimizations like Lively Partial Rollouts (APRIL), slime addresses the era bottlenecks that usually eat over 90% of RL coaching time, considerably accelerating the iteration cycle for complicated agentic duties.

The framework’s design is centered on a tripartite modular system: a high-performance coaching module powered by Megatron-LM, a rollout module using SGLang and customized routers for high-throughput knowledge era, and a centralized Knowledge Buffer that manages immediate initialization and rollout storage.

By enabling adaptive verifiable environments and multi-turn compilation suggestions loops, slime gives the strong, high-throughput basis required to transition AI from easy chat interactions towards rigorous, long-horizon techniques engineering.

To maintain deployment manageable, GLM-5 integrates DeepSeek Sparse Consideration (DSA), preserving a 200K context capability whereas drastically decreasing prices.

Finish-to-end information work

Zai is framing GLM-5 as an "workplace" instrument for the AGI period. Whereas earlier fashions centered on snippets, GLM-5 is constructed to ship ready-to-use paperwork.

It could autonomously remodel prompts into formatted .docx, .pdf, and .xlsx recordsdata—starting from monetary stories to sponsorship proposals.

In observe, this implies the mannequin can decompose high-level objectives into actionable subtasks and carry out "Agentic Engineering," the place people outline high quality gates whereas the AI handles execution.

Excessive efficiency

GLM-5’s benchmarks make it the brand new strongest open supply mannequin on this planet, in line with Synthetic Evaluation, surpassing Chinese language rival Moonshot's new Kimi K2.5 launched simply two weeks in the past, exhibiting that Chinese language AI firms are practically caught up with much better resourced proprietary Western rivals.

Based on z.ai's personal supplies shared right now, GLM-5 ranks close to state-of-the-art on a number of key benchmarks:

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

Merchandising Bench 2: In a simulation of working a enterprise, GLM-5 ranked #1 amongst open-source fashions with a closing stability of $4,432.12.

Past efficiency, GLM-5 is aggressively undercutting the market. Stay on OpenRouter as of February 11, 2026, it’s priced at roughly $0.80–$1.00 per million enter tokens and $2.56–$3.20 per million output tokens. It falls within the mid-range in comparison with different main LLMs, however based mostly on its top-tier bechmarking efficiency, it's what one would possibly name a "steal."

That is roughly 6x cheaper on enter and practically 10x cheaper on output than Claude Opus 4.6 ($5/$25). This launch confirms rumors that Zhipu AI was behind "Pony Alpha," a stealth mannequin that beforehand crushed coding benchmarks on OpenRouter.

Nevertheless, regardless of the excessive benchmarks and low price, not all early customers are enthusiastic in regards to the mannequin, noting its excessive efficiency doesn't inform the entire story.

Lukas Petersson, co-founder of the safety-focused autonomous AI protocol startup Andon Labs, remarked on X: "After hours of studying GLM-5 traces: an extremely efficient mannequin, however far much less situationally conscious. Achieves objectives by way of aggressive ways however doesn't motive about its scenario or leverage expertise. That is scary. That is the way you get a paperclip maximizer."

The "paperclip maximizer" refers to a hypothetical scenario described by Oxford thinker Nick Bostrom again in 2003, during which an AI or different autonomous creation unintentionally results in an apocalyptic situation or human extinction by following a seemingly benign instruction — like maximizing the variety of paperclips produced — to an excessive diploma, redirecting all sources vital for human (or different life) or in any other case making life unimaginable by means of its dedication to fulfilling the seemingly benign goal.

Ought to your enterprise undertake GLM-5?

Enterprises in search of to flee vendor lock-in will discover GLM-5’s MIT License and open-weights availability a big strategic benefit. Not like closed-source rivals that maintain intelligence behind proprietary partitions, GLM-5 permits organizations to host their very own frontier-level intelligence.

Adoption will not be with out friction. The sheer scale of GLM-5—744B parameters—requires an enormous {hardware} ground which may be out of attain for smaller corporations with out vital cloud or on-premise GPU clusters.

Safety leaders should weigh the geopolitical implications of a flagship mannequin from a China-based lab, particularly in regulated industries the place knowledge residency and provenance are strictly audited.

Moreover, the shift towards extra autonomous AI brokers introduces new governance dangers. As fashions transfer from "chat" to "work," they start to function throughout apps and recordsdata autonomously. With out the strong agent-specific permissions and human-in-the-loop high quality gates established by enterprise knowledge leaders, the danger of autonomous error will increase exponentially.

In the end, GLM-5 is a "purchase" for organizations which have outgrown easy copilots and are able to construct a very autonomous workplace.

It’s for engineers who must refactor a legacy backend or requires a "self-healing" pipeline that doesn't sleep.

Whereas Western labs proceed to optimize for "Considering" and reasoning depth, Zai is optimizing for execution and scale.

Enterprises that undertake GLM-5 right now aren’t simply shopping for a less expensive mannequin; they’re betting on a future the place essentially the most useful AI is the one that may end the mission with out being requested twice.

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