z.ai’s open source GLM-5 achieves record low hallucination rate and leverages new RL ‘slime’ technique

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.