GLM-5: Early Reactions and Community Evaluations
Introduction
The recent release of GLM-5 by Z.ai has sparked significant discussion across Twitter and Hacker News. As one of the first major open-weighted models of 2026, GLM-5 represents a substantial technical advancement in AI infrastructure. This article aggregates the early reactions and evaluations from the developer and AI research communities.
What is GLM-5?
According to the official announcement from Z.ai, GLM-5 is specifically designed for complex systems engineering and long-horizon agentic tasks. The model represents a significant scale-up from its predecessor:
- Parameters: 355B total (32B active) → 744B total (40B active)
- Training Data: 23T tokens → 28.5T tokens
This represents a more than 2x increase in total parameters and a 24% increase in training data.
Twitter Reactions
Positive Reception
@ArtificialAnlys, a well-regarded AI benchmarking organization, declared GLM-5 as "the new leading open weights model" in their Intelligence Index. They noted that GLM-5 makes "large gains over GLM-4.7 in GDPval-AA," their agentic benchmark focused on economically valuable work tasks.
Several users expressed enthusiasm, with @iruletheworldmo simply stating: "glm 5 is kinda wild..."—capturing the sentiment that the model represents a significant leap forward.
Infrastructure Focus
While many discussions focused on benchmarks, the most substantive technical commentary highlighted GLM-5's reinforcement learning (RL) infrastructure. Z.ai open-sourced their "slime" async RL training framework, which has powered GLM-4.5, 4.6, 4.7, and now 5.
Criticisms
Not all reactions were positive. @usb_type_d raised concerns about business changes accompanying the GLM-5 release:
"They release GLM 5 and take off the 'get flagship model updates' from the pro plan, not to mention a huge price hike..."
This reflects ongoing tensions in the AI community between accessibility and sustainability of open model development.
Hacker News Discussions
A deep thread on Hacker News (452 comments and 373 points at the time of writing) revealed nuanced perspectives from the developer community.
The "slime" Framework
The most discussed technical aspect was Z.ai's slime async RL training framework. User @jfaganel99 made a compelling argument:
"The thing nobody seems to be talking about here is 'slime,' the async RL training framework they built and open sourced... Everyone's debating benchmarks but honestly the actual gap between frontier and non-frontier models right now is RL infrastructure, not pre-training compute."
This perspective emphasizes that training infrastructure compounds while benchmarks are temporary. The community noted that rollout generation alone consumes 90%+ of RL training time, and GLM's APRIL strategy for handling long-tail problems represents a meaningful contribution to the field.
Licensing Significance
Multiple commenters highlighted the importance of GLM-5 and slime being MIT licensed. This allows unrestricted commercial use and modification, distinguishing it from competitors with more restrictive licenses.
Benchmark Debates
As is typical in AI discussions, there was skepticism about benchmark relevance. One user pointed out that current SOTA models make the classic "pelican riding a bicycle" test obsolete, while others defended certain benchmarks as still useful for differentiating model capabilities.
The discussion reflected broader concerns about scientific rigor in AI evaluation, with some arguing for more standardized, controlled testing approaches rather than creative, ambiguous prompts.
Key Takeaways
Technical Significance
- GLM-5 represents a major step in agentic AI capabilities
- The async RL training framework (slime) may be more impactful than the model itself
- Scale increases are substantial, but training infrastructure efficiency is the real differentiator
Community Concerns
- Pricing changes to Z.ai's pro plan have alienated some users
- Benchmark fatigue and skepticism about meaningful evaluation metrics
- Need for more authentic, standardized testing scenarios
Open Access
- MIT licensing on both model and infrastructure sets it apart from proprietary alternatives
- Open-weighted models continue to challenge the frontier model paradigm
Conclusion
GLM-5's early reception highlights the evolving AI landscape: while raw model scale continues to grow, the community is increasingly focused on training infrastructure and evaluation methodology as the true differentiators. The discussions on Twitter and Hacker News reveal a maturing ecosystem that values both raw capability and the ability to build upon and extend AI systems freely.
As the dust settles on GLM-5's release, the real test will be whether it enables new applications and research directions that weren't possible with previous generations—particularly in long-horizon agentic tasks where the model claims to excel.
Published: February 12, 2026