China has just released a free AI that prioritizes execution over chatter. After months of model testing, a clear shift is emerging. While attention is focused on ChatGPT’s conversational exchanges, Zhipu AI, one of the country’s leading labs, is offering GLM-4 (presented as GLM-4.7 in some demonstrations), an action-oriented model.
Its positioning is clear: concise, targeted, immediately operational answers. Where many Western systems multiply circumlocutions, GLM-4 focuses on the result. You ask him to build, he builds, without unnecessary preamble.
Public demonstrations illustrate this trend:
- A complete prototype of a Pokédex application generated in a single prompt.
- An interactive and rotating 3D visualization of a galaxy, designed with scientifically plausible parameters.
- Structured professional slide presentations, ready to be displayed.
It’s not simply a matter of raw intelligence, but of training philosophy. Western models are optimized for conversation; Chinese models are calibrated for task completion. This results in less verbosity, better adherence to instructions, cleaner structured outputs, and more systematic use of planning and tools.
The involvement is strategic. While the West is refining “conversation partners”, China is pushing “digital workers”. Ask a conversational system to build a deliverable, and it will explain how it does it. Ask GLM-4, and you’ll get the deliverable.
Another key aspect: the distribution policy. GLM-4 is offered in open weights and free of charge (with certain flow limits). This approach serves several purposes: to put pressure on Western API prices, to unite a global ecosystem of developers and to shift expectations from “AI that we talk to” to “AI that we work with”. It is about shaping the standard of use as much as the technology.
A delicate observation: while some players charge subscriptions for smoother conversations, China is advancing agent-oriented models capable of performing tasks. GLM-4 thus establishes itself among the most competent open weight models in the Chinese ecosystem, competing with LLaMA 3.1, Qwen 2.5 and DeepSeek-V3.
Practical consequences:
- For product designers, free access to agentic AI reduces the barrier to automation.
- For professionals, testing these models can improve efficiency on targeted tasks.
In summary, two trajectories are emerging: AI focused on exchange, and AI focused on production. The first excels at conversation; the second emphasizes effective construction. The gap between these approaches is widening, with tangible effects on how artificial intelligence is designed, deployed and used on a daily basis.
In the short term, teams will be able to compare costs, latency and quality of execution according to technological channels. In the medium term, competition in agentization and tool-based integration is expected to intensify. For decision-makers, the challenge is no longer just the quality of dialogue, but the ability to quickly deliver reliable, reusable and auditable artifacts, on a large scale, in various contexts.