Testing GLM‑5.2: A New High Point for Chinese Coding Models Amid AI Access Restrictions

Earlier this week the U.S. Commerce Department sent Anthropic a notice demanding an immediate suspension of foreign‑citizen access to its Fable 5 and Mythos 5 models, including users abroad, foreign employees in the United States, and Anthropic staff themselves. Anthropic responded by disabling the two models for all users.

The shutdown sparked massive online discussion, with millions of reads on social platforms.

In the same period, Zhipu (智谱) announced the release of GLM 5.2, positioning it as the next milestone for Chinese coding models. The model is delivered through a “Coding Plan” subscription (similar to Claude or GPT subscriptions) and will be made available via API next week, with the public launch timed at 5:21 PM to mirror the time Anthropic received its notice.

The author, who had been using Claude Fable 5 for code generation, quickly switched to GLM 5.2 after the shutdown. Initial impressions highlighted GLM 5.2’s strong solution‑building, architecture design, and output completeness, noting that for most tasks it performed on par with Claude Fable 5 and Opus 4.8, except for design‑heavy tasks where it lagged.

Key strengths observed include low hallucination, stable behavior, a 1 M token context window, and outputs that are easy to understand. The main drawback is the lack of multimodal capability and slower inference due to limited compute resources in China.

To illustrate practical ability, the author gave GLM 5.2 a monitoring‑bug scenario: a third‑party API account had run out of funds, causing missed alerts. GLM 5.2 reasoned through the symptoms, identified the likely cause, and suggested checking the API account balance.

The debugging session lasted 21 minutes; the reasoning path was almost identical to Claude Opus 4.8, but GLM 5.2 was roughly twice as slow, a difference the author attributes to infrastructure and compute limitations rather than model quality.

For a larger task, the author asked GLM 5.2 to transform the AIHOT website into a WeChat mini‑program. After providing the project directory and documentation, the model asked two clarification questions, then generated a development plan and launched four parallel agents. The entire mini‑program was built in about 40 minutes, with only minor UI issues (missing tab‑bar background and small layout bugs) that were manually fixed.

Further, the model was used to create a simple Three.js‑based online game camp. The functionality worked reliably, but the visual polish was lacking compared with Claude‑generated prototypes.

Finally, the author tested GLM 5.2’s skill‑building workflow by recreating a previously built “clean‑computer” skill. The result was comparable to the skill generated by Opus 4.8.

Overall, the author concludes that GLM 5.2 delivers a surprisingly strong coding and agent experience, matching Opus 4.8 for most tasks when compute constraints are ignored. The model’s main limitations are the absence of multimodal input and a less refined UI output. The author recommends using GLM 5.2 together with the Claude Code framework for any agent‑or‑coding work, and DeepSeek V4 Pro for general knowledge or writing tasks.