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Zhipu AI raises plan prices by 30% as demand grows

Chinese AI startup Zhipu announced a price increase for its GLM coding plan on February 12, citing a rise in user demand.

The company said subscription prices will be raised by at least 30% immediately, but existing subscribers will not be affected.

Zhipu, based in Beijing, develops AI models for coding assistance.

The company did not specify the number of new users or the reason for the demand surge beyond increased usage.

No further details were provided on future pricing or user metrics.

🔗 Source: Reuters

🧠 Food for thought

Implications, context, and why it matters.

Zhipu’s price hike follows demand and CNBC-reported reach into the US market

  • Zhipu investor relations told CNBC that most GLM Coding Plan users are in the United States and China, which it linked to rising demand for the product 1.
  • CNBC wrote that in its test, Zhipu produced a working app faster than some American rivals, although the output looked less refined 1.
  • Zhipu, based in Beijing, said on Feb. 12 it raised GLM coding plan subscription prices by at least 30% with immediate effect, and it said current subscribers would not face the increase 2.

A tougher race in AI developer tools with several claims that need tighter sourcing

  • The uptake of Zhipu’s coding product fits a common playbook where a company starts with a high-value niche such as coding help and then broadens from there, although this framing is analysis rather than a stated plan 1.
  • Some commentary ties Zhipu’s path to U.S. export controls (rules restricting the sale of advanced technologies abroad) and the U.S. Entity List (a U.S. trade blacklist that can limit access to American technology), yet Reuters did not back claims that Zhipu had to retrain models on Chinese hardware such as Huawei Ascend in its Feb. 12 price hike reporting 2.
  • Statements that GLM-4.7 beats GPT-4o on named coding benchmarks, or that this settles any “six months behind” debate, also do not appear in the Reuters pricing report and need clear attribution plus a reliable benchmark source if used 2.

Recent Zhipu developments

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