GLM-4.6 Emerges as Strong Contender in AI Coding, Researchers Divided on AGI Proximity

Beijing, China – The recent launch of GLM-4.6 by Zhipu AI, developed by a relatively small team, has sparked considerable discussion within the artificial intelligence community, particularly regarding its advanced coding capabilities. The model is rapidly gaining recognition, with many developers hailing it as a top-tier solution for programming tasks. This development unfolds amidst ongoing debates among researchers about the immediate future of Artificial General Intelligence (AGI) and varying perceptions of the "AI bubble," especially within Beijing's tech landscape.

GLM-4.6 has garnered significant praise for its performance in coding. According to a tweet by Steve Hsu, the model is considered by many developers to be "the BEST CODING LLM. Period." Users have reported successful application on complex, multi-component projects where other models struggled, and one user noted its perfect performance in refactoring a massive 600-line code file, a task where Claude Sonnet 4.5 reportedly failed. Reviews highlight GLM-4.6's superior coding performance, enhanced context window of 200K tokens, and improved reasoning and agentic capabilities, making it a strong competitor to models like Claude Sonnet 4.5 and GPT-5 in specific areas.

Developed by Chinese company Zhipu AI, GLM-4.6 is an upgrade over its predecessor, GLM-4.5, and is noted for its ability to integrate effectively with various coding tools like Claude Code, Cline, and Kilo Code. The model's weights are publicly available on Hugging Face under an MIT license, positioning it as an open-access model. While some reviews suggest it may not surpass top models from OpenAI or Google in all aspects, particularly reasoning, its coding prowess is frequently highlighted, often at a more accessible price point, with a coding plan starting at $3 per month.

The rapid advancement of models like GLM-4.6 contrasts with a more cautious outlook on AGI from some experts. Steve Hsu's tweet quotes researchers "in the trenches" as saying that AGI is "not near." This sentiment reflects a division within the AI community, where practical, incremental improvements in specialized AI applications are celebrated, even as the timeline for human-level general intelligence remains a subject of intense debate.

Perceptions of the "AI bubble" and AGI also differ significantly in Beijing compared to other global tech hubs. While some areas might view the current AI landscape with speculative concerns, the focus in Beijing appears to be on continuous development and practical application, as evidenced by Zhipu AI's efficient development of GLM-4.6. This regional perspective suggests a strategic emphasis on tangible AI products and capabilities rather than solely on long-term AGI aspirations.