GLM-5.2: A Real Competitor to Claude or Just Hype Around a Chinese Newcomer?
A serious intrigue is brewing in the world of artificial intelligence. Chinese company Z.ai has released its flagship model, GLM-5.2, and the developer community is already buzzing with discussions about whether this AI has become a true "killer" of the recognized leader—Anthropic's Claude. Let's figure out how justified such bold claims are and what this new product really represents.
What is GLM-5.2 and what are its main advantages?
The developers position GLM-5.2 as a model tailored for long, multi-hour work sessions. Its key advantage over its predecessor, GLM-5.1, is a giant leap in context window size. It now stands at 1 million tokens, which is five times larger than the previous version. This means the model can simultaneously "hold in mind" vast arrays of code or text without losing the thread of reasoning.
Key features of the model:
- 1 million token context without degradation during ultra-long sessions. The entire project codebase can be loaded into a single reasoning cycle.
- Two levels of reasoning enhancement: High for balancing performance and token consumption, and Max for maximum depth and accuracy, but with higher costs.
- Open MIT license without regional restrictions. This allows developers to run the model on their own hardware (self-hosting), which is critically important for many companies.
- API price remains at the level of the previous version, GLM-5.1, making it accessible.
The model is already available on HuggingFace and ModelScope, as well as through the GLM Coding Plan subscription and the ZCode desktop agent.
Numbers don't lie: what do the benchmarks show?
According to Z.ai's internal tests, GLM-5.2 is the strongest open model on the market. However, it still falls short of Anthropic's flagship, Claude Opus 4.8, in most scenarios. The gap has narrowed, but not disappeared.
On standard programming tests, the progress compared to GLM-5.1 is impressive: 81.0 vs. 63.5 on Terminal-Bench 2.1 and 62.1 vs. 58.4 on SWE-bench Pro. On Terminal-Bench 2.1, the score of 81.0 closely approaches Opus 4.8 (85.0) and surpasses Gemini 3.1 Pro (74.0).
Comparison in maximum reasoning mode (Max) on key tests:
| Benchmark | GLM-5.2 | GLM-5.1 | Opus 4.8 | GPT-5.5 | Gemini 3.1 Pro |
| SWE-bench Pro | 62.1 | 58.4 | 69.2 | 58.6 | 54.2 |
| Terminal-Bench 2.1 | 81.0 | 63.5 | 85.0 | 84.0 | 74.0 |
| NL2Repo | 48.9 | 42.7 | 69.7 | 50.7 | 33.4 |
| DeepSWE | 46.2 | 18.0 | 58.0 | 70.0 | 10.0 |
| ProgramBench | 63.7 | 50.9 | 71.9 | 70.8 | 39.5 |
| MCP-Atlas | 76.8 | 71.8 | 77.8 | 75.3 | 69.2 |
| Tool-Decathlon | 48.2 | 40.7 | 59.9 | 55.6 | 48.8 |
On long-horizon tasks, the picture is similar. On the FrontierSWE test, where the model manages open technical projects for tens of hours, GLM-5.2 lags behind Opus 4.8 by only 1%. However, it surpasses GPT-5.5 and the previous version, Opus 4.7. On SWE-Marathon with tasks like creating compilers, the gap from Opus 4.8 is 13%. Thus, on all tests, GLM-5.2 shows the best result among open models.
How much does the AI cost and what's the catch?
The GLM Coding Plan subscription is divided into three tiers with prices for annual payment at a 30% discount: Lite — $12.6/month, Pro — $50.4/month, Max — $112/month. Within the subscription, quota consumption depends on load: a 3x multiplier during peak hours (from 2:00 PM to 6:00 PM Beijing time) and 2x off-peak. Until the end of September, a promotion is active where off-peak usage is billed at 1x.
User reviews are divided. Strengths: the model is called the strongest open neural network tested so far. Basic logic is noticeably better than version 5.1, and in programming, the model is comparable to GPT-5.5 at a high reasoning level. The AI autonomously performs complex tasks through auxiliary agents and itself suggests fixing noticed inconsistencies.
However, criticism primarily concerns the service and stability. The cloud infrastructure, despite a good mathematical model, is described as extremely weak. Developers complain about expensive billing and poor support, noting that it's easier to pay for Claude or GPT. The neural network is criticized for a tendency to get stuck in infinite loops and ignore commands. According to users, the model is tailored exclusively for benchmarks.
My professional opinion
GLM-5.2 is undoubtedly an important step forward for open models. It demonstrates that