Crypto news

18.06.2026
06:34

GLM-5.2 vs Claude: Has the Chinese Neural Network Truly Become a "Market Leader Killer"?

A new dispute is heating up in the world of artificial intelligence: Chinese company Z.ai has unveiled the GLM-5.2 model, which some enthusiasts have already dubbed the "killer" of Anthropic's flagship product, Claude Opus 4.8. How justified are these bold claims? Let's take a closer look.

What is GLM-5.2 and what makes it powerful?

GLM-5.2 is a flagship model designed for extended work sessions. Its main advantage over its predecessor, GLM-5.1, is a stable context window of 1 million tokens, up from the previous 200,000. This means the model can keep an entire codebase or large project in view without losing quality.

Key features:

  • 1 million token context without degradation during ultra-long sessions.
  • Two levels of reasoning enhancement: High for a balance of performance and token consumption, Max for maximum capabilities.
  • Open MIT license with no regional restrictions — can be run on your own hardware (self-hosting).
  • API pricing remains at the same level as GLM-5.1.

The model is available on HuggingFace and ModelScope, as well as through the GLM Coding Plan subscription, the ZCode desktop agent, and the Claude Code and OpenCode environments.

What do the benchmarks show?

According to Z.ai's own tests, GLM-5.2 is recognized as the strongest open model on the market. However, in most cases, it does not quite reach the level of Claude Opus 4.8.

On standard programming tests, the gap from GLM-5.1 is noticeable: 81.0 vs. 63.5 on Terminal-Bench 2.1 and 62.1 vs. 58.4 on SWE-bench Pro. At the same time, on Terminal-Bench 2.1, the score of 81.0 comes close to Opus 4.8 (85.0) and surpasses Gemini 3.1 Pro (74.0).

Comparison with competitors in maximum reasoning mode:

BenchmarkGLM-5.2GLM-5.1Opus 4.8GPT-5.5Gemini 3.1 Pro
SWE-bench Pro62.158.469.258.654.2
Terminal-Bench 2.181.063.585.084.074.0
NL2Repo48.942.769.750.733.4
DeepSWE46.218.058.070.010.0
ProgramBench63.750.971.970.839.5
MCP-Atlas76.871.877.875.369.2
Tool-Decathlon48.240.759.955.648.8

The picture is similar for long-horizon tasks. On the FrontierSWE test, where the model manages open technical projects lasting tens of hours, GLM-5.2 lags behind Opus 4.8 by only 1%. However, it outperforms GPT-5.5 and the previous version, Opus 4.7.

How much does the AI cost and what's the catch?

The GLM Coding Plan subscription is divided into three tiers with a 30% annual 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 and 2x off-peak. Until the end of September, a promotion is active where off-peak usage is billed at 1x.

Users are divided in their opinions. Strengths:

  • The strongest open model currently available.
  • Basic logic is noticeably better than version 5.1.
  • Autonomously performs complex tasks through auxiliary agents.
  • Slow but extremely persistent in achieving its goal.

Criticism:

  • Weak cloud infrastructure and expensive pricing.
  • Tendency to get stuck in infinite loops and ignore commands.
  • Many believe the model is tailored exclusively for benchmarks.

Summary: a flagship by benchmarks, but a budget-tier AI for real-world code.

So, is it a "Claude killer" or not?

There is no clear answer. GLM-5.2 is recognized as the best open model for programming and autonomous tasks. In certain long-duration scenarios, it comes very close to Anthropic's flagship. The open MIT license, the ability to run on your own hardware, and the low entry barrier make it a notable player.

However, it is bloggers, not benchmarks, who are calling the new model a "Claude killer." In most tests, Z.ai itself ranks its model below Opus 4.8. Additionally, users complain about unstable cloud infrastructure, high token consumption in Max mode, and weak support. The new AI is closing the gap with the leaders, but it has not yet surpassed them.

My expert conclusion: GLM-5.2 is an impressive step forward for open models, especially in the programming segment. But calling it a "Claude killer" is premature. It is more of a chaser than a leader, and its real value will be determined not by benchmarks, but by its stability and convenience in real-world projects.