Introducing GLM-4.6: Challenging Claude Sonnet with Upgraded Coding and Reasoning Capabilities

Zhipu AI has officially launched its latest flagship model, GLM-4.6, which not only expands the context window to 200,000 tokens but also demonstrates amazing leaps in code generation, complex reasoning, and Agent capabilities. This article will provide an in-depth analysis of its performance evaluation, a comparison with top models like Claude Sonnet 4, and how to get started with GLM-4.6 right away.

Just as everyone was still hotly discussing the features of various large language models, Zhipu AI quietly dropped a bombshell—officially announcing their latest flagship model: GLM-4.6. This update is not a minor tweak, but a comprehensive upgrade to the previous GLM-4.5, especially in handling complex tasks and code generation, demonstrating a strong ability to compete with the industry’s top models.

So, what makes this new version so powerful? And where does it stand in the fierce AI competition? Let’s take a look together.

Five Core Upgrades: What’s Different about GLM-4.6?

Compared to GLM-4.5, this GLM-4.6 brings several key breakthroughs that directly impact its performance in real-world applications.

1. Longer Context Window The context window has been expanded from the original 128K tokens to 200K tokens. What does this mean? Simply put, the model can now “remember” more information and process longer documents, codebases, or conversation histories at once. This upgrade is crucial for complex agent tasks that require a deep understanding of context.

2. Superior Coding Performance Whether in standard code benchmark tests or in applications in real development tools like Claude Code, Cline, and Kilo Code, GLM-4.6’s scores and actual performance have reached a new level. It is particularly worth mentioning that it has shown significant improvement in generating visually exquisite web front-end interfaces.

3. Advanced Reasoning GLM-4.6 has shown clear progress in reasoning performance. It now supports calling external tools (Tool Use) during the reasoning process, which makes its problem-solving ability more comprehensive and powerful.

4. More Capable Agents With stronger tool use and search capabilities, GLM-4.6 can be more effectively integrated into various agent frameworks to perform multi-step complex tasks.

5. Refined Writing The model’s style and readability when generating content are closer to human preferences. It performs more naturally, especially in scenarios that require delicate emotional expression, such as role-playing.

Performance Showdown: How Does GLM-4.6 Perform in Benchmark Tests?

Seeing is believing, and data is the hard truth. Zhipu AI conducted a comprehensive evaluation of GLM-4.6 on eight public benchmark tests covering agent, reasoning, and coding capabilities.

Evaluation Description: The following scores are the results evaluated on 8 benchmark tests (AIME 25, GPQA, LiveCodeBench v6, HLE, BrowseComp, SWE-bench Verified, Terminal-Bench, T²-Bench) at a context length of 128K.

From the chart above, it is clear that GLM-4.6, represented by the blue bars, significantly outperforms GLM-4.5, represented by the green bars, in several tests such as AIME 25, GPQA, and BrowseComp.

What’s more interesting is its comparison with industry-leading models. GLM-4.6 has shown competitiveness comparable to DeepSeek-V3.2-Exp and Claude Sonnet 4 in many projects. However, as the saying goes, “there is always a higher mountain,” and in terms of coding ability, it still has a slight gap compared to the current top model, Claude Sonnet 4.5. This also shows the rapid development of AI technology and the fierce competition.

Not Just Looking at Scores: Real-World Coding in Action

While the scores on the leaderboard are important, what developers care about most is how the model “feels” in real development scenarios.

To this end, Zhipu AI has expanded their CC-Bench testing platform. In this test, human evaluators interact with the AI model in an independent Docker environment for multiple rounds to complete real-world tasks covering front-end development, tool construction, data analysis, software testing, and algorithm design.

The results are quite impressive:

• On par with Claude Sonnet 4: GLM-4.6’s win rate reached 48.6%, almost a tie with Claude Sonnet 4.
• Surpassing other open-source models: It significantly outperforms other models such as GLM-4.5, Kimi-K2-0905, and DeepSeek-V3.1-Terminus.

More importantly, efficiency. In terms of token usage efficiency, GLM-4.6 requires about 15% fewer tokens to complete the same task than GLM-4.5. This means it has not only become stronger, but also more economical. All evaluation details and data have been made public on Hugging Face for further research by the community.

How to Get Started with GLM-4.6?

After reading this, are you eager to try it out for yourself? There are currently several ways to experience the powerful features of GLM-4.6:

• Call via the Z.ai API platform Developers can directly call the GLM-4.6 model on the Z.ai API platform. For detailed API documentation and integration guides, please refer to the official documentation. In addition, it can also be accessed through the OpenRouter platform.

• Use in code agents GLM-4.6 now supports several mainstream code agent tools, such as Claude Code, Kilo Code, Roo Code, etc.

  • For GLM Coding Plan subscribers: The system will automatically upgrade for you. If you have ever customized your profile (e.g., ~/.claude/settings.json), you just need to change the model name to "glm-4.6" to complete the upgrade.
  • For new users: The GLM Coding Plan offers a very attractive price, allowing you to get three times the usage of Claude at one-seventh the price. Subscribe now!

• Chat on the Z.ai website The easiest and most direct way is to go to the Z.ai website, select GLM-4.6 in the model options, and you can chat with it directly.

• Deploy locally For users who want to run on their own machines, the model weights of GLM-4.6 will soon be available on HuggingFace and ModelScope. It supports mainstream inference frameworks such as vLLM and SGLang. Detailed deployment instructions can be found in its official GitHub repository.

In summary, the launch of GLM-4.6 undoubtedly provides AI developers and users with a very competitive new choice. It not only catches up with top models in performance, but also shows great value in real application scenarios and usage efficiency. The AI model arms race continues, and GLM-4.6 is undoubtedly a powerful player in this race that cannot be ignored.