Detailed evaluation of GLM-5 compared to Claude Opus 4.6 and GPT-5.3. Will domestic large models counterattack in 2026?

In early 2026, Zhipu continued to iterate on its flagship GLM large model, and in Chinese-language scenarios and Agent tool calling it is the domestic model maker drawing the most attention. Over the same period, Anthropic's Claude flagship series and OpenAI's GPT flagship series remain the internationally recognized benchmarks for coding and overall intelligence. This article does not cite the specific benchmark scores from each company's public leaderboards; instead, across the five dimensions of model positioning, typical-task feel, pricing strategy, and domestic availability, it tells you in which scenarios Zhipu's flagship can replace overseas flagships and in which it still lags.

The Positioning of Zhipu's GLM Flagship

GLM is Zhipu AI's flagship large model series, with a steady iteration cadence. New versions usually focus on three things: a longer context window, more stable Agent tool calling, and more native multimodality. For the exact latest version number, parameter scale, and context window, refer to the current page on the official site.

Its Chinese comprehension and writing ability are top-tier in the industry among domestic models, and its Agent tool-calling accuracy keeps improving; this is its biggest draw for domestic developers. API pricing is usually clearly lower than overseas flagships, which is also why it is most often used as a "cost-effective domestic alternative."

The Positioning of the Claude Opus Series

Opus is the largest and most overall-intelligent flagship in Anthropic's model family, with a long context window and creative writing style being its recognized strengths. At the architecture level, Anthropic keeps a low profile and its specific parameters are undisclosed. On public leaderboards like LMArena, Opus has long held a top position, and its stability in coding scenarios is widely acknowledged in developer circles.

Its API pricing is the highest of the three flagships, but its user stickiness is also the strongest, which is why it can commercially hold a high price tier.

The Positioning of the GPT Flagship Series

The GPT flagship series is OpenAI's signature offering, with a fast iteration cadence, and coding is one of the directions it pushes most aggressively. OpenAI usually releases specialized sub-versions for programming tasks, and in mainstream IDEs like Cursor, Windsurf, and Copilot it is one of the default options.

For the exact latest sub-version and pricing, refer to OpenAI's official site. Its pricing is usually in the middle tier of the three, and its overall capability and stability are its selling points.

Comparison on Long-Form Chinese Writing

Having each of the three write a 2,000-word Chinese article on the theme "the globalization of Chinese tea culture in 2026." Zhipu needs almost no revision for Chinese fluency, with solid use of local knowledge and a natural article style. The Claude series is also very fluent in Chinese, but occasionally its word choice is overly formal and it uses slightly too many Europeanized sentence structures. The GPT series is not as smooth as the other two at long-form Chinese writing, and this has not changed much over the years.

Conclusion: for long-form Chinese scenarios, Zhipu is often the most comfortable choice.

Comparison on Web Design

Having each of the three design a landing page in HTML+CSS+JS on the theme "AI learning platform," requiring responsive design plus animation plus dark mode. Zhipu GLM's output is clean and modern, correctly responsive, with passable animation and complete functionality; the Claude series has a finer sense of design, with parallax, transitions, and layering, but occasionally leaves a small bug in the toggle switch that needs manual fixing; the GPT series has the most orderly structure but a slightly weaker sense of creativity.

Conclusion: for high design demands use Claude, for high get-it-right-the-first-time practicality use GLM.

Framework Migration Task

Having each of the three migrate a Laravel project to a Next.js full stack, requiring the business logic and database structure to be preserved. All three can complete it; Claude handles details like authentication and ORM schema most solidly; GPT is fast with complete deployment configuration; GLM is a bit slower but has a clear price advantage, suiting budget-tight projects to first get the basic migration running, with the key authentication module backstopped by hand.

Comparison on Mathematical Reasoning

For complex mathematical reasoning tasks, all three have entered the realm of "thinking mode / long-chain reasoning," and which is faster or more accurate depends on the specific problem. The overall impression is that the Claude series has the most concise derivations, GPT reacts fastest, and GLM is friendlier in Chinese expression, but its answer speed and accuracy are both already good enough.

We do not cite the specific scores from each company's public leaderboards, because these leaderboards have fluctuated greatly over the past year and the differences between sub-models are huge; pinning it to a single number actually risks being misleading.

Three.js 3D Sandbox

Having each of the three make a Three.js 3D sandbox, requiring a block world plus first-person view plus mouse control. All three can produce basic sandbox functionality. The Claude series has the highest completion on add-ons like day-night cycles, sound effects, and simple monster AI; the GPT series has the most polished code structure; GLM suits making a runnable MVP first, then having Claude help fill in the details.

Agent Tool Calling

Making a simple Agent that automatically searches stocks plus writes a technical analysis plus sends an email. All three are already quite good at tool-calling stability, and GLM has improved fastest in function-calling accuracy over the past year, basically pulling level with Claude; GPT occasionally has minor issues with missing parameter fields, but is overall usable too.

This is one of the most notable areas of progress for domestic models this year. Whereas Agent work used to mean Claude or GPT, domestic models are now a qualified choice too.

The Common-Sense Price Range

Accounting for the same task volume, Zhipu GLM is usually a fraction of Claude Opus, with the exact ratio changing as each company adjusts pricing. GPT flagship is in the middle tier. If you are not absolutely chasing the strongest, GLM is still the most rational domestic choice in 2026; if your project has a hard requirement for the strongest overall intelligence, Claude Opus is still unavoidable.

Have Domestic Models Caught Up

It depends on the scenario. In Chinese scenarios, Chinese writing, and Chinese professional domains, GLM has matched or surpassed Claude; in coding, everyday tasks are already close but large, complex tasks still have a gap; in Agent scenarios, GLM has caught up, with stability level with Claude; in multimodality, GLM has progressed fastest and its basic features are already usable, but top-tier fine-grained tasks still call for Claude or GPT.

Overall, GLM is the first time in a year that a domestic model has substantively closed in on overseas flagships across multiple dimensions at once, rather than just matching at a single point. This structural catch-up makes 2026 the year that Chinese large models truly possess industrial substitution power.

Frequently Asked Questions (FAQ)

Can GLM be used directly in China

Yes. After registering on Zhipu's open platform bigmodel.cn, you can apply for the API directly, and new users usually get a free trial quota. You can also download the open-source GLM Lite version for local deployment; the 30B-class parameter scale can run on mid-range VRAM. Domestic access latency and stability are both clearly better than connecting directly to Claude / GPT.

Is GLM's data secure

Zhipu emphasizes in its user agreement that enterprise-edition data is not used for training, and you can separately sign a data protection agreement. For specific compliance certificates, refer to the current public page on the official site. For overseas enterprises involving heavily regulated data, it is recommended to prioritize OpenAI, Anthropic, or a privately deployed open-source GLM version. The compliance risk for individual users in everyday use is negligible.

For writing a thesis, should a student choose GLM or Claude

For Chinese theses GLM feels smoother and the price is a fraction; for English theses Claude is slightly stronger. Whichever you use, pay attention to your school's specific policy on "AI-assisted writing." Since 2026, the vast majority of universities have made explicit rules about "undeclared use of AI tools" as academic misconduct, and compliant use is what matters.

Is GLM suitable for an internal enterprise AI assistant

Very suitable, for three reasons: low price, support for private deployment, and top-tier Chinese support in the industry. For internal scenarios like knowledge bases, contracts, email, and customer service, GLM is quite handy, and quite a few large domestic enterprises are already piloting internal GLM Copilots; for specific names, refer to the vendor's public case studies.

How to choose between GLM and Kimi

GLM has higher overall intelligence, better Agent tool-calling stability, and stronger multimodality; the Kimi series has its differentiated advantage in ultra-long context windows and long-document processing. For everyday conversation and code, GLM is steady; for processing ultra-long PDFs or large codebases, Kimi has the longer reach. If you only want one domestic model, GLM is more general-purpose; if you often handle long documents or large codebases, Kimi is a better supplement.

Inspired by: Ruan Yifeng's "Zhipu Flagship GLM-5 Hands-On: Comparing Opus 4.6 and GPT-5.3-Codex" https://www.ruanyifeng.com/blog/2026/02/glm-5.html