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 will continue to iterate on the GLM flagship large model, which is the most popular among domestic models in terms of Chinese scenes and Agent tool calls. At the same time, Anthropic's Claude flagship series and OpenAI's GPT flagship series are still recognized overseas as benchmarks for code and comprehensive intelligence. This article will not cite the specific running scores of each public list. It will only tell you from the five dimensions of model positioning, typical task experience, price strategy, and domestic availability, in which scenarios the Zhipu flagship can replace overseas flagships, and in which scenarios there is still a gap.

Positioning of GLM flagship

GLM is GLM AI's flagship large model series, with a stable iteration rhythm. New versions usually focus on three things: longer context windows, more stable Agent tool calls, and more native multi-modalities. The specific latest version number, parameter scale and context window are subject to the current page of the official website.

Its Chinese understanding and writing capabilities are among the best in the industry among domestic models, and the accuracy of Agent tool calls continues to improve. This is its biggest attraction to domestic developers. The price of API is usually significantly lower than that of overseas flagships, which is why it is most often used as a "cost-effective domestic substitute".

Positioning of Claude Opus Series

Opus is the flagship with the largest volume and the highest comprehensive IQ in the Anthropic model family. Its long context window and creative writing style are its recognized advantages. At the architectural level, Anthropic keeps a low profile and its specific parameters are not disclosed. Opus has long been at the top of public lists such as LMArena, and its code scene stability is widely recognized by the developer community.

The API pricing is the highest among the three flagships, but the user stickiness is also the strongest. This is the reason why it can maintain a high price in business.

Positioning of GPT flagship series

The GPT flagship series is OpenAI's flagship series, with a fast iteration pace and code scenarios being one of its strongest areas of development. OpenAI usually launches specialized sub-versions for programming tasks, and it is also one of the default call items in mainstream IDEs such as Cursor, Windsurf, and Copilot.

For the specific latest sub-version and price, please refer to the OpenAI official website. Its pricing is usually in the middle range of the three, and its comprehensive capabilities and stability are its selling points.

Comparison of Chinese long-form writing

Let each of the three companies write a 2,000-word Chinese article on the topic "Globalization of Chinese Tea Culture in 2026". There is almost no need to revise the wisdom spectrum in terms of Chinese fluency, the local knowledge is solidly cited, and the writing style is natural. The Claude series is also very fluent in Chinese, but sometimes the words are more written and the sentences are more European. The GPT series is not as smooth as the previous two in writing long Chinese texts, and this has not changed much over the years.

Conclusion: Chinese long-text scenario intelligence is often the most comfortable choice.

Web design comparison

Let each of the three companies design a landing page HTML+CSS+JS with the theme "AI Learning Platform" and require responsiveness + animation + dark mode. GLM's output is clean and modern, with correct responsiveness, decent animations, and complete functions; the Claude series has a more delicate design, with parallax, transitions, and layers, but occasionally leaves a small bug in the toggle switching that needs to be fixed manually; the GPT series has the most regular structure and a slightly weaker sense of creativity.

Conclusion: If you have high requirements for design sense, use Claude, and if you have high requirements for one-time practicality, use GLM.

Framework migration tasks

Let each of the three companies make a Laravel project and migrate it to the Next.js full stack, requiring the business logic and database structure to be maintained. All three can be completed. Claude is the most solid in handling details such as identity authentication and ORM schema; GPT is fast and has complete deployment and configuration; GLM is a little slower but has obvious price advantages. It is suitable for projects with tight budgets. The basic migration is started first, and the key identity authentication modules are manually worked on.

Comparison of mathematical reasoning

For complex mathematical reasoning tasks, all three companies have entered the category of "thinking mode/long-chain reasoning". Which one is faster or more accurate depends on the specific question. The overall feeling is that the Claude series has the simplest derivation, GPT has the fastest response, and GLM is more friendly in Chinese expressions, but the speed and accuracy of answering questions are sufficient.

I won’t cite the specific scores of each company’s public lists, because these lists have fluctuated greatly in the past year and different sub-models have huge differences. It is easy to mislead if you specify a number.

Three.js 3D Sandbox

Let each of the three companies build a Three.js 3D sandbox, which requires a block world + first-person perspective + mouse control. All three can run basic sandbox functions. The Claude series has the highest degree of completion in additional functions such as day and night cycles, sound effects, and simple monster AI; the GPT series has the neatest code structure; GLM is suitable for making a running MVP first, and then letting Claude help fill in the details.

Agent tool call

Make a simple Agent to automatically search for stocks + write technical analysis + send emails. The three companies are already very good in terms of tool calling stability. GLM has made the fastest progress in function calling accuracy this year, basically on par with Claude; GPT occasionally has small problems with missing fields in parameters, but overall it can be used.

This is one of the most interesting improvements of domestic models this year - in the past, when mentioning Agent, Claude or GPT was required, but now domestic models are also qualified choices.

common sense range of prices

Based on the same amount of tasks completed, GLM is usually a fraction of Claude Opus, and the specific ratio changes with each company's pricing adjustments. The GPT flagship is in the middle of the pack. If you are not absolutely pursuing the strongest, GLM is still the most rational domestic choice in 2026; if the project has a rigid demand for the strongest comprehensive IQ, Claude Opus still cannot be avoided.

Have domestic models made a counterattack?

It depends on the scene. In the Chinese scene Chinese writing Chinese professional field, GLM has tied or surpassed Claude; in the code scene, daily tasks are close, but there is still a gap in large and complex tasks; in the agent scene, GLM has caught up, and its stability is equal to Claude; in multi-modal scenes, GLM has made the fastest progress, and basic functions are already available, but top-level detailed tasks still require Claude or GPT.

Overall, GLM is the first time this year that a domestic model has substantially approached overseas flagships in multiple dimensions at the same time, not just a single point of benchmarking. This structural catch-up will make 2026 the year when China’s large models will truly have industrial substitutability.

FAQ

Can GLM be used directly in the country?

Can. After registering on the Zhipu open platform bigmodel.cn, you can directly apply for the API. New users usually have a free trial quota. You can also download the GLM open source Lite version for local deployment. The 30B level parameters can run on medium graphics memory. Domestic access latency and stability are significantly better than direct connection Claude/GPT.

Is GLM data safe?

Zhipu emphasizes in the user agreement that the enterprise version data will not be used for training, and you can sign a separate data protection agreement. The specific compliance certificate is subject to the current public page of the official website. Overseas companies involving highly regulated data are recommended to give priority to OpenAI, Anthropic or privatized deployment of the open source version of GLM. The compliance risk for daily use by individual users is negligible.

Should students choose GLM or Claude when writing their thesis?

The Chinese paper GLM feels smoother and the price is a fraction; the English paper Claude is slightly better. No matter which one you use, you should pay attention to the school's specific policy on "AI-assisted writing" - starting from 2026, the vast majority of colleges and universities will have clear regulations on "undeclared use of AI tools" in academic misconduct, and compliant use is the key.

Is GLM suitable as an internal AI assistant for enterprises?

Very suitable for three reasons: low price, support for privatized deployment, and top-notch Chinese support in the industry. It is easy to use GLM for internal scenarios such as knowledge base, contracts, emails, and customer service. Many large domestic companies are already piloting GLM internal Copilot. The specific list is based on the manufacturer's public cases.

How to choose between GLM and Kimi

GLM has a higher overall IQ, better Agent tool call stability, and stronger multi-modality; the Kimi series has its differentiated advantages in ultra-long context windows and long document processing. GLM is more stable for everyday conversations and code; Kimi is more stable for dealing with extra-long PDFs or large code bases. If you only want a domestic model, GLM is more versatile; if you often deal with long documents or large code bases, Kimi is a more suitable supplement.

Source of inspiration: Ruan Yifeng's "Actual Measurement of GLM-5 Flagship: Comparing Opus 4.6 and GPT-5.3-Codex" https://www.ruanyifeng.com/blog/2026/02/glm-5.html