Comparison between DeepSeek and ChatGPT, whether the domestic large model can replace OpenAI in 2026

DeepSeek will continue to be out of the circle from 2024 to 2025. V3 and R1 have pushed domestic large-scale models to a position that overseas companies have to take a look at. The question is whether it can really replace ChatGPT. This article uses several typical tasks to make a horizontal comparison to tell you where DeepSeek and ChatGPT are strong and weak, and which one is most cost-effective in which scenario. This article does not cite the specific running scores and current pricing of each company's public list. The specifics are based on the current page of the official website.

What is DeepSeek and why it suddenly emerged from the circle

DeepSeek is a Hangzhou deep search company. Its parent company is the domestic private equity hedge fund Magic Square Quantitative. The company focuses on large-scale models.

Its V3 is a flagship model with MoE architecture. After the paper was published, it shocked the overseas community. The key selling point is that the training cost is significantly lower than that of conventional flagship models. R1 focuses on reasoning ability and has reached the first echelon level in mathematics competitions and programming benchmarks. Please refer to the official website for the latest sub-version. Subsequent iterative versions such as V3.5 and R1 V2 have successively completed multi-modal, long context, and Agent tool calls.

At the commercial level, DeepSeek takes the extremely low-price route. The API price is only a fraction of the GPT flagship. This is the reason why it has penetrated deeply into the domestic developer circle.

ChatGPT 2026 model matrix

ChatGPT's model matrix in 2026 has been differentiated relatively finely. The flagship is the latest version of the GPT series, with leading comprehensive capabilities; the mid-range is the default chat model, with low latency and suitable for real-time; there are also low-priced small models suitable for batch tasks, and specialized inference sub-models. Subscription levels Plus and Pro have different price points and different function unlocks, please refer to the official website.

This means that when comparing DeepSeek to "ChatGPT", you must first distinguish which sub-model you are talking about.

Chinese long article writing

Ask both sides to write a 1,500-word Chinese article on the topic "Why was commerce so developed in the Song Dynasty?" DeepSeek's writing is extremely fluent, and the Chinese expressions are naturally not stiff. It quotes allusions such as Wang Anshi's Reform, Shibosi, and "Along the River During the Qingming Festival", and basically gets it right at once. GPT flagship Chinese fluency is also very good but a little stiffer than DeepSeek; GPT mid-range has a more obvious somatosensory gap in Chinese.

This is the natural advantage of DeepSeek’s training corpus with a high proportion of Chinese.

code generation

Let each side write a React TODO List component using TypeScript, add localStorage persistence and drag-and-drop sorting. GPT flagship is generally more solid in terms of rigor and best practice details - TypeScript types are stricter, library selection is updated, and easy-to-step pitfalls such as dependency arrays are handled more stably. DeepSeek is generally usable, and occasionally there will be small bugs that need to be fixed.

The overall feeling is that GPT flagship is still ahead in coding tasks, but DeepSeek is competitive in terms of cost performance.

Mathematical and logical reasoning

For mathematics competition and logical reasoning questions, the overall level of DeepSeek R1 series and OpenAI's reasoning sub-model (o series) is close to the first echelon, and there is not much difference in accuracy. The difference is mainly in price - the price of the R1 series is only a fraction of the price of the OpenAI inference sub-model. This item is the most cost-effective scenario of DeepSeek.

Agent tool call

Simple agent tasks: automatically search web pages to query data + write an analysis + save to a local file. The function calling of the GPT series has the longest iteration time and leads in stability; DeepSeek supports function calling, but is slightly weaker in the robustness of tool parameter construction and occasionally needs to be retried. GPT is still recommended for key Agent scenarios.

long context understanding

Have both sides work on a 100-page PDF and ask cross-page consistency questions. DeepSeek's current long context window can cover most common long document scenarios, and cross-page inference is available; GPT flagship performs stably at the 128K level; if the document size exceeds 200K, Claude flagship is still the most comfortable choice at the moment. DeepSeek still has room to catch up in the ultra-long text scene.

Chinese professional field

Let both sides explain the relevant provisions of the Criminal Code on the crime of theft. In fields with deep localization such as Chinese law, traditional Chinese medicine, and Chinese history, DeepSeek's accuracy of clause citations and sense of practical cases are smoother than those of overseas flagships. GPT flagship sometimes cannot keep up with the details of Chinese professional fields, and there will be occasional confusion.

English Academic Writing

Ask both sides to write a sociological summary in English. GPT flagship's English is smooth and natural, its academic style is authentic, and there is almost no trace of AI; DeepSeek is also good but occasionally has traces of "Chinglish" sentence structure. English scene GPT still leads.

Price and cost performance comparison

The API unit price of the DeepSeek series is usually only a fraction of that of the GPT flagship, and the quality is close to that in a large number of daily scenarios. This is why it is widely used as the "daily default" in China. GPT flagship is used for critical tasks, and DeepSeek is used for batch running of routine tasks. This is the most common combination among domestic developers.

Which scene to choose?

• Chinese writing and translation: DeepSeek.
• Academic and Creative Writing in English: GPT Flagship.
• Key projects for code generation: GPT flagship; daily scripts: DeepSeek.
• Mathematics and programming competition reasoning: DeepSeek R1 series, cost-effective.
• Agent tool call: GPT flagship, stability is the most important.
• Long document analysis: GPT and DeepSeek are sufficient for scenes within 200K, and Claude is recommended for scenes above 200K.
• Domestic development and deployment: DeepSeek, because access is stable and does not require scientific Internet access.
• Cost-sensitive scenarios such as customer service robots, batch content generation, and educational product backends: DeepSeek.

FAQ

Is DeepSeek API safe? Will the data be viewed by the Chinese government?

DeepSeek publicly states that user data will not be leaked or used for training, and the enterprise version can sign a data protection agreement. However, because the company is located in China, it is theoretically subject to the "Data Security Law" and "Cybersecurity Law". For sensitive data of overseas enterprises, it is recommended to choose OpenAI, Anthropic or DeepSeek for private deployment. The compliance risk for daily use by individual users is negligible.

Can I use ChatGPT directly in China?

It cannot be accessed directly and requires scientific Internet access. Compliance paths include: cloud vendor agency (such as Azure OpenAI through partners' compliance access), subscribing to ChatGPT Plus for overseas use, etc. It depends on your enterprise qualifications and usage scenarios. DeepSeek is extremely stable in domestic access, which is its key advantage.

Is DeepSeek a scam? ChatGPT

no. DeepSeek is a completely self-developed MoE architecture model. The paper and model weights are disclosed and can be downloaded from GitHub and HuggingFace. The output of early versions occasionally mentioned "I am ChatGPT" because the training data contained ChatGPT conversation samples, but the model itself was not a shell.

Should students choose DeepSeek or ChatGPT when writing papers?

If it is a Chinese paper, DeepSeek feels smoother, the Chinese expression is natural and accurate in the professional field; the English paper, ChatGPT, is better. However, the compliance risks of using AI to write papers do not distinguish between models. Detectors such as Turnitin and Originality can identify both, and many schools have clearly written "unauthorized use of AI tools" into academic misconduct regulations.

How does Claude compare to DeepSeek?

Each has his own strengths. The advantages of Claude's flagship are ultra-long context, code understanding, especially large code base reconstruction, delicate writing style, and top-notch English creative writing in the industry. DeepSeek's advantages include price, more authentic Chinese expressions, stable domestic access, and high cost performance for inference tasks. Choose Claude for daily overseas development, DeepSeek for domestic projects, and Claude for key tasks. It is a common combination.