Breakthrough of Domestic Large Models: Engineers Admit 'Good Enough' as Codex and Claude Step Aside

Overview

Recently, domestic artificial intelligence large language models have risen rapidly within China's developer community, attracting widespread attention. An interview with programmers has revealed the true motivations behind this trend: pragmatism and cost considerations are gradually replacing blind pursuit of top foreign products. When asked about the AI tools they currently use, the interviewed developers explicitly stated that they mainly rely on DeepSeek and GLM, while choosing to actively step back from Codex and Claude, which have long dominated the high-end development market. This phenomenon marks a transition for domestic large models in vertical fields, moving from "usable" to "easy to use."

Engineer's Perspective: From "Chasing" to "Good Enough"

The dialogue in the interview hits right at industry pain points. Faced with the question of why they abandoned top international models, the respondent's answer was concise and powerful: "What I do is not that complex, domestic models can solve it." This shift in mindset reflects a reshaping of the evaluation criteria for AI tools among the domestic developer community—no longer blindly worshipping brand premium, but focusing more on actual problem-solving capabilities.

Currently, this engineer's work mainly focuses on C++ embedded development and Python test script writing. Under this technology stack, he admitted: "There are currently no problems that domestic models cannot solve." This indicates that in tasks of medium complexity and basic code generation, the performance boundaries of domestic large models already cover a large number of daily development scenarios.

"Domestic models can solve it... Currently, there are no problems that domestic models cannot solve."

This "good enough" theory is not based on prejudice, but on a rational assessment after high-frequency use. For a large amount of daily engineering that does not involve top-tier algorithm research or ultra-large-scale system architecture, the intelligent assistant experience provided by domestic models has reached or even exceeded the psychological expectation threshold.

Market Landscape: Local Challenges Facing International Giants

As models like DeepSeek continuously break through in technical performance, and GLM deepens its focus on Chinese context understanding, international giants such as OpenAI's Codex and Anthropic's Claude are facing unprecedented competitive pressure within the Chinese developer circles.

1. Cost-effectiveness advantage: Domestic models usually offer better pricing strategies and localized services, reducing the trial-and-error costs for enterprises and individual developers.
2. Data compliance and security: Against the backdrop of increasingly perfect domestic laws and regulations, using locally deployed or domestically operated models has become the priority choice for many enterprises and institutions.
3. Ecosystem adaptation: Optimizations targeting mainstream domestic programming language frameworks and development environments make domestic models smoother in practical implementation.

Although this interview is a single case, it is typically representative. It implies that in the massive mid-level developer market, a wave of substitution driven by "cost-effectiveness" and "practicality" is quietly occurring. In the future, whether domestic large models can establish a firm footing in more complex full-stack development and frontier scientific research fields will be the key touchstone for testing whether they have truly "broken through."