China’s AI Efforts Fall Short on Novel Reasoning Benchmarks
Recent testing suggests that while China has made strides in artificial intelligence, its leading models struggle with tasks requiring genuine, novel problem-solving. New benchmarks designed to prevent ‘gaming’ the system show a significant gap between Chinese AI and top Western models.
ARC AGI 2: A Test for True Intelligence
The Ark AGI 2 benchmark is specifically designed to measure AI’s ability to solve problems it hasn’t seen before. Unlike other tests that can be ‘brute-forced’ with more data or learned from existing models, this benchmark demands unique reasoning capabilities. Recent results reveal a surprising trend: top Chinese AI models, like Kim K2, Minmax M2.5, GLM5, and Deepseek 3.2, scored lower than expected.
These scores are comparable to Western models released in July 2025, suggesting that current leading Chinese models are effectively a generation behind. The Ark Prize notes that these Chinese models cannot even match what Western labs were achieving eight months prior.
Pencil Puzzle Benchmark Exposes Reasoning Gaps
Another new test, the Pencil Puzzle benchmark, further highlights this disparity. This benchmark evaluates how well AI models can solve complex constraint satisfaction problems, similar to those that are very difficult for computers. These puzzles require step-by-step logical reasoning, and the test is structured so that models cannot simply memorize answers or rely on existing training data.
The results are stark. U.S.-based models show strong performance, with GPT 5.2 achieving 56%, Claude Opus 4.6 scoring 36.7%, and Gemini 3.1 Pro reaching 33%. In contrast, Chinese models lag significantly. Kim K2 scored 6%, Minmax managed 3.3%, Deepseek got 2%, Quen 3.5 achieved 0.7%, and GLM 5 also scored 0.7%. This shows a clear ‘cliff’ in performance for Chinese AI on this type of reasoning challenge.
Frontier Math and Humanities Exams: More Evidence of a Gap
Standard AI benchmarks, such as those testing math and humanities knowledge, are often compromised by ‘data contamination.’ This means models are trained on test questions or similar data, allowing them to score highly without demonstrating true understanding. To combat this, new benchmarks like Frontier Math and the Humanities Last Exam (HLE) were created.
Frontier Math presents entirely new, unpublished mathematical problems that require hours of deep research for human experts. Even with access to coding tools to test hypotheses, AI models struggle. Chinese models like Kim K2 and GLM 5 score very low, often around 2-3% on these advanced problems. Similarly, the HLE, which tests broad knowledge across subjects, shows that Chinese models do not perform as well as their Western counterparts on these ungameable tests.
Software Engineering Test Reveals Reliance on Optimization
Even in software engineering, where Chinese models initially appeared competitive, a closer look reveals a potential issue. On the original SWE benchmark, models like Minmax and Kim K2 showed scores close to top Western models. However, a newer version called SWE rebench, which uses fresh, unseen coding tasks to prevent data contamination, paints a different picture.
On SWE rebench, Chinese open-source models drop significantly in the rankings, appearing at positions 11 and 17. This suggests that their high scores on the original SWE benchmark may have been due to optimizing for specific, known tasks rather than possessing generalizable coding intelligence. Western models generally maintain higher positions on this more rigorous test.
Why This Matters
The implications of these findings are significant for the global AI race. For years, there have been claims and counterclaims about China’s AI progress, with some experts suggesting they are rapidly catching up or even leading. However, these new benchmarks, which specifically target the AI’s ability to reason and solve novel problems, indicate a persistent gap.
This gap suggests that while Chinese AI companies may excel at tasks that can be improved with vast amounts of data or by replicating existing solutions, they are not yet matching the fundamental reasoning capabilities of leading Western AI systems. This could impact their ability to develop truly groundbreaking AI applications in the future. Companies and researchers should be cautious about benchmark scores, especially for open-source models, and conduct their own tests to ensure performance aligns with real-world needs.
Source: New Tests Reveal The Truth About China’s AI Progress… (YouTube)
