The Chinese startup Apex Intelligence, founded in June 2026, unveiled its first model, Apex-35B-V1.0, on October 28. The model weights are available on the Hugging Face platform. The model is based on Alibaba's Qwen3.6-35B-A3B and was further refined through intermediate and subsequent training focused on coding tasks, agent workflows, general tasks, as well as research in artificial intelligence, computational science, and robotics simulation.
The company's main focus is not so much on the model's size as it is on its creation methods. Apex Intelligence asserts that its automated research system, named Apex Research, participated in generating training data, conducting experiments, developing the training plan, and optimizing GPU cores and the inference system.
As an example, the company cited a case where Apex Research discovered a weakness in the model's computational fluid dynamics tasks. The system analyzed the reasons for the model's shortcomings in reasoning, then generated and verified new training data, which was used in subsequent training. According to the company's internal testing, this cycle increased the model's CFD-Bench score by 32.1 points. Furthermore, Apex Intelligence reports that AI-driven optimization increased training throughput by 45% to 60% and accelerated decoding by 1.6–1.8 times.
All benchmarking results published with the release were obtained through the company's own evaluation. Based on this data, Apex Intelligence claims that Apex-35B-V1.0 surpasses both Qwen3.6-35B-A3B and Flint from Recursive SuperIntelligence across all six presented research benchmarks. The model also performs better than GLM-5.3 on FrontierScience and PaperBench tests, outperforms DeepSeek-V4.1-Flash-1017 in the human-difficult BioMysteryBench division, and demonstrates the highest BFCL V4 score among comparable models.
In two robotics tests, RoboRSI and Real2Sim, the model achieved scores of 44.5 and 21.55, respectively, which are close to the performance of the larger GLM-5.3-Flash model (52.4 and 21.4).
The Real2Sim test requires the model to reconstruct a working physical simulation from a single photograph: it must identify objects, select simulation resources with similar visual and physical characteristics, and recreate their arrangement for robot action testing. RoboRSI assesses the model's ability to control simulated robots by writing and correcting code based on visual feedback.
The company was founded by Chen Yunchao, an associate professor at Tsinghua University's AI College. He describes this work as recursive self-improvement—a concept where AI systems help create their successors. In an interview with Tencent Technology, he noted that AI has made clear progress in conducting research, but it still lacks 'research intuition,' meaning the ability to determine which problems are worth pursuing.