LLM System Design Benchmark (nqbao.com)

A new benchmark has been introduced to evaluate the performance of various large language models (LLMs) on system design tasks, marking a significant step in assessing AI capabilities in complex problem-solving environments. The benchmark entails presenting each LLM with a cold prompt for system design—lacking examples or hints—and then evaluating their responses based on architecture, capacity estimation, tradeoffs, and failure analysis. A total of nine models were tested across nine unique problems, generating 81 transcripts that were judged independently across five dimensions. The results of the benchmark highlight the competitive landscape of LLMs, with the kimi-k2.6 model emerging as the top performer with a mean score of 4.39, closely followed by gpt-5.4 at 4.34. This systematic evaluation not only sets a standard for future competitions among LLMs but also provides insights into their strengths and weaknesses in practical applications. As AI/ML continues to evolve, such benchmarks are crucial to push the boundaries of model capabilities and guide further innovations in technology.