API Benchmark Scores Do Not Reliably Transfer to Chatbot Interfaces
Benchmark scores are a central currency in model releases: they inform purchasing decisions, shape public trust, and influence policy. Yet, a key assumption underlying benchmark scores is that the model performance measured through APIs faithfully reflects the behavior of deployed systems. We challenge this assumption by auditing ChatGPT, Claude, and Gemini across seven systems and nine benchmarks spanning general capability, social bias, and sycophancy. We find systematic API--interface differences in both accuracy and consistency. On average, API evaluations score 3.4 percentage points higher in accuracy and 2.1 percentage points higher in test--retest agreement than corresponding inter...
| Authors | Jennifer Wang, Joachim Baumann, Daniel E. Ho et al. |
|---|---|
| Primary category | cs.AI |
| Published | 8 September 2026 |
| Last updated at source | 8 October 2026 |
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