GR-Ben - Computer Science: leaderboard
Metric: F1 (%) of first-error identification: the harmonic mean of the accuracy on flawed solutions (the earliest erroneous step named) and the accuracy on fully correct solutions (judged correct), on the 400 computer-science problems (MMLU-Pro) of GR-Ben (3,595 human-annotated solutions written by 15 LLMs); process reward models by their step predictions (scalar-score PRMs thresholded on a 40-item dev split held out of their scoring), LLMs prompted as critics with thinking disabled (mean of three runs for open models, one run for API models); higher is better. Source: arxiv.org. Saturation forecast: Around January 2027. 22 models tracked.
Top models
| # | Model | Score |
|---|---|---|
| 1 | Gemini 3 Flash (Minimal) | 61.6 |
| 2 | DeepSeek V3.2 (Non-reasoning) | 58.5 |
| 3 | GPT-5.2 (Non-reasoning) | 53.5 |
| 4 | Kimi K2 0905 | 51.3 |
| 5 | Qwen 3 8B (Non-reasoning) | 35.7 |
| 6 | Qwen 3 32B (Non-reasoning) | 35 |
| 7 | Gemma 3 27B (IT) | 31.2 |
| 8 | Qwen 3 14B (Non-reasoning) | 29.3 |
| 9 | Gemma 3 12B (IT) | 29.1 |
| 10 | Llama 3.3 70B Instruct | 28.9 |
| 11 | Qwen 3 4B (Non-reasoning) | 27.5 |
Interactive version: theaggregate.ai/benchmark?slug=gr-ben-computer-science · How It Works · Data refreshed daily, snapshot 2026-10-07.