Geo-Eval: leaderboard
While general-purpose Large Language Models (LLMs) applied to Geology often hallucinate when reasoning about subsurface structures and deep-time evolution, current AI in Earth sciences predominantly targets surface remote sensing and GIS. To bridge this gap, we introduce Geo-Expert, a family of parameter-efficient geological LLMs fine-tuned on a custom-curated, high-quality instruction dataset pro
Metric: Average (self-reported). Source: benchmarklist.com. Saturation forecast: Around April 2027. 11 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GPT-5.4 | 7.15 | #79 |
| 2 | GPT-4o | 5.93 | #343 |
| 3 | Gemma 3 27B (IT) | 5.16 | #523 |
| 4 | Qwen 3 32B | 5 | #443 |
| 5 | Qwen 3 8B | 4.63 | #674 |
| 6 | glm-4-9B | 4.57 | #889 |
| 7 | Llama 3.1 70B Instruct | 4.12 | #561 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=geo-eval · How It Works · Data refreshed daily, snapshot 2026-10-20.