AdaPlanBench - Physical Plausibility: leaderboard

AdaPlanBench (2026) gives LLM agents 307 household planning tasks whose world and user constraints stay hidden until a proposed plan violates one, so the agent must re-plan from feedback; boards use the mid-level constraint profile. This board is the mean 1 to 5 rubric score of three LLM judges for whether the plans respect real-world physics and realistic tool effects; higher is better.

Metric: Physical plausibility rubric (1-5): whether the plan's actions obey real-world physics and its tool use would produce the claimed effects; mean score (1-5) from three rubric judges (GPT-4.1, DeepSeek-V3.2, Qwen3.6-Flash) over the agent's plans, 307 household planning tasks with progressively revealed world and user constraints at the mid-level constraint profile: a hidden constraint is disclosed only when a proposed plan violates it (GPT-5.4 constraint judges), and the agent re-plans within the turn budget; temperature 0 (GPT-5 family at its fixed default, mean of three runs); higher is better. Source: arxiv.org. Saturation forecast: Estimated already saturated. 10 models tracked.

Top models

#ModelScoreOverall rank
1GPT-54.68#167
2GPT-5 Mini4.62#348
3GPT-5 Nano4.43#889
4Gemini 3 Flash4.28#164
5Gemini 3.1 Pro (Preview)4.26#94
6DeepSeek V4 Flash4.22#258
7Llama 3.3 70B Instruct3.82#1357
8Qwen 3 14B3.52#1233
9Qwen 3 32B3.5#960
10Qwen 3 8B3.48#1830

No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.

Interactive version: theaggregate.ai/benchmark?slug=adaplanbench-physical-plausibility · How It Works · Data refreshed daily, snapshot 2026-11-13.