GlobeBench

GlobeBench evaluates whether a language model can act as a text-based world model: given an agent's trajectory of actions and environment observations, it must predict the environment's response to the next action. The benchmark has 1,632 execution-grounded items across five domains (Code, Enterprise, Games, Math and Science, Dialogue) and 19 simulation skills.

• Code, full model outputs, and judge records: https://github.com/patronus-ai/globebench

Configurations

items (1,632 rows)

Field	Description
example_id	Item identifier
messages	The rendered trajectory shown to the model (system prompt, prior turns, final action)
target	The execution-derived gold next observation
metadata	Domain, primary skill (primary_sub_eval), all tested skills (sub_evals), reach-back (h_reachback), impact radius (radius_level), scoring contract, source, and source license

results (32,640 rows: 20 models x 1,632 items)

Field	Description
model	Model name as reported in the paper
model_key	Model identifier used in the runs
item_id	Item identifier (matches example_id)
prediction	The model's predicted observation (null when there was no usable response)
served_state	served, or the reason no usable response was returned
refusal	True for provider refusals (finish_reason: content_filter)
passed	Final task-success decision under the paper's hybrid grader
resolution_rule	Whether the deterministic grader or the LLM judge decided the outcome
format_outcome	Format compliance, reported separately from correctness

Rows without a usable response count as failures. Pooled task success is 6,423 / 32,640 = 19.68%.

Leaderboard

Rank	Model	Task success
1	GPT-6 Astra	49.57%
2	Grok 4.6	28.13%
3	Claude Opus 5.5	26.35%
4	Qwen 3.8 Max	25.31%
5	GPT-5.5	25.18%
6	GPT-6 Sol	25.00%
7	GPT-5.6 Sol	23.65%
8	Qwen 3.8 Flash Next	23.22%
9	Gemini 3.7 Flash	21.75%
10	Claude Fable 5.1	21.63%
11	Claude Opus 4.8	20.04%
12	GPT-6 Luna	17.77%
13	Claude Opus 5	15.93%
14	GLM 5.3	14.77%
15	Qwen-AgentWorld-35B-A3B	14.46%
16	Kimi K3	13.97%
17	Inkling	11.89%
18	Claude Fable 5	7.29%
19	Digital World Model (DWM)	5.21%
20	Laguna S2.1	2.45%

Scoring

Each prediction is first compared with the gold by a deterministic grader that accepts differences of notation but not of state. When the grader can neither certify a match nor find a concrete contradiction, an LLM judge (GPT-5.6 Terra, with GPT-5.6 Sol as fallback) decides. The grader, judge prompt, and an offline rescoring script are in the GitHub repository.

License

The code and the items authored by Patronus AI are released under Apache-2.0. Items adapted from upstream projects keep their source license, recorded per item in metadata.source_license; one item derived from python-chess is GPL-3.0-or-later. Model outputs may also be subject to the respective providers' terms. See the GitHub repository's THIRD_PARTY_NOTICES.md for details.

Citation

@misc{saban2026globebench,
  title  = {{GlobeBench}: Evaluating Text-Based World Model Capabilities for Scalable Environment Simulations},
  author = {Saban, Nicholas and Bannur, Charvi and Vasileva, Mariya and Deshpande, Darshan},
  year   = {2026},
  note   = {arXiv preprint}
}