Datasets:
language:
- en
license: apache-2.0
size_categories:
- 1K<n<10K
task_categories:
- text-generation
pretty_name: GlobeBench
tags:
- world-models
- benchmark
- agents
- llm-evaluation
configs:
- config_name: items
data_files:
- split: test
path: data/items.jsonl
- config_name: results
data_files:
- split: test
path: data/results.jsonl
GlobeBench
Paper: GlobeBench: Evaluating Text-Based World Model Capabilities for Scalable Environment Simulations
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}
}