| --- |
| 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](https://huggingface.co/papers/2610.22708) |
|
|
| 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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
