SimuVerity
Agents build executable Simulink models from text for 101 tasks in ten engineering domains. Simulations check that each model meets its engineering requirements.
It had 36 Hugging Face paper upvotes and 21 GitHub stars.
- Made by
- Xi'an Jiaotong University
- Released
- October 2026
- Kind
- Electronics
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
- GPT-5.5 #20 on the capability index
- Last model added
- August 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
Existing Simulink benchmarks mainly evaluate whether generated models compile, execute, or resemble a reference model. These criteria do not establish whether a model satisfies its engineering requirements. We introduce SimuVerity, a benchmark of 101 text-to-executable Simulink model-generation tasks across ten engineering domains. For each task, executable-system profiles ground the engineering specification and four families of native simulation scenarios. A hierarchical evaluator first checks artifact delivery, native executability, and engineering qualification, then scores qualified models across six dimensions covering accuracy, output quality, mechanistic fidelity, control and causal integrity, operating-domain robustness, and dynamic response. We evaluate six agent systems with SimuVerity. The best system achieves an overall score of only 42.86. The results show that structural similarity is a poor proxy for engineering performance: capability bottlenecks arise both in producing qualified implementations and in satisfying multidimensional requirements after qualification. Meanwhile, some high-scoring models still exhibit severe visual-layout disorder. SimuVerity provides a systematic basis for assessing agents' engineering capabilities and diagnosing failures in executable Simulink model generation.
Reference tops the leaderboard with 96.0
Best score per entry from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best. Names in gray aren't in our model list: fine-tunes, agent systems, products and older models. They don't count toward leaders. The best model we track is Claude Opus 4.8, at 42.9.
The newest model with a score here came out in August 2026, and nobody has tested a newer one. Among the 20 highest-ranked models on the capability index, these have no score yet:
- Claude Fable 5.2
-
Gemini 4 Carbon
- GPT-6 Astra
- Claude Opus 5.5
- and 15 more
- 1 Reference 96.0
- 2 Claude Opus 4.8 42.9
- 3 GPT-5.5 41.7
- 4
DeepSeek-V4-Pro 28.4
- 5
Qwen 3.8 Max 25.0
- 6 GLM-5.3-Flash 4.98
- 7 Qwen3.8-27B-FP8 1.60
Source: Official SimuVerity leaderboard
| # | Entry | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Reference | 96.0 | Official leaderboard | ||
| 2 | Claude Opus 4.8 | 42.9 | Claude Code | May 2026 | Official leaderboard |
| 3 | GPT-5.5 | 41.7 | Codex | Apr 2026 | Official leaderboard |
| 4 | | 28.4 | Claude Code | Apr 2026 | Official leaderboard |
| 5 | | 25.0 | Claude Code | Aug 2026 | Official leaderboard |
| 6 | GLM-5.3-Flash | 4.98 | Claude Code | Aug 2026 | Official leaderboard |
| 7 | Qwen3.8-27B-FP8 | 1.60 | Claude Code | Official leaderboard |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official SimuVerity leaderboard
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Source: Official SimuVerity leaderboard
No lab model card reports it yet
We read the headline results of 522 launch posts from 52 labs, and none of them list this benchmark.
More in Engineering and CAD
- EEBench In use 13 electrical-engineering circuit and PCB design tasks graded by deterministic build checks, simulation and BOM constraints. No LLM judge.
Gemini 4 Carbon 83.9%
- Parametric CAD Bench In use Agents author editable parametric FreeCAD part designs, graded against held-back references for geometry and spec consistency. Claude Opus 5.5 61%
- 3DCodeBench In use Agentic procedural 3D modeling via code: author engine-ready 3D assets through software APIs. Graded for executability and shape fidelity.
LongCat-2.5 55.4%
- CADGenBench Only in system cards Agents construct CAD models from design prompts. The generation split is graded for executability and geometric correctness. Claude Fable 5 45.1%
- PartBench Only in system cards Parametric mechanical part design and CAD-style generation, graded for geometric and manufacturing fidelity. Grok 4.6 58.5%
- LMBuild Not in model cards Language model agents design 3D structures as parts, joints, materials and assembly steps. Scoring checks that the structures can be built and work as intended. GPT-6 Astra 66.5%