Benchmark Almanac

SimuVerity

Reference 96.0 tops its leaderboard Claude Opus 4.8 42.9 leads the models we track
Not in model cards

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.

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.

Abstract of SimuVerity: Benchmarking Agents for Engineering-Grade Simulink Model Generation (arXiv:2610.02304, CC0)
7
Entries on its own leaderboard, 5 models we track

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:

  1. 1 Reference 96.0
  2. 2 Claude Opus 4.8 42.9
  3. 3 GPT-5.5 41.7
  4. 4 DeepSeek-V4-Pro 28.4
  5. 5 Qwen 3.8 Max 25.0
  6. 6 GLM-5.3-Flash 4.98
  7. 7 Qwen3.8-27B-FP8 1.60

Source: Official SimuVerity leaderboard

#EntryScoreSettingDateSource
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 DeepSeek-V4-Pro 28.4 Claude Code Apr 2026 Official leaderboard
5 Qwen 3.8 Max 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.

Anthropic
Own leaderboard 1 leader

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.

On the benchmark’s own leaderboard
0.00 10.0 20.0 30.0 40.0 50.0 2026 0.00 10.0 20.0 30.0 40.0 50.0 2026

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

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  • 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%