EngiWorld
Agents work through the full industrial engineering design loop on 1,301 expert-curated tasks that need reasoning over geometric and physical constraints across software.
59 HF paper upvotes.
- Made by
- Zhiman AI
- Released
- September 2026
- Kind
- CAD models
- Level
- Expert
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
- Claude Opus 5 #10 on the capability index
- Last model added
- September 2026
- Scores refreshed
- November 12th, 2026
In the authors’ words
Autonomous agents have made rapid progress in general-purpose computer use, yet reliable automation of professional industrial engineering remains out of reach. Engineering workflows require reasoning over geometric and physical constraints and keeping dependencies consistent across software and design stages. We present EngiWorld, a benchmark designed around the capabilities that real engineering projects require, from operating a single application to solving engineering problems end to end. It contains 1,301 expert-curated tasks spanning six engineering domains (CAD, CAE, CAM, BIM, EDA, and DCC) and 26 professional software applications, with both GUI and CLI interfaces. The tasks are organized into six types, corresponding to capabilities that real engineering projects require. We further introduce an artifact-centric evaluation methodology built on a unified suite of domain verifiers. The verifiers programmatically check whether final and intermediate artifacts are geometrically valid, physically feasible, and compliant with design rules. Beyond binary success, they also score how well optimized designs meet their specifications. Evaluation of seven frontier models reveals a substantial capability gap: the strongest model scores only 44.7/100, and just 3.6% of runs on tasks that require cross-software coordination succeed. EngiWorld provides a rigorous foundation for measuring progress toward agents that operate professional engineering software end to end.
Claude Opus 5 leads with 44.7% among models released by September 2026
Best score per model from the benchmark's own leaderboard, read on November 12th, 2026. Where a model ran with several settings or harnesses, we keep its best.
The newest model with a score here came out in September 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
- Claude Opus 5.5
- GPT-6.1 Sol
- and 14 more
- 1 Claude Opus 5 Overall (306 tasks) 44.7%
- 2 GPT-5.6 Sol Overall (306 tasks) 38.6%
- 3
Qwen 3.8 Max Overall (306 tasks) 26.3%
- 4
Gemini 3.7 Flash Overall (306 tasks) 26.1%
- 5
DeepSeek V4.1 Flash Overall (306 tasks) 25.9%
- 6 Kimi K3 Overall (306 tasks) 24.3%
- 7
Qwen3.8 Flash Overall (306 tasks) 16.7%
Source: Official EngiWorld leaderboard
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | Claude Opus 5 | 44.7% | Overall (306 tasks) | Jul 2026 | Official leaderboard |
| 2 | GPT-5.6 Sol | 38.6% | Overall (306 tasks) | Jul 2026 | Official leaderboard |
| 3 | | 26.3% | Overall (306 tasks) | Aug 2026 | Official leaderboard |
| 4 | | 26.1% | Overall (306 tasks) | Aug 2026 | Official leaderboard |
| 5 | | 25.9% | Overall (306 tasks) | Sep 2026 | Official leaderboard |
| 6 | Kimi K3 | 24.3% | Overall (306 tasks) | Jul 2026 | Official leaderboard |
| 7 | | 16.7% | Overall (306 tasks) | Aug 2026 | Official leaderboard |
EngiScore versus API cost
Overall evaluation across 306 tasks, including unsuccessful runs. The line joins the runs that no other run beats on both measures. We redrew this chart from the numbers on the official leaderboard.
| Model | Setting | EngiScore | Mean API cost per task |
|---|---|---|---|
| Claude Opus 5 | Overall (306 tasks) | 44.7% | $18.78 |
| GPT-5.6 Sol | Overall (306 tasks) | 38.6% | $9.57 |
| | Overall (306 tasks) | 26.3% | $6.44 |
| | Overall (306 tasks) | 26.1% | $2.08 |
| | Overall (306 tasks) | 25.9% | $0.8 |
| Kimi K3 | Overall (306 tasks) | 24.3% | $5.85 |
| | Overall (306 tasks) | 16.7% | $0.44 |
Number one over time
Lab claims count from the day the card came out, independent runs from the model’s release.
Source: Official EngiWorld 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 EngiWorld 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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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%