DiagFlowBench: leaderboard
Language models increasingly serve as advisory systems in maintenance operations. To prevent hallucination, recent systems ground these models in procedural documentation to constrain them to approved steps. In practice, however, operator queries frequently stray from this path, requiring models to recognise out-of-scope inputs mid-conversation, a dynamic that current benchmarks rarely prioritise.
Metric: Step Accuracy (self-reported). Source: benchmarklist.com. 10 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Llama 3.3 70B | 85 | #579 |
| 2 | Gemini 2.5 Flash | 84 | #245 |
| 3 | Llama 4 Maverick | 82.8 | #464 |
| 4 | Llama 4 Scout | 79.2 | #656 |
| 5 | GPT-OSS-120B | 76.2 | #341 |
| 6 | GPT-4o Mini | 75.4 | #599 |
| 7 | Qwen 3 235B A22B | 73.7 | #313 |
| 8 | Nemotron 3 Super | 70.3 | #335 |
| 9 | Qwen 3 30B A3B | 70.1 | #493 |
Interactive version: theaggregate.ai/benchmark?slug=diagflowbench · How It Works · Data refreshed daily, snapshot 2026-10-20.