Drift-Bench (Service-Oriented, No Clarification) - Premise: leaderboard
Drift-Bench (Bao et al., University of Notre Dame, 2026) service-oriented set: 150 StableToolBench single-tool API tasks that at least two of three reference models solve with the clean instruction, rewritten so the instruction carries a false premise (an assumption that does not hold in the environment). The agent must act without being able to ask the user. Success rate in percent, mean of three trials; higher is better.
Metric: Success rate (%; 150 StableToolBench G1 API tasks with a premise fault (a false assumption), no clarification tools, mean of 3 trials). Source: arxiv.org. Saturation forecast: Around 2029. 7 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | GLM-4.7 | 56.65 | #365 |
| 2 | Llama 4 Maverick | 54.44 | #1105 |
| 3 | DeepSeek V3.2 | 47.11 | #403 |
| 4 | Qwen 3 235B A22B 2507 Instruct | 46.33 | #619 |
| 5 | Gemini 2.5 Flash | 40.44 | #452 |
| 6 | GPT-5.2 | 36.78 | #172 |
| 7 | GPT-OSS-120B | 34.44 | #699 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=drift-bench-service-oriented-no-clarification-premise · How It Works · Data refreshed daily, snapshot 2026-11-12.