AppWorld-UL (Code Agent) - Scenario Goal Completion: leaderboard
AppWorld-UL (2026) turns AppWorld test-challenge scenarios into user-in-the-loop tasks that are infeasible, underspecified or need confirmation, so an agent succeeds only if it also asks, informs or confirms with a simulated user. Share of scenarios whose every task variant is completed with the required interaction, in percent, with a ReAct code agent; higher is better.
Metric: Interactive scenario goal completion (%; share of task scenarios in which the agent completes every task variant under the same interaction checks; ReAct code agent that writes Python against the AppWorld app APIs; GPT-5.5 user simulator answering from a fixed knowledge set). Source: arxiv.org. Saturation forecast: Around September 2027. 6 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Claude Opus 4.7 | 30.2 | #79 |
| 2 | GPT-5.5 (Medium) | 23.5 | #49 (GPT-5.5) |
| 3 | Qwen 3.7 Max | 22.9 | #145 |
| 4 | GLM-5.1 | 21.2 | #179 |
| 5 | Kimi K2.6 | 12.5 | #178 |
| 6 | DeepSeek V4 Pro | 7.3 | #188 |
No result here: #1 Claude Fable 5.2, #2 Gemini 4 Carbon, #5 Claude Opus 5.5.
Interactive version: theaggregate.ai/benchmark?slug=appworld-ul-code-agent-scenario-goal-completion · How It Works · Data refreshed daily, snapshot 2026-11-13.