An Empirical Study of Proactive Coding Assista: leaderboard
Large language model (LLM)-based coding assistants have made substantial progress, yet most systems remain reactive, requiring developers to explicitly formulate their needs. Proactive coding assistants aim to infer latent developer intent from integrated development environment (IDE) interactions and repository context, thereby reducing interaction overhead and supporting more seamless assistance
Metric: Pass@1 (self-reported). Source: benchmarklist.com. Saturation forecast: Around June 2027. 7 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Claude Sonnet 4.6 | 13.57 | #90 |
| 2 | Gemini 3.1 Pro (Preview) | 11.46 | #58 |
| 3 | Qwen 3.5 397B A17B | 8.43 | #146 |
| 4 | DeepSeek V3.2 | 7.77 | #205 |
| 5 | GLM-5 | 7.77 | #141 |
| 6 | GPT-5.4 | 6.59 | #79 |
| 7 | MiniMax-M2.5 | 2.77 | #303 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=an-empirical-study-of-proactive-coding-assista · How It Works · Data refreshed daily, snapshot 2026-10-20.