IntentGrasp: leaderboard
Accurately understanding the intent behind speech, conversation, and writing is crucial to the development of helpful Large Language Model (LLM) assistants. This paper introduces IntentGrasp, a comprehensive benchmark for evaluating the intent understanding capability of LLMs. Derived from 49 high-quality, open-licensed corpora spanning 12 diverse domains, IntentGrasp is constructed through source
Metric: Overall (All Set) Avg (Std) (self-reported). Source: benchmarklist.com. Saturation forecast: Around July 2027. 20 models tracked.
Top models
| # | Model | Score | Overall rank |
|---|---|---|---|
| 1 | Gemini 3.1 Pro (Preview) | 59.68 | #58 |
| 2 | Claude Opus 4.7 | 57.31 | #49 |
| 3 | Gemini 3 Flash | 55.4 | #99 |
| 4 | Gemini 3.1 Flash Lite | 50.99 | #191 |
| 5 | GPT-5.4 | 50.69 | #79 |
| 6 | Claude Sonnet 4.6 | 50.38 | #90 |
| 7 | Gemma 4 31B | 49.35 | #200 |
| 8 | Claude Haiku 4.5 | 46.57 | #280 |
| 9 | Gemma 4 E4B | 45.87 | #767 |
| 10 | Qwen 3 32B | 44.22 | #443 |
| 11 | GPT-5.4 Mini | 44.04 | #212 |
| 12 | Llama 3 70B | 43.92 | #631 |
| 13 | Gemma 4 E2B | 40.52 | #1070 |
| 14 | GPT-5.4 Nano | 40.35 | #328 |
| 15 | Qwen 3 4B | 38.14 | #829 |
No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.
Interactive version: theaggregate.ai/benchmark?slug=intentgrasp · How It Works · Data refreshed daily, snapshot 2026-10-20.