MMPCBench - Strategic Effectiveness: leaderboard

MMPCBench (arXiv 2608.29286v1, Table 2): proactive critique of flawed multimodal inputs for 14 MLLMs; a judge panel of GPT-5.2, Gemini-3-Pro and Claude-Sonnet-4 scores the responses, and two of those judges are also evaluated. Mean judged utility of the chosen correction, clarification or refusal on detected errors only, 0 to 2; higher is better.

Metric: Judge-panel score (0-2; detected errors only). Source: arxiv.org. Saturation forecast: Estimated already saturated. 14 models tracked.

Top models

#ModelScoreOverall rank
1GPT-5.1 (High)1.95#145 (GPT-5.1)
2GPT-5 Mini1.93#193
3GPT-5.21.87#115
4Claude Sonnet 4.5 (Thinking)1.86#148 (Claude Sonnet 4.5)
5Gemini 3 Pro1.79#82
6Gemini 3 Flash1.77#103
7Qwen 3 VL 235B A22B Instruct1.69#280
8GLM-4.6V1.69#330
9Qwen 3 VL 30B A3B Instruct1.57#390
10Qwen 3 VL 8B Instruct1.54#419
11Gemma 3 27B (IT)1.42#528
12Gemma 3 12B (IT)1.42#672
13Gemma 3 4B (IT)1.24#997

No result here: #3 Claude Opus 5.5, #6 GPT-6 Astra, #7 GPT-6.1 Sol.

Interactive version: theaggregate.ai/benchmark?slug=mmpcbench-strategic-effectiveness · How It Works · Data refreshed daily, snapshot 2026-11-05.