Intern-Decision scales multimodal decision models against Jev
The 0.8B, 2B, and 4B models average 79.38, 84.68, and 90.02 across seven decision benchmarks, with the 4B reaching 88.74 to pass Jev at lower latency and returning structured JSON with calibrated probabilities in one forward pass.
Intern-Decision scales multimodal structured decision-making across 0.8B, 2B, and 4B models. 🤖 https://modelscope.ai/collections/Shanghai_AI_Laboratory/Intern-Decision 🏆 The three models average 79.38, 84.68, and 90.02 across seven decision benchmarks. Intern-Decision-4B surpasses Jev at 88.74 while achieving better probability calibration. ⚡ Reported mean latency is 33.98 ms, 33.28 ms, and 44.16 ms respectively, compared with 109.70 ms for Jev in the same local HF setup. 🧠 One forward pass answers multiple choice, score, and yes/no questions with typed JSON and calibrated probability distributions. 🖼️ Text states and up to eight images enable routing, tool selection, scoring, and multimodal workflow control. 📜 Apache 2.0. Applicable Qwen3.5 upstream notices must be retained.