VibeThinker-3B achieves 80.2 on LCBv6

VibeThinker-3B, a dense 3B parameter model, achieves 80.2 Pass@1 on LCB v6, 94.3 on AIME'26, and 76.4 on IMO-AnsBench, with further improvements using CLR. The model demonstrates frontier-level verifiable reasoning and OOD coding performance, challenging traditional scaling laws.

⭐ VibeThinker-3B is released — a dense 3B model for frontier-level verifiable reasoning. 🚀 Reasoning: 94.3 on AIME’26, 76.4 on IMO-AnsBench, and 80.2 Pass@1 on LCB v6; with CLR, AIME‘26 improves to 97.1 and IMO-AnsBench to 80.6. 💻 OOD Coding: On recent unseen LeetCode weekly B passes 123/128 96.1% first-attempt Python submissions. ⚡ Efficiency: Only 3B parameters, yet reaching the performance range of much larger top-tier reasoning models. 🧠 Perspective: Small models are not just cheaper substitutes. In parameter-dense domains with clear verification signals, SLMs offer a path to frontier-level reasoning that complements traditional Scaling Law. Model :