Sina’s open model VibeThinker-3B aims to show reasoning compresses well but factual knowledge doesn’t

Sina Weibo’s VibeThinker-3B has just three billion parameters but matches models like DeepSeek V3.2 and Kimi K2.5 on math and coding benchmarks. Those models are up to 333 times larger.

Weibo’s parent company Sina has released a small language model that competes with today’s top models on hard math and coding tasks. According to a technical report, VibeThinker-3B performs on par with DeepSeek V3.2 and Kimi K2.5 on competitive benchmarks like AIME26. Both of those models have 200 to 333 times more parameters. Sina positions the model as an experiment in figuring out how much compute a model actually needs to compete at the top. Its predecessor, VibeThinker-1.5B, launched in November 2025. The new version pushes further, asking whether a small model can hit genuine top-tier performance, not just be “good for its size.”Ad The results tell two different stories. On structured tasks with clearly verifiable solutions, like math olympiads or programming challenges, VibeThinker-3B matches models like GLM-5 or Gemini 3 Pro. On LiveCodeBench, it beats every other model under 20 billion parameters.AdDEC_D_Incontent-1 Factual knowledge is a different story. On the knowledge-heavy GPQA-Diamond benchmark, the model falls well behind its much larger competitors. To rule out data contamination, the team had the model compete in LeetCode contests held between late April and late May 2026, after training wrapped up. VibeThinker-3B solved 123 out of 128 problems on the first try. That puts it ahead of GPT-5.2, Qwen3-Max, Kimi K2.5, and Claude Opus 4.6. It trails only GPT-5.3-Codex, Gemini 3.1 Pro, and Gemini 3 Flash, but not by much.Ad VibeThinker-3B builds on Alibaba’s Qwen2.5-Coder-3B. Sina’s contribution is the post-training, everything that happens after generic pre-training on large data sets. According to the report, that’s what brings a 3B model close to the top performers.