Sina's open model VibeThinker-3B aims to show reasoning compresses well but factual knowledge doesn't
3B parameters. 333x smaller than DeepSeek V3.2. Same math and coding scores. Sina's VibeThinker-3B just proved reasoning compresses—but knowledge doesn't.
Why it matters
VibeThinker-3B challenges conventional wisdom that model capability requires scale, demonstrating that reasoning can be efficiently compressed through multi-stage post-training while exposing a fundamental tradeoff: logical reasoning vs. factual knowledge retention. This has direct implications for edge deployment and cost optimization strategies.
The key facts
5 to knowVibeThinker-3B: 3 billion parameters
Matches DeepSeek V3.2 and Kimi K2.5 on math and coding benchmarks
333x smaller than comparable models
Multi-stage post-training approach
Hypothesis: reasoning compresses well, broad world knowledge does not
Go to the source
The Decoderthe-decoder.com
Publisher excerpt: 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. The secret isn't size but multi-stage post-training. The researchers propose a hypothesis based on their…