Unverified50% confidenceFactExact time
A Google DeepMind paper indicates that having a smaller model spend more time in deep thinking and generating hundreds of thousands of tokens can outperform a model 14 times its size on problems where the smaller model has some baseline probability of success.
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50%
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Long-term
Relevance
10/4/2026
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Related Claims
VerifiedDeepMind research has shown that on certain mathematical reasoning tasks, smaller models can surpass models with larger parameter counts by increasing the amount of inference-time computation.80% similarVerifiedGoogle DeepMind has proposed methods related to Nested Learning.77% similarUnverifiedDeepSeek minimal mode paired with Max tends to elicit the model's strongest reasoning capabilities, at the cost of longer response latency and higher token consumption.75% similarVerifiedGoogle DeepMind's Chinchilla paper optimized the optimal ratio between model parameter count and training data volume.74% similar
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