Our research seems to indicate you can get a decent signal based on the first few reasoning trace summaries. We analyzed hundreds of billions of tokens to figure this out. Any open-weights model also gives you reasoning, which is very helpful (because they are usually lower-quality candidates you would want to cut off early)

The largest models are very well known for having large reasoning traces. The thing that cracks down on overthinking is reasoning-effort training, which is largely a function of how the model is trained (certain RL objectives/teachers) rather than size of the model. The Kimi K3 technical report has some excellent work on this: https://arxiv.org/pdf/2607.