Test-Time Compute
Test-time compute is the processing a model spends while answering, rather than during training. Letting a model 'think' longer at answer time — exploring and checking more before it commits — raises accuracy on hard problems without retraining the model.
Also known as: inference-time compute, test time compute, inference-time scaling
For years, the way to get a better answer was a bigger model trained on more data. Test-time compute is the other axis: keep the model fixed and let it spend more effort while answering — generating a longer chain of thought, exploring several approaches, or checking its own work before responding. On math, code, and multi-step logic, more thinking measurably beats a snap answer.
This is the idea underneath reasoning models: they’re trained to use test-time compute well, turning extra inference into accuracy. DeepSeek’s breakthrough made the trade-off concrete — strong reasoning from heavier inference rather than a larger base model, which we unpacked on the show.
The cost is direct and worth budgeting: more test-time compute means more tokens, higher latency, and a bigger bill per answer. Spend it on the hard problems where being right matters, and keep it low for simple ones. See the AI reasoning overview for how it fits with chain-of-thought and reasoning models.
