Unverified50% confidenceFactExact time
The evaluation results show that the relationship between reasoning intensity and accuracy is not linear; at low intensity levels, some tasks exhibit obvious reasoning breaks, while the accuracy improvements brought by high intensity levels are often accompanied by a significant rise in token cost.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
10/13/2026
First Seen
Valid until: 1/11/2027
Sources
Related Entities
Related Claims
VerifiedThe Reasoning Effort parameter is divided into levels such as low, medium, high, and extra high; the higher the level, the stronger the reasoning capability, but token consumption and latency also increase proportionally.76% similarUnverifiedIn machine learning evaluation, precision and recall often have a trade-off relationship, and increasing recall typically leads to more genuine products being misclassified.75% similarUnverifiedBenchmarks have distributional bias, as the task types, difficulty distribution, and repository scale in test sets may not represent real-world development scenarios, and Max mode boosts scores but significantly increases Token consumption.75% similarVerifiedHigh-intensity reasoning configurations typically entail higher costs and longer response times, trading response speed for logical rigor.75% similarUnverifiedThe cost-per-unit-of-performance ratio depends heavily on the task's baseline accuracy: the lower the baseline, the greater the relative benefit of a small improvement; when the baseline is already very high, the same marginal cost may buy only fluctuations close to noise.74% similar
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