Unverified50% confidenceFactExact time
In complex reasoning tasks, the number of reasoning tokens can far exceed the number of final output tokens, which is why the usage cost of the o1/o3 series is typically higher than that of GPT models at the same tier.
1
Sources
50%
Confidence
Medium-term (~90 days)
Relevance
9/2/2026
First Seen
Valid until: 12/1/2026
Sources
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Unverified标准注意力机制的计算复杂度为O(n²)，序列长度翻倍时计算量增加四倍，这是早期GPT-3仅支持4K Token的根本原因64% similarUnverifiedThe core idea of model routing is that not all tasks require the most powerful model; simple tasks can use lightweight models such as GPT-4o-mini, Claude 3 Haiku, or Llama 3.1 8B, potentially at just 1/50 or even 1/100 the cost of flagship models.62% similarUnverifiedIn the AID benchmark, o1 high (corresponding to o1 pro) scored above 60 in programming ability, outperforming o3-mini-high, though the two are very close.61% similarUnverifiedQwen3-4B is already on par with DeepSeek V3 or GPT-4o on multiple reasoning evaluation metrics.61% similarUnverifiedK2.6 uses only approximately 3% of its total parameters per inference, making its inference cost comparable to that of a 32B dense model.60% similar
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