# 研究通过知识问题估算LLM参数规模

- 来源：Deedy (@deedydas)
- 发布时间：2026-04-30 00:17
- AIHOT 分数：50
- AIHOT 链接：https://aihot.virxact.com/items/cmoka80zg00inslegz8uvqsv3
- 原文链接：https://x.com/deedydas/status/2049523583517634862

## AI 摘要

研究人员通过询问不同难度知识问题，估计大型语言模型参数大小。结果显示，GPT 5.5约10T参数，Claude Opus 4.x约4-5T，Grok 4约3T。事实性知识容量与模型规模呈对数线性关系。论文提出7个知识层级，最高层级T7对所有模型接近零，表明预训练仍有显著提升空间。Gemini 3.1 Pro可能超过10T参数。此方法有助于推断模型训练成本及后训练在非事实性任务上的性能。

## 正文

Researchers just estimated the size of all the LLMs by asking it knowledge questions of varying degrees of obscurity！

- GPT 5.5： ~10T params
- Claude Opus 4.x： ~4-5T
- Grok 4： ~3T

The idea here is that factual capacity scales log-linearly with size. The paper shows 7 knowledge tiers and T7 is essentially ~0% for all models， suggesting there is still significant headroom for pretraining. Gemini 3.1 Pro is likely >10T given its used as an anchor but has no direct estimate.

This means we can infer what different models might cost to some degree and their post-training effectiveness （performance at certain non-factual tasks given its size）.

One of the coolest papers I've read of late.