# GPT-5.6 Sol 分词效率比 Claude Opus 5 高 34.5%

- 来源：Tibo (@thsottiaux)
- 发布时间：2026-08-16 13:52
- AIHOT 分数：49
- AIHOT 链接：https://aihot.virxact.com/items/cmsvegh540hmqrosakt8hzpwy
- 原文链接：https://x.com/thsottiaux/status/2088866513008873560

## AI 摘要

OpenAI 的 token 与其他模型不等价，每百万 token 价格对比会误导决策。GPT-5.6 Sol 的分词器在测试中仅用 766 个 token，而 Claude Opus 5 需约 1,170 个，少 34.5%。真正该比较的是每次成功结果的价格，而非单价。

## 正文

On tokens and prices per token.

I said I'd write more about this, so here goes: an OpenAI token != another model's token. We compare AI prices in dollars per million tokens as if a token were a standardized unit, like a gram or a kilowatt-hour. It isn't. Different models use and produce the exact same text using different numbers of tokens, which means a lower price per token does not necessarily mean a lower bill.

Imagine two identical pizzas. One is cut into 8 slices at $2 each. The other is cut into 16 slices at $1.25 each. The second place advertises cheaper slices, but the whole pizza costs $20 instead of $16. Bummer ... your stomach doesn't actually care about the number of slices you just ate.

I know you are hungry now, but back to tokens. In one small comparison spanning English, technical, multilingual, and numerical text, the tokenizer we use for GPT-5.6 Sol used 766 tokens versus an estimated 1,170 for Claude Opus 5. That's a very significant difference of about 34.5% fewer tokens. You can get the same exact text, but pay for all those extra tokens. The price per token doesn't really tell this story.

Even correcting for tokenizer differences misses the bigger point. What actually matters is price per successful outcome, and for that you can use benchmarks as a starting point, but really you have to try it and measure on your own use cases.

That's all. May the tokens flow.