How Much Do Chinese Prompts Really Cost on OpenAI? (Measured)
Measured: cl100k_base spends ~1.10 tokens per Chinese character, o200k_base only ~0.64. Where the Chinese token penalty still bites, and where the real saving comes from today.
The hidden tax on Chinese text
The Chinese token penalty is real, but it is a cl100k_base problem. Measured on ordinary prose, cl100k_base — the tokenizer behind GPT-4-era deployments — spends about 1.10 tokens per Chinese character. o200k_base, used by GPT-5.x and GPT-4o, spends about 0.64. A CJK-optimised vocabulary such as DeepSeek-V4-Flash or Qwen3.7-Plus lands near 0.6.
So switching a Chinese workload from GPT-4-era models to a CJK-optimised one still cuts roughly 40% of the token count, but moving from o200k_base saves only around 10%. English text is unaffected either way: no vocabulary gives you a discount on ASCII.
What you can do today
The bigger lever today is price per token, not token count. As of October 2026, DeepSeek-V4-Flash (served as V4.1-Flash) lists a peak cache-miss input price of $0.30 per million tokens against $2.50 for GPT-5.4 — about 8×, and off-peak hours are half of that. Either way the gap dwarfs any tokenizer difference. So: 1. Audit prompts with a local tokenizer before shipping, and check which encoding you are actually on. 2. Compare on total cost, not token count. 3. Trim system prompts — redundant instructions multiply across every call.
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