Tokened — Guides & Insights https://tokened.uk/blog/ LLM token efficiency, prompt cost control and compliance notes from tokened.uk. en Wed, 24 Jun 2026 00:00:00 GMT How Much Do Chinese Prompts Really Cost on OpenAI? (Measured) https://tokened.uk/blog/chinese-token-cost/ https://tokened.uk/blog/chinese-token-cost/ Wed, 24 Jun 2026 00:00:00 GMT 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.

Tokened.uk runs entirely in your browser so you can compare models side-by-side without sending data to our servers.

]]> How Prompt Caching Can Cut LLM API Spend by 40% https://tokened.uk/blog/prompt-caching-savings/ https://tokened.uk/blog/prompt-caching-savings/ Wed, 24 Jun 2026 00:00:00 GMT Identical system prompts resent on every request are wasted spend. Learn how edge caching and Tokened Gateway solve this. The duplication problem

Most chat apps send the full system prompt on every message. If your system prompt is 800 tokens and you handle 100k messages per day, you are paying for 80 million duplicate tokens daily — only the last user message actually changed.

Cache at the edge

Tokened Gateway (beta) deduplicates identical prompt prefixes at the proxy layer. Point OPENAI_BASE_URL to api.tokened.uk and identical requests hit cache before reaching the provider.

For read-heavy workloads (support bots, documentation Q&A), teams typically see 25–40% cost reduction in early tests.

]]> GDPR, PIPL, and Why Local Token Analysis Matters https://tokened.uk/blog/gdpr-pipl-local-analysis/ https://tokened.uk/blog/gdpr-pipl-local-analysis/ Wed, 24 Jun 2026 00:00:00 GMT Sending prompts with PII to US-hosted LLMs creates compliance risk. Local-first tooling is the safest first step. Data leaves your control fast

When a developer pastes a customer email, UK postcode, or Chinese national ID into ChatGPT for "quick debugging", that data may be processed in the US under a different legal framework than GDPR or PIPL expects.

Local-first is step one

Tokened counts tokens entirely in-browser — your prompt never touches our servers. Upcoming PII pre-check will flag sensitive patterns before you send anything upstream.

Gateway adds a compliance layer: intercept, redact, or block prompts containing regulated data before they cross borders.

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