Two providers both advertise $0.50 per million output tokens, why is that comparison still dishonest?

Different LLM providers use **different tokenizers**, and a tokenizer is what turns raw text into the discrete pieces (tokens) the model actually processes. OpenAI uses one BPE vocabulary (tiktoken with the cl100k or o200k family), Anthropic uses Claude's tokenizer, Meta's Llama-3 uses its own 128k-vocabulary BPE, Google's Gemini uses yet another. They were trained on different corpora with different merge rules, so the same sentence does not split into the same tokens across providers. For English prose, the spread between major families is roughly **10 to 30 percent**. For code, non-English languages, or domain text, the gap can be larger; a tokenizer trained heavily on English will fragment Japanese or Arabic text far more than one trained multilingually. The practical effect is that 'a million output tokens' means a different amount of delivered text at each provider. So when a finance spreadsheet ranks providers by **$/million output tokens**, it is comparing prices denominated in different units. If provider A is $0.50/M tokens and provider B is $0.55/M tokens, you cannot conclude A is cheaper without knowing how many tokens each one needs to produce the same response. If A's tokenizer is 20 percent more verbose than B's on your corpus, A's effective cost is $0.60/M of B-equivalent tokens, which is more expensive, not less. The correct comparison fixes the unit of value at something tokenizer-independent: characters of input text, words of output, or a normalized reference corpus. Tokenize a representative sample through each provider's tokenizer, multiply by their per-token rate, and compare the total dollars. That number is honest. The headline $/M-token rate alone is not.

**Tokens are not a universal unit.** Each provider's tokenizer is trained on its own corpus with its own BPE merge rules and its own vocabulary size. The same paragraph splits into a different number of tokens at each provider, typically a 10 to 30 percent spread on English prose, and larger on code or non-English text. That means $/M-token prices are denominated in **different units across providers** and only comparable within a family. A spreadsheet that ranks providers by $/M output tokens is comparing prices in different currencies as if the exchange rate were 1.00. The error compounds because the priced direction matters: input tokens are usually 3 to 5x cheaper than output, so output verbosity dominates the comparison. If provider A's tokenizer produces 20 percent more output tokens for the same delivered text, A's effective price is 20 percent higher than its headline rate. The **honest comparison** fixes the unit of value at something tokenizer-independent. Take a representative corpus of input prompts and target outputs (real production traffic if available), tokenize that corpus through each provider's tokenizer to get per-provider token counts, multiply by each provider's $/token rate for input and output separately, and compare the total dollars per response or per million-character delivered. This is exactly how serious procurement decisions actually work. The 'cheapest by headline rate' provider is often not the cheapest by delivered cost. The unit that actually matters to the buyer is dollars per useful answer; the tokenizer is just the intermediate accounting layer.