Why normalize token prices at all?

Raw price-per-token rewards weak, cheap models. A model that costs a tenth as much but scores far lower on quality is not actually a better deal for most tasks. Normalizing by a quality score reveals which model gives the most capability per dollar.

What does the value index mean?

It is price divided by quality score — cost per point of quality. A lower number means you pay less for each unit of measured capability, so the lowest index is the best value. It is a ratio, not a dollar amount.

What is a blended token price?

Most models price input and output tokens differently. A blended price weights them by your typical input-to-output ratio into a single per-million figure, so you can compare models on one number. Use a blend that reflects your real workload.

Which quality metric should I use?

Use the one closest to your task. MMLU captures broad knowledge, MT-Bench and Arena Elo capture instruction-following and chat quality. For a specialized use case, plug in your own eval score as the custom metric for the most relevant comparison.

Is my data sent anywhere?

No. The tool runs entirely in your browser. Nothing you enter is uploaded, stored or logged.

Token Price Normalization Tool

Token price normalization tool

Comparing LLMs on price-per-token alone is a trap: it makes the cheapest, least capable model look like the winner. The honest question is how much capability you get per dollar. This tool normalizes each model’s price by a quality score and ranks them by cost per quality point, so a slightly pricier but much stronger model gets the credit it deserves.

How it works

For every model you supply a blended token price and a quality score on a common benchmark. The normalized value index is simply:

value_index = price_per_million_tokens ÷ quality_score

This is cost per point of quality — a lower number is better value. The tool sorts all models by this index and stars the best one. Because every model is scored on the same metric, the comparison is apples-to-apples even when the raw prices differ by an order of magnitude.

Tips and notes

Use a blended price that matches your real input-to-output ratio — a model that is cheap on input but expensive on output will look different for a summarization workload than for a generation-heavy one. Pick the quality metric closest to your task: MMLU for broad knowledge, MT-Bench or Arena Elo for chat and instruction-following, or your own eval score for a specialized domain. The value index is a starting filter, not the final word — also weigh latency, context window, rate limits and reliability. But for cutting through marketing claims about “10× cheaper tokens,” normalizing by quality is the single most clarifying move.