How to Compare LLM Model Prices per Million Tokens on llm-prices.com

Updated

Open llm-prices.com and use the Model prices (per million tokens) table to compare models side by side. Each row shows three cost columns — input, cached input, and output — so you can rank models on the dimension that matches your workload. Use the selection controls to narrow the table to the models you actually care about, then read the Compare Prices view, which also shows a last updated timestamp so you know how fresh the numbers are.

What the comparison table gives you

The table is the core of the site. Its columns are:

Column What it tells you
Model The model being priced
Input cost Price per million input (prompt) tokens
Cached input cost Price per million cached input tokens
Output cost Price per million output (completion) tokens

Prices are expressed per million tokens, which is the standard unit for normalizing API costs across providers.

Step-by-step: comparing models

  1. Open the calculator at llm-prices.com. The page loads with the model price table and the cost calculator fields.
  2. Narrow the table. Use the selection controls to pick the models you want to compare. The page offers Show just selected to display only your chosen models, and Clear selection to reset.
  3. Read across the three cost columns. For each model, note input, cached input, and output prices. These are the numbers you compare — not a single blended figure.
  4. Rank by your dominant cost driver. If your workload is prompt-heavy (long documents, RAG context), input and cached input dominate. If it generates long answers, output dominates.
  5. Check the last updated time. The Compare Prices table shows when prices were last refreshed. If it looks stale, verify against the provider's own pricing page before committing.

Estimating your own cost, not just the per-token price

Per-million-token prices are unit prices, not your bill. To get an estimate:

  1. In the calculator, enter number of input tokens, number of cached input tokens, and number of output tokens for a representative request.
  2. Enter the cost per million figures for input, cached input, and output.
  3. Read the Total cost output.

Multiply that per-request total by your expected request volume to project monthly spend.

The catch: token-based comparisons aren't exact

The site itself notes that different models use different tokenizers, so direct token-based price comparisons may not be entirely accurate. The same text can produce different token counts across models, which means a cheaper per-million price doesn't always mean a cheaper real-world bill. Treat the table as a strong first filter, then sanity-check with token counts from the actual tokenizer of each model you're considering.

Practical comparison checklist

  • Compare all three columns, not just output price.
  • Weight the columns by your real input/output ratio.
  • Use the Show just selected filter to keep the comparison readable.
  • Confirm the last updated date before relying on a number.
  • Re-check tokenizer differences if two models are close in price.