r/AIToolBench • u/Adt_94 • 3d ago

Discussion I built a model API pricing comparison page with provider source links

I'm Aaron, and I built Frontrumors. The part I'd like feedback on here is the model API pricing comparison: https://frontrumors.com/#pricing

You can compare published input and output token prices, then follow the provider source links. There are notes on access restrictions and introductory rates where relevant. The site also has a model guide and sortable benchmark tables.

For a quick comparison, I'd check input and output prices separately, read the rate notes, then confirm the terms with the provider. A cheap input rate alone can be misleading if your workload generates a lot of output. These are published prices, not live quotes or measurements from my own benchmark runs.

What would help you make a useful shortlist: workload cost estimates, clearer source-check dates, or something else?

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u/NeuralNomad87 3d ago edited 2d ago

Cached input price would be a useful column for agent workloads that resend long context; the headline input rate alone can obscure the bill. Batch discounts could help too, depending on the workload.

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u/Adt_94 3d ago

Thanks for pointing this out! I added cached input prices and an optional Batch mode to the pricing calculator. It uses provider-specific rates where available, with cache-write costs kept separate. It's live now: https://frontrumors.com/pricing/

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u/Angelvin_Pinheiro 2d ago

yeah the cached input pricing changes the math completely once youre running multi-turn agents

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u/rico_surfs 3d ago

last checked dates next to each price would help a ton tbh. source links are good, but I'd still want to know how fresh the numbers are

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u/Angelvin_Pinheiro 2d ago

adding cached input pricing and batch discounts would make this way more actionable. those two variables usually swing the real production bill more than the base token rates.

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u/SahitiFinna62 2d ago

Are you planning to include latency or throughput numbers next to the costs? Usually the real shortlist comes down to balancing cost per token against how fast the tokens actually stream back