AI Model Cost Comparison Calculator

Every major LLM provider charges meaningfully more per output token than per input token -- commonly two to five times more -- because generating each new token requires a full forward pass through the model, while processing input tokens can be batched and cached more efficiently. Keeping the two separate, and showing an input cost share, lets you see whether your workload's cost is driven mainly by long prompts (input-heavy, like document analysis) or long generations (output-heavy, like content writing).

Instead of a metered per-token price, it estimates GPU compute cost: total tokens processed divided by an assumed processing throughput, multiplied by an assumed hourly GPU rental rate. This is a much rougher approximation than the API models' published per-token prices, because actual self-hosted throughput and hourly cost vary substantially with the specific model size, how well requests are batched together, and which GPU hardware and cloud provider you actually use -- treat the self-hosted figure as a ballpark, not a quote.

Frontier and flagship-tier models (like a top-tier "Opus"-class model) charge substantially more per token than smaller or more efficient models, often by an order of magnitude, because they cost more to train and run. For a workload with identical token volume, switching from the most expensive model to the cheapest one in this comparison can cut your bill dramatically -- which is exactly why this calculator exists as a side-by-side comparison rather than a single-model estimator.

Not necessarily -- LLM API pricing changes frequently as providers compete, release new model versions, and adjust rates, sometimes with little advance notice. The figures here are a snapshot taken at one point in time, not a live feed from each provider. Always check the provider's own current pricing page before making a purchasing or architecture decision based on a cost comparison, and treat this calculator as a way to understand the RELATIVE shape of costs (which factors matter most) rather than an exact current-dollar quote.

Daily request volume is a pure multiplier on every cost component, so doubling it doubles daily cost exactly. Output token length, on the other hand, only scales the output-cost portion of the bill; the input-cost portion (driven by your prompt length) stays the same, so doubling output tokens doubles total cost by somewhat less than 2x whenever input tokens contribute a meaningful share of the total -- the exact multiplier depends on your input/output cost split.