Compare estimated API costs for OpenAI and Anthropic models using the same token usage assumptions. Enter your monthly prompt and completion volumes plus each provider’s per‑million token pricing to see a side‑by‑side total.
An OpenAI vs Anthropic cost calculator helps teams estimate and compare monthly API spend based on token usage. Since both providers typically charge separately for input tokens and output tokens, even small differences in pricing can materially affect costs at scale.
This type of calculator is most useful when you already know your approximate traffic, prompt size, and response size. By entering monthly input and output token totals along with each model’s current per‑million token rates, you can quickly see which option is more budget friendly for your workload.
Keep in mind that raw token price is only one part of the decision. Model quality, latency, context window, tool use, caching, rate limits, and prompt efficiency can all change your real-world cost profile, so this comparison works best as a first-pass budgeting tool rather than a complete procurement analysis.
The calculator multiplies your monthly input tokens by the provider’s input price per million tokens and your monthly output tokens by the output price per million tokens, then adds the two amounts together.
Most LLM APIs price prompt tokens and generated tokens at different rates. Separating them gives a more accurate estimate, especially for applications with long prompts or long responses.
Yes. API pricing changes over time and varies by model, so you should enter the latest published prices for the exact OpenAI and Anthropic models you want to compare.
In 2026, one easy mistake is comparing only list input/output prices and ignoring prompt-caching rules. For long system prompts, tool schemas, or repeated context, your real cost can swing a lot depending on whether the provider bills cache writes and cache reads separately, how long the cache lives, and whether tiny prompt changes break reuse. Before trusting this calculator, run a 1,000-request sample with your actual prompt template and measure cache-hit rate. A model that looks pricier on paper can end up cheaper if your workload reuses context heavily.