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LLM cost comparison

LLM Cost Calculator

Compare LLM cost scenarios across providers and model tiers before choosing the API behind your product workflow.

Estimate LLM costs across providers

LLM cost planning starts with the same core variables across providers: model choice, request volume, input tokens, output tokens, and active usage days. This page gives you a neutral framework for comparing those scenarios.

Calculator shortcut

Open the calculator, choose a provider and model, then adjust usage assumptions to compare scenarios.

Compare LLM API costs

Benefits

Provider comparison

Review OpenAI, Claude, Gemini, and other model assumptions through one calculator workflow.

Token-based planning

Understand how prompt size and response length shape the cost of each LLM call.

Monthly forecasting

Convert daily usage into monthly and yearly estimates for planning discussions.

Use cases

Model selection

Compare cost ranges before testing quality, latency, and reliability.

Procurement reviews

Give stakeholders a simple forecast before selecting or changing an LLM provider.

Feature planning

Estimate AI costs for search, chat, summarization, extraction, and copilots.

Pricing estimation warning

LLM pricing changes over time and may include discounts, caching, batch pricing, or free tiers not represented in a simple estimate.

Launch checklist

Make the estimate more useful

A few practical checks help developers and founders avoid surprises after real users arrive.

Common cost mistakes

Forgetting retries, long context, power users, and generated output length.

How to reduce AI API costs

Shorten prompts, cap output length, cache repeated answers, and route simple tasks to cheaper models.

Cheaper vs stronger models

Use stronger models when accuracy or reasoning changes the outcome; use cheaper models for routine work.

Before launching an AI feature

Ask who triggers requests, how often, how long responses are, and what happens during usage spikes.

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FAQ

What is the best way to compare LLM costs?

Use the same request volume and token assumptions for each provider, then compare estimated monthly cost alongside quality and latency.

Do token estimates need to be exact?

No, but they should be realistic. Use low, expected, and high token scenarios so the budget has a range.

Why do LLM API costs differ so much?

Providers price models differently based on capability, speed, context size, output tokens, and product-specific pricing rules.