Provider comparison
Review OpenAI, Claude, Gemini, and other model assumptions through one calculator workflow.
LLM cost comparison
LLM Cost Calculator
Compare LLM cost scenarios across providers and model tiers before choosing the API behind your product workflow.
Side-by-side model cost
Compare LLM model costs
Choose two or three models and compare the same request volume and token assumptions side by side.
Comparison results
Enter your usage details, then select Calculate estimate to see your projected cost.
Estimated cost = input usage cost + output usage cost + supported optional charges.
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 costsBenefits
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.
Related planning resources
Continue with the most relevant provider, guide, comparison, or calculator for this page's distinct planning intent.
LLM API pricing
Compare current provider and model unit prices with verification details.
Compare Claude Sonnet vs GPT-4o Cost
Claude Sonnet resolves to exact canonical Anthropic pricing. GPT-4o is absent from current CostRivo pricing data and remains unresolved.
Compare Gemini Flash vs GPT-4o Mini Cost
Gemini Flash resolves to exact canonical Gemini pricing. GPT-4o mini is absent from current CostRivo pricing data and is not replaced.
Compare GPT-4o Mini vs Claude Haiku Cost
Claude Haiku resolves to an exact canonical model. GPT-4o mini is absent from current CostRivo pricing data, so no OpenAI model price is substituted.
Compare OpenAI and Anthropic API pricing
Compare verified OpenAI and Anthropic model prices, calculated unit-price differences, and one explicitly defined monthly token workload.
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.
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.