Gemini vs OpenAI cost calculator
Google’s Gemini and OpenAI’s GPT models cover similar capability tiers at very different prices. This calculator maps Gemini Flash and Pro against GPT-4o mini, GPT-4o, and o1 for your exact token profile, so you can see the cost gap for your workload before you pick a provider.
How it works
You enter the prompt and completion tokens for a typical request and pick
a model pair. Each model is priced as (prompt ÷ 1M × input price) + (completion ÷ 1M × output price), multiplied by your daily volume. The same
inputs feed both sides, so the only thing that varies is the published rate.
The output shows per-request and daily cost for each model, the cheaper option, and the gap as both a percentage and a dollar figure — the number that actually moves your monthly bill.
Tips and notes
- Flash is built for volume. Gemini Flash often undercuts GPT-4o mini meaningfully, which matters most on high-throughput, cost-sensitive workloads.
- Reasoning tiers do not map cleanly. o1-class reasoning models are priced well above standard chat models and have no exact Gemini equivalent — compare on task quality, not just price.
- Prices are editable estimates. Google and OpenAI revise rates regularly; confirm current pricing in each console before budgeting.
How to interpret the comparison
Input-heavy vs output-heavy workloads
Most LLM pricing separates input tokens (the prompt you send) from output tokens (the model’s response). These are priced differently — typically output tokens cost more than input tokens — and the ratio in your workload matters enormously. A retrieval-augmented generation (RAG) pipeline that sends large context documents as input but returns short answers is input-heavy; a code generation workflow is output-heavy. The calculator lets you enter your actual prompt and completion token split so the comparison reflects your real cost structure.
When Flash is the obvious choice
Gemini Flash was designed for high-volume, latency-sensitive, cost-constrained tasks: classification, entity extraction, routing decisions, summarization of standard documents, and customer-support drafts. For these tasks the quality difference versus a more expensive model is often negligible, but the cost difference can be an order of magnitude. If you are running millions of requests per day, the choice of model at this tier is probably the single largest line item in your AI budget.
When model quality differences dominate price
For tasks where the model’s reasoning or knowledge depth determines whether the output is usable at all — complex legal analysis, novel code generation, multi-step research — a cheaper model may fail on a significant fraction of requests, requiring expensive reruns or human review that eliminates the apparent savings. In those cases, test on a representative sample of your real inputs before committing to the lower-cost option.
Context window and modality
Gemini models have historically offered very large context windows, which makes them strong candidates for tasks that involve long documents. If your prompt regularly exceeds a certain length, the available context window may constrain which model you can use regardless of price. Similarly, if your workload uses images or audio alongside text, verify that the specific model version you select supports those modalities — not all tiers do.
Latency and rate limits
Cost per token is not the only operational variable. Both providers impose rate limits at various tiers, and the speed of a response matters for real-time applications even when the cost is acceptable. For high-throughput production workloads, verify the tokens-per-minute and requests-per-minute limits at your tier before committing to either provider.