How Much Could You Save by Switching AI Models?

It is common to choose an AI model, find that it works, and keep using it. Meanwhile, new models arrive and prices change.

That raises a practical question: would the same work cost less on another model?

A normal price table cannot answer that very well. One person may send long documents and want short summaries. Another may ask brief questions that produce long, heavily reasoned answers. The cheaper model depends on the work.

The NanoGPT Cost Simulator uses your own recent usage to make the comparison more relevant. Think of it as taking a recent grocery receipt to several other shops and asking what the same basket would cost there.

Use the result as a comparison, not a prediction of your next bill.

What the Cost Simulator actually compares

Start by choosing a period:

• Today
• This week
• Last week
• The last 30 days
• Your current billing cycle, when available

Next, select a model you used during that period and up to 30 alternatives. The simulator takes up to the 100 most recent matching text generations, then calculates what that same recorded usage would cost at the current API prices of the comparison models. Image, video, and audio generations are not included.

The calculation preserves the recorded size of your prompts and answers. It also accounts for recorded reasoning, search, citations, and prompt caching—a discount some models offer when you reuse the same large input. The alternative model is priced as if it had produced the same usage.

That last sentence is the main limitation. A different model may not produce the same answer length or use the same amount of reasoning in a real request. The simulator holds those details steady so you can compare prices on equal terms.

Why your own usage beats a generic price table

Model prices usually separate input from output. Input is what you send; output is what the model generates.

That creates very different cost patterns:

• Long documents with short answers: Input pricing matters more.
• Short prompts with long answers: Output pricing matters more.
• Reasoning-heavy work: The extra internal work a reasoning model performs before answering can become a large part of the total.
• Repeated work with the same large material: Prompt-cache pricing may matter.
• Web-enabled questions: Search and citation charges can affect the result.

A model with a low input price may be attractive for large-document analysis but less appealing for long-form generation. A model with an inexpensive output rate may suit coding or drafting even if its input price is not the lowest.

The Cost Simulator reflects the balance found in your sample rather than an average user's workload.

How to run a useful comparison

Open the Cost Simulator and choose a period that represents normal work.

Today is useful for a quick check, but it can be misleading if today's work was unusual. Last week gives you a complete week when the current one is still in progress. This week or Last 30 days usually gives a more representative mix. A billing-cycle view is helpful when you want the estimate to line up with your current subscription period.

Next, choose a model you used often enough to create a reasonable sample. The simulator can analyze up to 100 matching generations. If it finds fewer, it tells you how many were available.

Then choose a small set of plausible alternatives. You can compare as many as 30, but starting with three to five makes the results easier to interpret. Include:

• A lower-cost model you think may be good enough
• A balanced model you would realistically use every day
• A stronger model if you want to see the cost of moving up rather than down

Run the simulation and look for differences large enough to justify a real test. A tiny percentage change may not be worth the effort of rewriting prompts and checking integrations, or the risk of lower quality. A large recurring difference deserves closer attention.

Reading the two source-model costs

The result shows both What you were charged and Your model at API pricing.

They are not necessarily the same number.

What you were charged is the recorded charge for the requests in the sample. Your model at API pricing reprices that same sample using the model's current API rates.

The figures can differ when prices have changed or when the original requests were made through a NanoGPT interface whose charges do not match API pricing exactly. The simulator warns you when the difference is substantial.

The comparison table measures each difference against the current API-pricing figure, not against what you were originally charged. Do not assume the target-model estimate can be subtracted directly from an entire monthly invoice: the table covers the sampled generations, not all usage in the selected period.

Pay attention to “May not fit”

A cheaper model is not an alternative if it cannot accept your prompts.

The simulator checks the longest input in the sample against each target model's input limit. If a target has a smaller limit, it receives a May not fit warning.

This is especially important for large documents, long conversations, and repository work. You may still be able to use the cheaper model by shortening the prompt, splitting the task, or keeping a smaller working summary. That is a workflow change, not a like-for-like switch, so its estimate should be treated separately.

The model's input limit—how much material it can accept at once—is not the only compatibility question. Before switching, check that the target supports the inputs and features you rely on, such as images, files, tools, a required output format, or a specific reasoning setting.

What the other warnings mean

A small sample

If fewer than 100 matching generations were found, the result shows the actual count. A small sample can still be useful when your requests are consistent. It is less reliable when your workload changes from day to day.

Try a longer period or run the comparison again after more normal usage.

Caching is approximate

Prompt caching is not identical across models. A target may charge differently for repeated input, handle cache creation differently, or lack an explicit cache rate. In that case, the simulator uses fallback cache pricing and warns that the estimate is approximate.

If your sample includes substantial cached input, treat the comparison as a direction rather than an exact quote.

Reasoning is approximate

The simulator can reprice recorded reasoning usage, but a different model may reason for a different length on the same task. One model might reach an answer quickly; another might do far more internal work before responding.

The simulator cannot predict that behavioral change because it holds the recorded reasoning amount steady. A model with low published rates can still cost more than expected in practice if it reasons at greater length or writes more verbose answers.

Search and citations can change

Search costs are repriced, but another model may search differently or use a different number of sources in practice. The estimate assumes the same recorded search pattern.

Test web-enabled workflows directly before relying on the simulated number.

What the simulator cannot tell you

The Cost Simulator compares prices. It does not measure whether another model is better for your work.

It does not predict:

• Answer quality or factual reliability
• Speed and time before the first response
• Whether the model will write longer or shorter answers
• How much reasoning it will use on future requests
• Whether your prompts need to be rewritten
• How well it handles your tools, files, or preferred output format

A model that costs half as much but requires twice as many retries may not save anything. A more expensive model can be the better value when it completes difficult work correctly on the first attempt.

The estimate is best used to narrow the field before direct testing.

Test the shortlist before switching

Once the simulator identifies promising alternatives, take a small set of real tasks and run them on the source model and the candidates.

Compare:

• Whether the answer is correct and complete
• How much editing or retrying it needs
• How quickly it responds
• Whether it follows your formatting instructions
• Whether it supports the context and features you use
• The actual charge after several representative requests

If the work is varied, test more than one category. A cheaper model might be excellent for summaries and extraction but weaker for coding or complicated analysis. You may save more by routing routine work to it while keeping the stronger model for difficult requests.

Export the simulator results as CSV if you want to keep a record, share the shortlist, or compare the same models again after prices change.

A sensible switching rule

Do not switch because one model has the lowest number in one simulation.

Switch when three things are true:

1. The estimated saving is meaningful for your actual volume.
2. The alternative fits your longest normal prompts and supports the features you need.
3. It performs well enough on a representative set of real tasks.

Recheck the comparison occasionally. New models arrive, prices move, and your own usage can change. A model that was the right default six months ago may still be the right choice, or it may just be the one you never revisited.

Try the NanoGPT Cost Simulator with a model you have used recently. For advice on spending limits, billing modes, and separating workloads across API keys, read our broader guide to controlling AI API spending.