LLM Cost Comparison — the same workload, priced across six models

The LLM cost comparison tool above takes one workload — a fixed input token count, output token count, and monthly request volume — and prices it across six models: GPT-4o, GPT-4o mini, Claude Sonnet, Claude Haiku, Gemini Pro, and Gemini Flash. Every price field is editable, pre-filled with illustrative 2025-era figures you should update against current published rates before making a real budgeting decision.

Arb Digital runs this same comparison internally whenever we help a client choose a model for a production feature — because the "best" model on a leaderboard is rarely the right one for a specific job once cost enters the picture, and the gap between options is often much larger than people expect.

What This Tool Does

Rather than comparing models by capability or benchmark score, this tool holds your actual workload constant — the same input size, output size, and request volume — and calculates what that identical job would cost on each of six models. It ranks them by total monthly cost, shows the cheapest and most expensive options, calculates the spread between them, and breaks out a cost-per-1,000-requests figure so you can compare pricing independent of your specific volume.

This turns an abstract pricing page into a concrete number tied to your actual usage pattern, which is a much more useful basis for a real decision than comparing raw per-token rates in isolation.

How to Use It

1. Enter your workload. Set input tokens per request, output tokens per request, and how many requests you expect to run per month.
2. Check the pricing rows. Six models are pre-filled with illustrative input/output prices per million tokens — edit any of them to match current published rates.
3. Read the ranking. The headline shows your cheapest option and its monthly cost; the table below ranks all six models by total monthly spend.
4. Check the spread. The frontier premium figure shows how many times more the most expensive model costs versus the cheapest for the exact same job.

The Formula / How It's Calculated

For each model, monthly cost is ((input tokens per request ÷ 1,000,000) × input price) + ((output tokens per request ÷ 1,000,000) × output price), multiplied by requests per month. Input and output are priced separately because virtually every commercial provider bills them at different per-token rates, with output typically costing several times more than input. This structure follows how pricing is actually published — see OpenAI's API pricing page, Anthropic's pricing page, and Google's Gemini API pricing page, all of which quote separate input and output rates per million tokens.

The default prices loaded into this tool are illustrative 2025 figures meant to demonstrate realistic order-of-magnitude differences between frontier and smaller models — they are not live, auto-updated rates. Providers change pricing periodically, sometimes without much notice, so always confirm current numbers on the official pricing pages above before finalizing a budget or a vendor decision.

Why the Same Task Can Cost 50–100x More

The most important thing this comparison makes visible is how large the gap between a frontier model and a smaller sibling model actually is. It's common for a flagship model's per-token price to run fifteen to twenty times higher than its own smaller variant, and when you compare the smallest available model from one provider against the largest flagship from another, the spread can reach fifty to one hundred times for the identical workload. At meaningful volume — tens of thousands of requests a month — that gap stops being a rounding error and starts being the difference between a sustainable product and an unsustainable one.

This doesn't mean smaller models are always the right call. Quality, reasoning depth, and reliability on complex tasks genuinely differ between model tiers, and that has to be benchmarked separately against your actual use case — this tool prices the workload, it doesn't grade the output. But for a large share of production tasks — classification, extraction, short-form generation, routing, simple Q&A, tagging — a small or mid-tier model performs the job just as well as a frontier model, and paying frontier prices for it is money left on the table every single month.

When the Frontier Model Is Worth It

• Complex, multi-step reasoning. Tasks that require holding several constraints in mind at once, planning, or nuanced judgment often see real quality gains from larger models.
• High-stakes, low-volume calls. If you're only making a handful of calls a month, the cost gap in absolute dollars may be small enough that quality should decide, not price.
• Long-context, dense documents. Some tasks benefit from a model's stronger handling of long, information-dense context even at a token-price premium.
• Anything customer-facing at the edge of quality tolerance. Where a wrong or awkward answer has real reputational cost, the premium can be justified by the failure rate it avoids.

For everything else — the bulk of high-volume, repetitive, well-defined tasks most production systems actually run — start by testing a smaller model against your real data before defaulting to the most expensive option on the list.

Building This Into a Real Decision

A responsible workflow pairs this cost table with a quality check: run your actual prompts against the candidate models, score the outputs against your own criteria (or a held-out human review), and only then weigh quality differences against the monthly cost gap this tool shows you. A model that's 5% better on a benchmark but 40 times more expensive for your exact workload is rarely the right production default — but a model that's meaningfully better on your specific task, at even a large price premium, can absolutely be worth it if the volume is low or the stakes are high enough.

A Worked Example at Volume

Take the default workload above: 1,500 input tokens and 700 output tokens per request, run 50,000 times a month — roughly the profile of a mid-sized support or content-generation feature. At illustrative 2025 rates, a mini or flash-tier model can land the entire monthly bill under a few hundred dollars, while the flagship model handling the identical volume can run into the thousands. That gap alone is often enough to fund a second engineer or a quarter of paid marketing spend, for work that a smaller model may complete just as reliably. Multiply the request volume by ten as a product grows, and the absolute dollar gap between tiers grows right along with it — which is exactly why this decision deserves revisiting every time volume meaningfully changes, not just once at launch.

Cost Per Request vs. Cost at Scale

It's easy to look at a per-request cost measured in fractions of a cent and conclude the model choice doesn't matter much. That intuition breaks down fast at scale. A cost difference of $0.01 per request looks trivial in isolation, but at 50,000 requests a month it's $500, and at 500,000 it's $5,000 — every month, indefinitely, for as long as that feature runs in production. This is why the monthly-cost and cost-per-1,000-requests figures in this tool matter more than the raw per-token price on a vendor's homepage: the number that actually hits a budget is the one multiplied by real usage, not the sticker price on its own.

Common Mistakes to Avoid

• Comparing only input price. Output tokens are usually priced higher and often make up the larger share of cost for generation-heavy workloads — always compare the full blended cost, not just the input rate.
• Defaulting to the flagship model for every task. Using a frontier model for simple classification or extraction work is one of the most common sources of avoidable AI spend.
• Never re-checking prices. Provider pricing changes periodically; a comparison built on stale numbers can misdirect a real budgeting decision.
• Ignoring request volume. A small per-token price difference multiplied across millions of monthly requests can dwarf a larger difference at low volume — always model your real scale.
• Skipping the quality check. Cost is only half the decision; validate that a cheaper model actually performs acceptably on your specific task before switching.

Related Free Tools From Arb Digital

To estimate the token size of a single request first, use the Prompt Token Estimator, which models system prompt, history, and output together. For a quick text-based token count, try the ChatGPT Token Counter, or convert between words and tokens with the Words to Tokens Converter. To check whether your workload fits a given model's context window, use the Token Limit Checker, and explore more with the GPT API Cost Calculator or the full AI Token Calculator collection. Browse our complete free online tools hub for more calculators.

Frequently Asked Questions