Compare AI models by what it costs to produce an acceptable result for the same task—not by headline price per token. A useful comparison measures the real workload, including input and output, cached context, reasoning or intermediate usage, retries, and any tools or media, then considers cost alongside quality and latency.
Why price per token can give the wrong answer
A token rate is a billing rate, not a task price. The total depends on how much of each billable category a model uses to finish the work. Two models given the same assignment may consume different input and output volumes, use different amounts of reasoning or intermediate tokens, or need different numbers of attempts before producing an acceptable result.
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That means the model with the lower listed rate may not be the less expensive choice for your workload. A March 2026 arXiv preprint, “The Price Reversal Phenomenon: When Cheaper Reasoning Models End Up Costing More”, cautions that list prices can mis-rank actual reasoning-model costs. Treat that as a research finding, not a universal benchmark or provider ranking.
Define what counts as a successful task
Before comparing rates, write down a small set of representative tasks and an acceptance criterion for each. For example, for a support-response task, acceptance might require a correct answer, no unsupported claims, and a reply in the required format. Use the same task instructions and inputs for every candidate model.
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The criterion matters because a low-cost response that fails review is not a completed task. Track accepted completions, failures, and retries so the comparison reflects the work needed to reach an acceptable result rather than just the first call.
Measure the workload each model actually uses
Run the task set with the model settings and workflow you expect to deploy. Record usage per attempt, not an assumed shared token mix. A practical measurement sheet should include:
- Model name and version, region, date, and processing mode.
- Input tokens, cached input or cache writes, and output tokens.
- Reasoning or intermediate usage, when separately reported.
- Retries and tool calls needed to reach an accepted result.
- Image, audio, video, URL-context, code-execution, or other feature usage.
- Whether processing must be interactive or can run asynchronously.
Usage can vary from one representative task to another. When that variation is material, report a range or distribution rather than presenting one unusually cheap or expensive request as typical.
Calculate cost using the applicable billing categories
Use the provider’s current rates for the exact model, region, platform, and processing mode you plan to use. Keep each rate category visible in your estimate instead of blending everything into one per-token number.
OpenAI’s enterprise token-rate explanation describes the calculation as input tokens multiplied by the input rate, plus cached-input tokens multiplied by the cached-input rate, plus output tokens multiplied by the output rate, with each token count divided by one million. This is a provider-specific explanation for token-based pricing, not a universal formula for every provider or modality. OpenAI’s API pricing page covers additional model and feature pricing dimensions.
Apply each provider’s rules to the usage records you collected. For example, do not count cached input at a cache rate unless the request actually qualifies and records cache usage. Add distinct tool or modality charges where the pricing page specifies them.
Include caching, batch processing, tools, and media
Cached context
Caching can lower costs when a workload repeatedly reuses context, but the benefit depends on actual reuse. Estimate cache reads or hits and cache writes separately where applicable, and base the estimate on observed traffic. OpenAI describes prompt caching as a way to reduce costs for repeated input context in its Prompt Caching in the API documentation. Anthropic also documents cache-related pricing in its Claude Platform pricing.
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Batch processing
Use a batch rate only when the task can tolerate asynchronous processing and the specific model and request qualify. Anthropic’s pricing documentation describes batch discounts; check the current eligibility and terms rather than assuming they apply to every request.
Tools and media
Images, audio, video, URL context, code execution, built-in tools, and server-side tools can change the bill. Pricing rules vary by provider and feature combination, so check the current provider documentation for the actual workflow. OpenAI says built-in tool tokens are billed at the selected model’s per-token rates and that its API endpoints are not priced separately; confirm any tool-specific charges on the current OpenAI API pricing page. Anthropic notes that some server-side tools can add charges and that geography or platform can affect pricing. Google’s Gemini Developer API pricing page covers modality-specific input pricing and features such as URL context and code execution; managed-agent inference includes intermediate input/reasoning tokens according to that pricing information.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Compare cost per accepted completion
For each model, add the charges for all attempts needed to reach accepted results, then divide total spend by the number of accepted completions:
Cost per accepted completion = total spend across attempts ÷ accepted completions
Keep the number of failures and retries alongside this figure. If a model has a cheaper first attempt but more failures, retry costs can erase the apparent savings. Compare models on the same task set and acceptance criteria, and report quality and latency with cost. A model that meets the quality bar but misses an interactive latency requirement may not be suitable even if its cost per accepted result is low.
Use a comparison table that exposes assumptions
For each candidate, include at least the following in your evaluation report:
- Cost per accepted task and the workload or task set used to calculate it.
- Quality, including the task success or failure rate.
- Latency and whether the work is interactive or asynchronous.
- Input/output mix and separately reported reasoning or intermediate usage.
- Cache-hit and cache-write assumptions, tool calls, and modality mix.
- Batch eligibility, region, platform, model/version, and the date rates were checked.
Do not claim a universal cheapest model from a rate card alone. Provider pricing changes, and the right result depends on your task mix, success standard, usage pattern, and applicable charges. Recheck the official OpenAI pricing, Anthropic pricing, or Gemini pricing pages when you calculate costs; the rates and terms are subject to change.
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