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The cheapest AI model is not the one with the lowest posted token rate. It is the model that produces an acceptable result at the lowest cost per successful task under your actual workload. Compare candidates by running the same representative requests, measuring every billed input, output, cache, tool and modality unit, and dividing total spend by the number of runs that meet a declared success test.
A rate card is only an input to that calculation. Model quality, retries, context size, latency, regional availability, batch eligibility and tool charges can change the result.
Start with a task definition, not a model price
Write a one-page task specification before looking at prices. Include the input format, typical and maximum context size, required output format, acceptable response time, expected request volume and any tools or non-text media involved. State what a successful completion means in observable terms.
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A success criterion should be strict enough to reflect production use. For a document-extraction task, every required field might need to be correct and valid JSON. For support replies, you might require factual accuracy, policy compliance and a human rating of at least four out of five. For code generation, the criterion could include passing a test suite and meeting security checks.
#1 Best Overall
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Do not count the most attractive output from a run while ignoring failures. A model that is inexpensive per attempt but needs frequent retries can cost more per successful result than a higher-priced model with a better first-pass rate.
Record constraints that can disqualify a candidate
- Required input and output modalities, such as text, images, audio or video.
- Context-window size and any price threshold for unusually long prompts.
- Supported region, data-residency, privacy and retention requirements.
- Tool or function-calling support and the tools your workflow must use.
- Maximum acceptable latency, throughput and availability.
- Integration requirements, model version stability and contractual terms.
Only compare models that satisfy these constraints. A model with a lower rate is not an option if it cannot process the required context or operate in your permitted region.
Build a representative, repeatable test
Use identical cases and instructions
Create a test set that mirrors the real distribution of work: common cases, large inputs, difficult edge cases and known failure modes. Keep prompts, attached files, tool definitions and output schemas identical across candidates. Version the test set so a later price or model change can be compared with the same workload.
Repeat enough cases to expose variation. Include deterministic-looking tasks as well as cases where outputs can differ legitimately. If a human evaluates quality, give evaluators a written rubric and hide the model identity where practical. Automated checks should be recorded with their version and thresholds.
Separate quality measurement from price measurement
For each run, store the model and version, prompt identifier, success or failure, quality score, latency, retries and any human correction. A cost comparison without outcomes cannot answer which model is cheaper for the task; it only compares attempts.
No neutral, cross-provider statistic establishes a universal cost per successful completion for an unspecified task. Official provider pages publish their own rates, not a same-task benchmark. Present your own measured result with its sample size and assumptions rather than declaring a provider universally cheapest.
Capture every billed unit
Export usage from the API response or billing records for every attempt. At minimum, log:
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- Cached-input tokens, including cache writes or storage where the provider bills them separately.
- Output tokens.
- Tool calls, retrieved content and tool-generated input.
- Image, audio and video units or seconds when applicable.
- Batch, flex, priority or other processing tier.
- Region, currency, taxes and any account-specific discount that is actually contractually available.
Keep failed calls and retries in the log. A timeout, invalid response or safety refusal still may consume billable tokens. Record fixed charges, storage and metered services separately so they are not accidentally hidden inside a token estimate.
Rank #2
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Calculate the cost of one attempt
Use the exact unit in the provider’s current rate card, commonly dollars per million tokens. The basic estimate is:
estimated cost per run = (uncached input tokens × input rate + cached input tokens × cached-input rate + output tokens × output rate) ÷ provider token unit + tool, modality, storage and other applicable charges
If a rate is quoted per million tokens, divide token counts by 1,000,000 before multiplying. Keep input and output rates separate; generated output can be a small fraction of the prompt for extraction tasks but dominate cost for long-form generation.
Illustrative arithmetic
Suppose a run uses 100,000 uncached input tokens and 2,000 output tokens. At a rate of $0.25 per million input tokens and $1.50 per million output tokens, the token portion is (100,000 ÷ 1,000,000 × $0.25) + (2,000 ÷ 1,000,000 × $1.50) = $0.028. This is arithmetic based on the stated rates, not a benchmark or a promise of quality.
For another illustration, 120,000 input tokens and 3,000 output tokens at $2.50 per million input and $15.00 per million output cost $0.345 before tools or other charges. Whether those rates apply depends on the named model, service tier and processing mode.
Convert attempt cost into cost per successful task
After the test, add the spend for every attempt and divide by the number of successful completions:
cost per successful task = total measured spend across all attempts ÷ successful completions
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For example, an illustrative test with 80 attempts, 68 successes and $10.88 in total measured spend has a cost per successful completion of $10.88 ÷ 68 = $0.16. The figure is meaningful only alongside the success rubric, test mix, model version, date and included charges.
Rank #3
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Report the success rate as well: successful completions ÷ attempts. A simple expected-cost approximation for a stable workflow is attempt cost divided by first-pass success probability, but the measured total-spend method is safer when retries, variable token counts or different failure types are present.
Understand provider pricing dimensions
Model and service tier
Prices are model-specific. Standard, batch, flex and priority processing can have different rates or latency guarantees. Do not apply a rate from one model or tier to another. A free allowance may reduce an initial invoice but is not a permanent commercial rate; include the post-allowance price in a production forecast.
Caching
Repeated system instructions, reference documents or tool definitions may qualify for prompt caching. Measure cache eligibility, write and read prices, cache lifetime and hit rate. A cache that misses in real traffic will not deliver the savings shown by a best-case estimate. Include any minimum token size or storage charge in the calculation.
Tools and retrieval
Search, code execution, retrieval and external tools can add a per-call fee and can also generate additional input tokens. Count both. Anthropic’s current pricing documentation, accessed in 2026, states that its Batch API gives a 50% discount on both input and output tokens; web search is listed at $10 per 1,000 searches plus standard token charges for search-generated content. Apply those figures only to the particular Claude model, cache behavior and tier you actually use.
Images, audio and video
Multimodal services may meter pixels, seconds, frames or modality-specific tokens rather than ordinary text tokens. Use the provider’s modality table and log the units returned by the API. Do not substitute a text-token estimate for an audio or video workload.
Long context
Some models change prices when prompts exceed a stated size. The GPT-5.4 model page, for example, documents a long-context price condition for prompts over 272K input tokens. Check whether the threshold applies to the whole request or a particular token category before projecting costs for large documents.
Published price examples (checked October 13, 2026)
The following figures illustrate how rate cards differ. They are provider-published rates, not evidence that one model delivers better or cheaper results for your task.
| Provider and example | Published figure | Qualification |
|---|---|---|
| OpenAI GPT-5.4 | Batch input $2.50 per million tokens; cached input $0.25 per million; output $15.00 per million | Model-page figures for the stated batch and cache categories; do not generalize to other models or tiers. The pricing documentation says Responses, Chat Completions, Realtime, Batch and Assistants APIs are not priced separately from the selected model’s rates. |
| Google Gemini 3.1 Flash-Lite Standard | $0.25 per million text, image or video input tokens; $1.50 per million output tokens | Standard-service figures on the Gemini API pricing page. Its Batch listing shows $0.125 input and $0.75 output per million tokens. |
| Anthropic Claude Batch API | 50% discount on input and output tokens | Anthropic’s current pricing documentation; the underlying model rates and cache behavior still apply. |
| Google Search grounding | $14 per 1,000 requests after the stated shared monthly allowance | Allowance, eligibility and model details are page-specific. Add standard token charges for the request and returned content. |
Rates and model catalogs change. Date-stamp every example and recheck the official pricing page immediately before committing to a forecast. Public list prices may not include negotiated discounts, taxes or account-specific terms.
Rank #4
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Compare candidates on more than token price
| Axis | What to measure or verify |
|---|---|
| Cost per successful task | All billed usage across attempts divided by successes under a declared criterion. |
| Output quality | Identical cases, rubric and evaluation method; identify human versus automated scoring. |
| Input/output profile | Actual token counts and output-length distribution, not a single average request. |
| Caching | Eligibility, write/read price, hit rate and storage charges. |
| Tools and modalities | Tool-call fees, retrieved-content tokens and image, audio or video units. |
| Latency and throughput | End-to-end time, queue effects and asynchronous completion behavior. |
| Context and limits | Context ceiling, long-input price thresholds, supported modalities and availability. |
| Deployment constraints | Region, residency, privacy terms, service levels and integration effort. |
| Volume and billing mode | Expected traffic, minimums, free-tier boundaries, batch or flex eligibility and effective date. |
Show the quality and latency results beside cost. A cheaper model that fails the rubric is not a viable lower-cost choice; a more expensive model may be economical if it avoids manual correction or repeated attempts.
Check whether batch processing changes the answer
Batch processing can lower token rates when the task tolerates asynchronous completion. Re-run the same workload under the batch rate, include queue time and failure handling, and verify maximum batch size, result-retention period and cancellation rules. Do not use a batch discount for an interactive workflow that requires immediate responses.
For a mixed workload, model each class separately: interactive requests at the real-time rate, overnight documents at the batch rate and urgent traffic at its priority rate. Combining them into one average can conceal a costly segment.
Project monthly spend without false precision
- Start with the measured cost per successful task for each workload class.
- Multiply by the expected number of successful tasks in the month.
- Add the expected retry rate, cache hit rate, tool usage and modality mix.
- Model peak traffic separately from average traffic if a higher tier or reserved capacity is required.
- Add fixed API, storage, infrastructure, monitoring and human-review costs that were actually measured.
- Label the result as an estimate, state the pricing effective date and provide a range for uncertain variables.
Keep billing and infrastructure outside the token calculation until you have measurements for them. Taxes, negotiated discounts, currency conversion and account terms can move the invoice away from public list pricing.
Use sensitivity analysis
Recalculate the forecast with low, expected and high values for input length, output length, success rate, retries and cache hits. This shows which assumption matters most. If a small change in output length dominates the forecast, constrain output format or length before switching models. If failure rate dominates, invest in prompt or workflow improvements and evaluate quality again.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.A practical comparison worksheet
For every candidate, fill one row per run with the following fields:
- Task case and prompt version.
- Provider, model version, region and processing tier.
- Uncached input, cached input, output and modality units.
- Tool calls, search or retrieval requests and tool-generated tokens.
- Start time, end time, queue time and retry number.
- Success result, rubric score and human correction minutes.
- Applied rate-card line, calculated charge and any fixed charge.
Aggregate those rows into total spend, attempts, successes, success rate, median and percentile latency, quality distribution and cost per successful task. Preserve the raw usage export so a later rate change can be recalculated without repeating the entire test.
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Choosing by input-token price alone
Fix it by including output, cache, tool, modality and retry costs. A low input rate does not guarantee a low task cost.
Best Value
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Comparing different workloads
Fix it by sending identical cases, prompts and files to every candidate. If a model needs a different prompt to work, record that change and include its token impact rather than hiding it.
Counting only successful calls
Fix it by charging every attempt and dividing by successful completions. Keep failures visible in the report.
Using a promotional or free-tier rate for all traffic
Fix it by modeling the allowance boundary and the rate that applies afterward. State the date and eligibility conditions.
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Ignoring human correction
Fix it by measuring review and rework time. A model that costs less in tokens but requires more correction may have a higher total task cost.
Declaring a universal winner
Fix it by naming the workload, model version, region, tier, date, quality threshold and sample size. The cheapest model for one task can be the wrong choice for another.
When self-hosting belongs in the comparison
Hosted API prices are not the whole cost of an on-premises or self-hosted deployment. Include accelerator purchase or rental, capacity planning, power, cooling, storage, engineering, maintenance, upgrades, observability and idle capacity. Compare those costs over a declared period and divide by productive successful inferences, not theoretical maximum throughput.
A 2025 arXiv preprint proposes a “Levelized Cost of Artificial Intelligence” framework that normalizes capital and operating costs by productive inference volume and discusses API and self-hosted scenarios. It can provide a way to organize a total-cost analysis, but its abstract does not establish a settled industry standard. Treat it as a framework to examine, not as a universal accounting rule.
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Select the candidate with the lowest measured cost per successful task among models that meet your quality, latency, region, privacy and integration requirements. If two options are close, choose using the constraint that matters most to the business: predictable latency, data residency, output quality, operational simplicity or volume discounts.
Publish the comparison as a dated, reproducible record: task definition, success rubric, test-set composition, number of attempts, measured usage, applied rate-card lines, total spend, successes, cost per success and known exclusions. Re-run it whenever the model version, workload mix, pricing tier or success requirement changes.
Quick Recap
Product prices and availability are accurate as of the date/time indicated and are subject to change. Any price and availability information displayed on Amazon at the time of purchase will apply.
