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Clear out junk files and repair common Windows errorsFree Scan →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →Repair Windows errors before they cause bigger problemsFix Now →There is no reliable universal price for an AI task in 2026. The cost depends on the model and service tier, how much input and output it uses, whether input is cached, which tools it calls, and how many attempts it takes to produce a successful result. For a useful estimate, measure a representative task, add every billed request and tool charge, then divide total spend by successful completions. For self-hosted AI, include infrastructure and operating costs—not just the GPU-hour rate.
What “cost per task” should mean
A request is not necessarily a completed task. One task may involve several model calls, tool use, intermediate agent steps, retries, and failed attempts. Define the task as one successfully completed unit of work—for example, one correctly classified support ticket—and define what counts as success before comparing prices.
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Keep three different figures separate:
- Metered API spend: what the provider bills for tokens, tools, and other metered services.
- Cost per successful task: total observed spend divided by the number of tasks that meet your success criterion.
- Full deployment cost: the broader cost of operating the system, including relevant hosting, integration, monitoring, storage, engineering, and operations.
An API invoice can help establish the first figure, but it does not by itself establish the other two.
How to calculate the cost of an API task
Use the rates that match the actual workload
For a token-metered request, calculate:
Request cost = input tokens × input rate + output tokens × output rate + applicable cached-token charges + tool and service charges.
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Use consistent units. If a rate is published per million tokens, divide it by 1,000,000 before multiplying by a token count. Apply cache rates only to tokens billed as cache writes or reads, and add separately billed services such as search or code execution.
Sum every call in the task
For a multi-step workflow, calculate each call and add the results. Include retries, failed attempts, and intermediate agent calls in observed spend; do not count only the final answer. An agent loop can use additional input, output, and reasoning tokens between the user’s request and the completed task. Google’s Gemini API pricing notes that agent inference is billed at standard rates, including intermediate tokens generated during agent loops.
Divide by successful completions
Run representative examples and record token counts by type, tool calls, retries, failures, and outcomes. Then use:
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Observed cost per successful task = total measured spend for the run ÷ number of tasks that met the success criterion.
Keep the failure and retry costs in the numerator. Excluding them can make an unreliable workflow look artificially cheap. This is a practical calculation based on metered pricing and lifecycle-cost considerations, not a universal formula published by an AI provider.
A dated API pricing example—and what it does not tell you
Google’s Gemini API pricing page lists Gemini 3.7 Flash at paid Standard rates of $0.75 per million input tokens and $3.75 per million output tokens through December 31, 2026. The page lists rates of $1.50 per million input tokens and $7.50 per million output tokens beginning January 1, 2027. These are model- and tier-specific figures, not general Gemini prices; use the live pricing page for the applicable region and service conditions when estimating a real workload.
At the listed paid Standard rates through December 31, 2026, a hypothetical call with 10,000 input tokens and 2,000 output tokens would have a token charge of $0.015 before any cache, tool, or other service charges: (10,000 × $0.75 ÷ 1,000,000) + (2,000 × $3.75 ÷ 1,000,000). That is the cost of the specified token usage for one call, not a typical task price or a guarantee of a successful result.
Google also lists different Batch, Flex, and Priority rates, and an additional charge applies to Google Search grounding after the listed free allowance. Check the selected option and the current rate card rather than applying the Standard example to other tiers. Prices can change: the listed Gemini 3.7 Flash rates change on January 1, 2027.
Why provider rate cards do not identify the cheapest option
Token rates are metering prices, not prices for an equivalent completed task. A valid comparison needs the same work, comparable model capability, the same success criterion, and the same assumptions about token volume, caching, tools, retries, geography, and service tier. The available provider rates do not establish an apples-to-apples task benchmark or a universal cheapest provider.
- OpenAI: its pricing page separates input, cached input, and output rates by model. It also says eligible regional-processing endpoints for models released on or after March 5, 2026 carry a 10% uplift. That uplift applies to the eligible endpoints described by OpenAI, not automatically to every model or request.
- Google Gemini API: pricing varies by model and service option; cache and grounding charges can also matter. The dated Gemini 3.7 Flash Standard example above should not be generalized to other models or tiers.
- Anthropic: its pricing page lists model-specific rates, prompt-cache writes and reads, a 50% batch-processing saving, and separate tool charges, including web search and code execution. Anthropic says web-search charges exclude the input and output tokens needed to process requests, so those token charges must be counted separately.
Anthropic’s model page estimates that Opus 5.5 costs 40% less to run than Opus 5 for typical token-billed workloads, and lists cache reads at $0.20 per million tokens. Treat the reduction as Anthropic’s estimate for the stated typical workloads, not as an independently verified saving for every task. Confirm the current model rates and applicable conditions before using either figure in a budget.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.What self-hosting changes in the calculation
With a self-hosted model, a GPU-hour quote is only one input to unit cost. Utilization affects how much valid work that capacity produces, and a full estimate may also need to account for capital costs and operating expenses. The LCOAI paper, “Introducing LCOAI: A Standardized Economic Metric for Evaluating AI Deployment Costs” (2025), proposes normalizing capital and operational costs by valid inference volume. It presents a proposed metric, not an adopted industry standard or a universal cost-per-task benchmark.
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A fair comparison checklist
Before choosing a model, provider, or deployment method, compare options against the same measured workload:
- Success: Does each option meet the same defined quality or completion criterion?
- Usage: What are the input, output, and context token counts for representative tasks?
- Caching: Which tokens are written to or read from cache, and what rates apply?
- Tools and steps: Are search, code execution, other tools, and intermediate agent calls included?
- Retries and failures: How much spend occurs before a successful completion?
- Service conditions: Which model, tier, region, latency, and availability assumptions apply?
- Deployment: For self-hosting, what utilization and amortized capital and operating costs apply per valid completion?
Record the date alongside each rate-card estimate. For each option, report both spend per attempt and spend per successful task so that cost and task quality remain visible together.
When an online calculator helps
Economize’s online LLM API Cost Calculator page says it compares 197 models from 10 providers and asks for monthly input and output token volumes. The page reports an update date of October 2, 2026. It can provide a first-pass estimate from the volumes entered, but it cannot establish how many tokens your task will actually use, whether its outputs will meet your success criterion, or what retries and tool calls will add. Validate calculator rates against provider pricing pages and replace assumed usage with measured traces.
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