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There is no established universal price for a unit of AI “intelligence.” A useful comparison separates three things: what a provider bills for, what it costs to complete a defined workload, and what the completed result is worth to the buyer. Those measures answer different questions.
What does an AI price actually measure?
A quoted price can measure access, usage, or an outcome. It does not automatically measure a model’s intelligence, the cost of a finished task, or the value that task creates.
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Access and usage
A subscription or seat fee buys access for a user or period; a usage charge rises with consumption. The billing unit might be tokens, calls, seats, or a fixed plan. These approaches allocate cost and risk differently, but they do not establish that the services being sold are equivalent.
Completed work
For a specific workflow, cost per successfully completed task can be more informative than a rate per token or call. It requires a clear definition of success and accounting for the full workflow, including repeated model calls, tools, and any human review or retries.
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Buyer value
The value of an answer depends on what it enables or avoids: staff time, operating expense, revenue, or risk. A value-based price can be discussed as a ceiling or negotiation framework, but it is not evidence that a buyer actually realized savings. Those effects need their own measurement.
Why tokens are not units of intelligence
OpenAI’s Help Center defines tokens as “the units that OpenAI models use to process text.” (OpenAI token explainer.) That makes a token a processing and billing unit—not a standardized measure of reasoning, quality, or business value.
Different models can tokenize the same text differently, and they may produce different amounts of output or use different numbers of calls to answer a prompt. Even where two offers quote a price per million tokens, the figures do not establish equal work or equal results. The model, task, context, and quality threshold matter.
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Why the headline rate is not the task price
Provider price lists can separate input tokens, cached input, cache writes, and output, with other charges for tools or services. Rates may also vary by model, context length, processing mode, and service conditions. OpenAI’s API pricing page and Google Cloud’s Vertex AI pricing page illustrate how many dimensions can sit behind a headline rate. These live pages change; check the applicable service and current terms when estimating a budget.
A low input-token rate alone does not prove that a service is cheaper for a task. If it takes more output, extra calls, additional tools, or more review to meet the same acceptance standard, the full cost may differ. The reverse is also possible: a higher rate may be economical if a model reliably avoids other costs, but that conclusion requires workload evidence.
Agentic systems make the distinction sharper. A workflow that plans, calls tools, checks results, and tries again can consume resources across multiple steps. McKinsey’s July 2026 interview with David Tepper, Pay-i’s CEO and cofounder, discusses cost drivers in agentic operating expenditure and presents cost per completed task as a useful measure. That is an attributed enterprise perspective, not a universally validated industry estimate.
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What historical price declines do—and do not—show
A 2025 article in Nature Machine Intelligence compared API prices of US$20 per million tokens for GPT-3.5 in December 2022 and US$0.075 per million tokens for Gemini-1.5-Flash in August 2024. The article describes the latter model as exceeding GPT-3.5 performance and reports a 266.7-fold reduction in price in that comparison. (Nature Machine Intelligence article.)
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How to compare AI offers fairly
Set the task and quality bar before comparing prices. Then measure what each service needs to meet that bar, rather than treating a billing unit as a proxy for the result.
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- Define one workload. Fix the task, input data, context, modality, and expected output. Specify what counts as acceptable and whether a person must review the result.
- Record the complete billable usage. Include input and output, cached tokens or cache writes when applicable, model calls, tools, and retries. Separate one-time setup from recurring usage if relevant.
- Measure successful completions. Count tasks that meet the acceptance standard, not just prompts sent or tokens processed. Include review and correction effort where it is part of the real workflow.
- Compare service conditions. Note relevant latency, throughput, availability, context limits, and processing region. A price comparison is incomplete if the offers do not meet the same operational need.
- Calculate cost predictability. Estimate how the bill changes as task length, complexity, volume, or retry frequency changes. A flat subscription and a per-use charge expose the buyer to different usage risks.
- Assess value separately. Estimate time or cost avoided, revenue effects, or risk changes, and state what supports the estimate. Do not treat a provider’s unit price or a successful technical benchmark as proof of realized business value.
A result should be reported with its workload and quality threshold attached. “Cheaper” is meaningful only when the services are compared on a sufficiently similar task and the costs counted are made explicit.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why AI is not costless just because access is cheap
AI services run on physical infrastructure. The OECD describes AI compute as a stack that includes specialized hardware and infrastructure, and identifies potential impacts from training and inference such as energy and water use, emissions, electronic waste, and resource extraction. (OECD: Compute and the environment.)
This context explains why compute is economically relevant even when a user sees only a software bill. It does not establish a universal hardware cost, electricity cost, or environmental impact per task; those depend on the system and workload.
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When is AI becoming a commodity?
Commoditization is a claim about substitutability, not merely a decline in one price. If buyers can switch providers and obtain sufficiently similar results, reliability, service conditions, and integration for a given workload, then access to that capability may behave more like a commodity for that use. But a shared token unit or falling price does not prove that condition.
The practical question is narrower: for this task, can another offer meet the same acceptance standard under the buyer’s operational constraints, at a lower total cost or with greater value? Answering that requires comparable workload data—not a universal “price of intelligence.”
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