Recommended Free Tools
Some links on this page are affiliate links: if you buy through them we may earn a commission, at no extra cost to you.
The oft-repeated $700,000-a-day figure is based on a real 2023 report, but it was never an audited OpenAI bill—and it is not a reliable measure of ChatGPT’s current daily cost. The estimate primarily described the infrastructure needed to serve ChatGPT users at the time. By 2026, OpenAI’s AI spending is reportedly far larger, involving inference, training, data-center capacity, chips, energy, personnel, and other expenses. The exact current daily cost of ChatGPT is not publicly disclosed.
Where the $700,000 figure came from
The number originated in an article published by Futurism on April 23, 2023. It reported an estimate attributed to SemiAnalysis analyst Dylan Patel and previously reported by The Information: operating ChatGPT could cost OpenAI up to $700,000 per day.
That wording matters. This was a reported analyst estimate, not a figure OpenAI announced in its financial statements. It was not an independently audited expense, a verified electricity bill, or OpenAI’s total daily cash burn. It primarily concerned the servers and related infrastructure required to respond to users’ requests.
Do these 3 things before closing this tab:
1Clear out junk files and repair common Windows errors2Fix the driver behind crashes, sound loss and screen glitches3Repair Windows errors before they cause bigger problems“Up to” also suggests an approximate upper estimate rather than a precise daily charge. The headline is useful as a snapshot of generative-AI economics in early 2023, but presenting it as though OpenAI still pays exactly $700,000 every day is misleading.
What does “running ChatGPT” actually include?
A ChatGPT request may appear simple to a user, but the underlying service can involve several layers of computing and operating expense.
- Inference: GPUs and other infrastructure process a prompt and generate the response.
- Routing: The system may choose among different models or services depending on the task, account, availability, and required performance.
- Networking and storage: Prompts, responses, uploaded files, images, audio, and model data must be transferred and stored.
- Additional tools: Browsing, code execution, image generation, voice, memory, reasoning, and other features can require additional computation.
- Reliability and safety: Monitoring, abuse prevention, moderation, security, red-teaming, and operational engineering are part of delivering the product.
- Capacity reserves: OpenAI must maintain enough capacity for busy periods, even when every available GPU is not processing a request.
Therefore, the cost of a response is not simply the electricity used while text appears on screen. It includes the cost of owning, renting, reserving, operating, and maintaining the capacity needed to provide a fast and dependable service.
Inference, training, and company operations are different costs
One of the biggest mistakes in discussions about ChatGPT’s finances is treating all AI spending as one category.
Quick wins for a faster PC:
Scan for outdated or missing drivers - takes under a minuteDriver Scan →Repair Windows errors before they cause bigger problemsFix Now →Fix the driver behind crashes, sound loss and screen glitchesFind Drivers →| Category | What it covers | Why it matters |
|---|---|---|
| Inference | Serving live user requests and generating outputs | Rises as users send more and longer requests |
| Training | Developing, pretraining, and updating models | Can require enormous bursts of compute and data-center capacity |
| Corporate operations | Research staff, engineers, sales, support, legal, compliance, security, offices, and administration | Determines OpenAI’s total operating expenses and profitability |
The original $700,000 estimate was mainly associated with serving ChatGPT, or inference. It did not establish OpenAI’s total cost of doing business. Training new models, hiring employees, developing safety systems, supporting enterprise customers, and paying other corporate expenses are separate matters.
Why answering a prompt can be expensive
When a user submits a prompt, the request is routed to computing hardware capable of processing the model. GPUs perform the mathematical operations needed to interpret the input and generate output tokens. The system then sends the response back to the user.
Costs generally increase when a request involves:
- A longer prompt or attached document
- A longer response
- A larger or more capable model
- Additional reasoning or “thinking” tokens
- Image, video, audio, or real-time voice processing
- File analysis or code execution
- Browsing and other external tools
- High concurrency or low-latency performance requirements
Some requests can be handled by smaller or more efficient models. Others may require expensive hardware for a longer period. The cost of a short text exchange therefore cannot be treated as representative of a long reasoning task, a voice conversation, or a multimodal workflow.
Why free users create a subsidy problem
A free user may generate no direct subscription revenue while still consuming computing capacity. That can create a meaningful subsidy burden, particularly when usage is frequent or resource-intensive.
However, it is not possible to calculate OpenAI’s exact cost per free user from public retail API prices. Internal accounting may reflect reserved capacity, discounts, model routing, utilization rates, cloud contracts, and infrastructure shared among several products. A free request also may not use the same model or resource profile as a paid API request.
The defensible conclusion is that free access can be expensive to provide—not that every free prompt costs the same amount or necessarily loses money individually.
API prices are not OpenAI’s cost per prompt
Public API pricing is a customer price, not a transparent disclosure of OpenAI’s underlying cost. The price may include gross margin, cloud-provider arrangements, hardware depreciation, support, research, safety work, and capacity costs. It may also be affected by discounts and committed-use contracts.
Conversely, OpenAI may incur costs for users who generate no direct API revenue, including free ChatGPT users and workloads covered by subscriptions or enterprise agreements.
The Tool Desk
Outbyte PC Repair FREERepair Windows errors before they cause bigger problemsFix Now →Outbyte Driver Updater FREEFix the driver behind crashes, sound loss and screen glitchesFind Drivers →For that reason, multiplying published API rates by estimated ChatGPT activity does not produce a reliable estimate of OpenAI’s cost. It also does not reveal the company’s profit on an individual request.
Why the 2023 number is outdated
ChatGPT in 2026 is not the same workload it was in April 2023. The service has expanded across more models and more demanding use cases, including reasoning, file analysis, images, voice, and agent-like tasks. A larger user base and more complex requests can increase total inference demand.
At the same time, efficiency has improved. Better chips, batching, caching, quantization, model routing, software optimization, and smaller specialized models can reduce the resources required for some requests. Microsoft said it achieved a 40% improvement in inference throughput for certain heavily used models through hardware and software optimization. That is a Microsoft-wide or product-specific disclosure, not a ChatGPT-only cost calculation, but it illustrates why user growth does not automatically translate into an identical increase in cost per request.
The result is a moving target: total demand may rise rapidly even as the cost of an individual operation falls.
What later financial reporting says
Later reports indicate that the financial scale of AI inference grew far beyond the 2023 estimate. The Information reported figures based on OpenAI financial documents indicating that inference computing was expected to reach $1.8 billion in 2025, alongside projected revenue of $3.7 billion.
Rank #3
The same reporting indicated that OpenAI expected a possible $14 billion loss in 2026 and as much as $9.5 billion in model-training compute costs during that year. These figures were reported projections or internal financial information, not independently audited public-company accounts. Projected expenses are not the same as realized expenses.
Separate reporting cited by CNBC and Reuters said OpenAI was targeting approximately $600 billion in cumulative compute spending through 2030. That report also said inference expenses increased fourfold in 2025 and that adjusted gross margin declined from 40% in 2024 to 33%. These figures should be understood as reported company or media estimates, not as a complete public ledger of ChatGPT’s costs.
Even the $1.8 billion inference figure should not be converted casually into a current daily bill. Dividing an annual projection by 365 would produce an average, not a disclosed daily expense. It would not show how costs vary with demand, capacity reservations, accounting methods, or product mix.
Does OpenAI lose money on every ChatGPT prompt?
There is no public evidence supporting the blanket claim that OpenAI loses money on every prompt.
The economics vary according to:
- The user’s plan and usage limits
- The model used
- Prompt and response length
- Reasoning and tool requirements
- Hardware utilization and cloud pricing
- Revenue-sharing or infrastructure agreements
- Whether the request comes from a free user, subscriber, enterprise customer, or API client
Some high-volume or demanding usage is likely subsidized, especially when the revenue attached to a user is fixed while resource consumption varies. But the public record does not support assigning a universal profit or loss to every individual prompt.
Is ChatGPT profitable?
That question requires a date and a definition. A product can have positive revenue or positive gross margin while the company behind it still reports a large overall loss. Inference costs are only one part of OpenAI’s financial picture, and company-wide losses cannot be assigned entirely to ChatGPT.
Reported projections indicate that OpenAI has faced very large expenses even as revenue expanded. Because OpenAI is private and does not publish ordinary public-company quarterly financial statements, estimates attributed to internal documents require careful qualification.
Free tools Windows power users keep installed
One-click scans. No signup required.
Microsoft’s filings separately record the effects of its investment in OpenAI, but those filings do not provide a complete OpenAI income statement or a ChatGPT-only profitability calculation. A Microsoft accounting entry should not be treated as a direct measure of OpenAI’s daily operating cost.
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.Why Microsoft matters
Microsoft is central to the story because the companies are connected through a major partnership and cloud relationship. Microsoft has invested heavily in AI infrastructure and provides computing capacity through its cloud business.
In its fiscal 2026 disclosures, Microsoft said operating expenses were affected by investments in compute capacity, AI talent, and data. It also described pressure on Microsoft Cloud gross margins from AI infrastructure investment and higher product usage. Its investor materials indicated approximately $190 billion in calendar-year 2026 capital expenditures, including substantial AI-related investment.
Microsoft’s spending provides useful context for the scale of the infrastructure race, but it cannot simply be added to OpenAI’s bill. The same data-center ecosystem may support ChatGPT, API customers, Microsoft products, training workloads, and other services. The public disclosures do not identify a ChatGPT-only allocation.
Microsoft’s SEC filing likewise describes broader company costs and cloud-margin effects, not a separate daily invoice for ChatGPT.
How AI companies are trying to reduce the cost
The business challenge is not simply to make a powerful model. It is to serve that model at a cost that customers and investors will support.
- More efficient hardware: New chips can improve the amount of work completed per unit of time or energy.
- Software optimization: Better kernels, scheduling, batching, and memory management can increase utilization.
- Model routing: Simple tasks can be sent to smaller models while complex work uses more capable systems.
- Caching: Repeated or reusable computations can reduce duplicated work.
- Quantization and compression: Some models can run with less memory and computing capacity.
- Tiered access: Providers can reserve the most expensive capabilities for higher-priced plans or usage levels.
- Usage limits: Caps can protect capacity and prevent a small number of users from creating disproportionate costs.
- Higher-value workloads: Enterprise, API, and specialized applications may generate more revenue than free consumer usage.
These measures do not eliminate costs. They change the relationship between capability, speed, usage, and revenue.
What the $700,000 claim does—and does not—tell you
It is useful for:
- Explaining why early free access to a popular AI service was financially unusual
- Showing that inference is a real infrastructure expense, not merely a software abstraction
- Introducing the economics of GPUs, capacity, and model serving
- Providing a historical baseline for the rapid growth of generative AI
It is misleading for:
- Describing OpenAI’s current 2026 daily cost
- Estimating OpenAI’s total daily operating loss
- Calculating a universal cost per prompt
- Describing ChatGPT’s electricity bill
- Proving that ChatGPT loses money on every user
- Proving that ChatGPT is inherently unprofitable
The financial takeaway
The original “$700,000 per day” claim was a plausible 2023 estimate of ChatGPT-serving infrastructure, but it was never an audited statement that OpenAI pays exactly that amount every day. It also did not include all of OpenAI’s costs.
As of 2026, the more important story is the scale and complexity of the AI infrastructure business. Reported figures point to billions of dollars in inference and training costs and much larger future compute commitments. At the same time, efficiency improvements, subscriptions, enterprise contracts, API revenue, partnerships, and higher-value workloads may help providers convert that spending into a sustainable business.
So the accurate answer is not “ChatGPT costs OpenAI $700,000 every day.” It is: the figure was a historical estimate, and the current cost of running ChatGPT is both substantially larger in scale and impossible to reduce responsibly to one publicly verified daily number.
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.
