A $200 ChatGPT subscription could cost OpenAI $14,000 if you actually used it to its full potential

Bottom line: The math behind AI subscriptions is starting to look uncomfortable. Flat monthly pricing helped fuel the rapid adoption of tools like ChatGPT and Claude, but new analysis suggests those fees may not come close to covering the actual cost of heavy use. As users push these systems harder and more demanding AI workflows take hold, the gap between revenue and compute costs is becoming difficult to ignore. SemiAnalysis has calculated how big that gap really is. After testing subscription tiers from both OpenAI and Anthropic – running long-horizon coding and agentic tasks until weekly limits were exhausted – the firm found that the cost of theoretical maximum usage of these plans if priced at standard API rates far exceeds what users actually pay. A $200 ChatGPT Pro 20x subscription could cost as much as $14,000 in API pricing if fully utilized. Anthropic's Claude Max 20x plan, also priced at $200 per month, has a comparable ceiling, with potential usage totaling roughly $8,000 in token costs. Those figures help explain why utilization rates matter so much to the AI companies offering them. According to SemiAnalysis, Anthropic breaks even on Claude Pro and Claude Max 5x at around 20% utilization. OpenAI's margin is thinner. It begins losing money on ChatGPT Plus and ChatGPT Pro 5x once usage climbs above 11.4%. – SemiAnalysis (@SemiAnalysis_) June 10, 2026 The economics get tighter at the high end. Anthropic reaches zero gross margin at roughly 10% utilization on its top-tier plans, while OpenAI crosses into negative territory at just 5.7%. It doesn't take extreme use for these subscriptions to turn unprofitable. Adjusting pricing or restricting access is not a straightforward fix. Subscription models have been central to user growth, and pulling back risks slowing momentum in a market where capabilities remain a key competitive differentiator. Part of the pressure comes from how AI is actually being used. Token consumption is rising quickly, especially with agentic systems that can require up to 1,000 times more tokens than a standard prompt. That kind of demand is already forcing large organizations to rethink how freely these tools should be deployed. Microsoft, Meta, and Amazon have reportedly pulled back from internal efforts that encouraged heavy usage after costs escalated. In one widely cited example, a company burned through $500 million in a single month using Anthropic's Claude, largely because it failed to put limits on employee access. That kind of overspending is pushing companies toward more controlled approaches. One strategy gaining traction is to shift workloads between models depending on the task. More complex queries go to expensive frontier models, while routine work is handled by cheaper alternatives. – Dong Ming (@dming) June 14, 2026 The savings can be substantial. A Wall Street Journal report found that routing tasks this way can cut costs by up to 95%. "You don't need a model that knows quantum gravity," Columbia University vice dean Vishal Misra told the publication. "These open-source models are very capable, and the ability to charge a big premium for AI is going to diminish." Some companies have already made the shift. Flo Crivello, founder and CEO of AI assistant startup Lindy, announced that the company moved 100% of its traffic to DeepSeek V4, switching entirely away from Anthropic's models. DeepSeek V4 proved comparable to Claude Sonnet at a fraction of the cost, and the move has "saved the company millions of dollars," Crivello said. Others are going further by building their own AI systems on top of open-source models trained on internal data. While that requires more upfront investment, it offers tighter cost control and reduces dependence on third-party providers. In some cases, these tailored systems may even outperform general-purpose frontier models for specific use cases. There is some expectation that costs will ease over time. As infrastructure expands and newer models replace older ones, the cost of running mid-tier systems should decline. SemiAnalysis suggests that models at the Opus 4.8 level could eventually be delivered profitably for around $20 per month. That does not apply to the most advanced systems, though. Frontier models, including those still in development, remain expensive to run. Their highest-end capabilities may increasingly be priced via APIs rather than bundled into consumer subscriptions. For now, AI providers are juggling two forces: users want powerful tools at low, predictable monthly prices, but the infrastructure to run them remains costly and highly sensitive to usage. OpenAI CEO Sam Altman has acknowledged the tension, noting that rising token costs are becoming a serious issue and that the company is working to help users "get more value for less spend" when using ChatGPT.