OpenAI vs Anthropic: Token pricing war erupts as AI costs test enterprise budgets

'Pretty Crazy' Token Usage Is Testing Bosses' Bet on AI

At the software company 8x8, employees are using Anthropic's Claude to draft emails, analyze customer feedback, and write code, but so far, their growing reliance on the artificial intelligence chatbot hasn't troubled the finance team. While other Silicon Valley companies, such as Meta, Uber, and Salesforce, have publicly expressed concerns about the growing cost of generative AI tools and have begun introducing usage caps in some cases, 8x8 says it finds itself in the black. Over the past 18 months, the company estimates it has saved about $5 million in annual costs by canceling subscriptions to dozens of software and educational tools it deemed unnecessary in part because Claude could provide similar capabilities. So far, 8x8's annualized bill for Claude is "well below" that figure, says Joel Neeb, the company's chief transformation and business operations officer. Neeb expects the savings and costs to eventually even out as 8x8 encourages more employees to adopt AI and it incorporates the tech into more complicated work. But for now, there's still a huge gap, which "makes my chief financial officer happy," he tells WIRED. He declined to share exact total spending on generative AI. As companies pour hundreds of millions of dollars collectively into AI tools for coding, marketing, and customer service, a new obsession has emerged in the tech industry: "tokenomics," or how to manage the soaring cost of AI usage. (Tokens represent the amount of content an AI model analyzes and generates.) Last month, Royal Bank of Canada's CEO disclosed that its token usage surged 500 percent over the past six months. At Cisco, a third of employees are using an internal AI chatbot on a daily basis, so "the token usage is getting pretty, pretty crazy," CEO Chuck Robbins said on an earnings call. Some top engineers at analytics software developer Amplitude are "spending thousands of dollars a month or more on tokens," according to its CEO Spenser Skates. Aaron Levine, the CEO of Box, said, "The token budgeting conversation has absolutely taken over as one of the most important" and "heated" topics. Roughly 300 companies addressed questions or concerns about AI tokens during their earnings calls or in public discussions with financial analysts in April or May, according to a WIRED review of transcripts from the data provider AlphaStreet. That's a small fraction of the thousands of calls held during the span, but just 93 companies mentioned "token" in April and May a year ago. Executives at several companies said they are developing or looking to buy systems to help monitor token usage and choose the lowest-priced model for a given prompt. Others said they were still trying to figure out balancing hiring more people and increasing their budgets for tokens to achieve their goals. Software has rarely come cheap, but the latest generation of AI tools is causing unusual stress in C-suites for a variety of reasons. Prices keep fluctuating. New models that are more powerful -- and more expensive -- than the last get released every month. And getting entire organizations on board with new ways of working has been a challenge, so AI-fueled productivity gains on one team can lead to bottlenecks for another. 20 Percent That said, some companies are still encouraging employees to use AI more without worrying about the tab. In April, Long Island, New York-based clothing brand Baseball Lifestyle 101, which expects to generate $250 million in sales this year, told about 50 of its top managers to spend the equivalent of about 20 percent of their salary on AI tokens every month. Bill Rom, cofounder and chief strategy officer of Baseball Lifestyle 101, tells WIRED the cost is likely to exceed $100,000 a month by the end of the year, but it's already paying off. Claude recently helped land a $1 million order by identifying that a retailer was running low on some sizes of the company's popular ice-cream-patterned shorts. "That's a day and a half of work that can now happen in an hour or two that might make me eight figures of additional revenue over 12 months," Rom says. The AI chatbot also helps write financial reports and plan photoshoots, allowing the company to hire fewer junior staffers and to direct investments elsewhere. Rom says it's important to "inspire people how to use AI" before setting financial ground rules on the technology. At 8x8, which develops a communications platform to help manage sales and customer service, all of the 1,800 or so full-time employees are encouraged to regularly check a dashboard showing how much they and their colleagues are using Claude in the spirit of not leaving anyone behind. "It's not punitive in the least; it's really just so that we all stay tightly packed in this journey," Neeb says. In May, the product and customer success teams were among the heaviest users, and the sales and finance teams were among the lightest. Neeb says it's possible that 8x8 will institute caps on how much staff can use Claude. He discussed the idea for the first time recently with the CFO due to growing internal usage of the Claude Opus 4.8 model, which was released last month and costs nearly 1.7 times more than an offering Anthropic released in February. Though no decisions have been made, access to Opus going forward might require proving that older models can't get the job done, Neeb says. "Can we downgrade the model a little bit and still get the same outcome?" Otherwise, Neeb says 8x8 isn't backing off generative AI in any way. Measures of customer satisfaction and loyalty have been trending higher, and revenue has grown for four consecutive quarters as AI-generated analyses accelerate the work of its sales staff. Attributing the trends to solely AI, or even AI at all, is difficult, but Neeb suspects a connection. "It really is the rising tide that floats all boats when you do this right," he says. "Go Faster" About two years ago, 8x8 began wading into generative AI by giving all of its employees training and support for OpenAI's ChatGPT and Google's Gemini. Later, a select few were offered Claude, Neeb says, and it became the companywide standard over the past year. Management monitors usage and has warned employees that refusing to gain AI fluency will lead to consequences. "If you're not using AI in some capacity for your role, then you're missing the opportunity to go faster and get better answers more effectively than your peers," Neeb says. Other tech employers have issued similar directives to mixed results. At companies like Amazon and Meta, workers have reported incorporating AI into their work just because they feel they have to or slacking off because AI tools free up their time, both leading to what critics have described as waste. Neeb contends that patience and accountability measures are necessary to orient employees. He doesn't want them taking longer lunches or sitting on the beach as AI speeds up their work. Neeb also has wanted more out of what he describes as "laggards in this journey" such as the sales and finance teams, which together account for 28 percent of employees at the company but just 15 percent of token consumption. He's hoping a recent AI hackathon for the finance team spurs it to automate its extensive manual processes, such as collecting money from customers and generating quarterly accounting. The operations leader says he has witnessed firsthand how Claude can make his company more efficient. Neeb uses the tool to automate a daily email to the company that summarizes top AI usage tips from industry influencers on YouTube. After noticing that the task used up "a lot of tokens," Neeb says he asked Claude whether it could operate more affordably. Claude reworked the automation, cutting token usage by 80 percent.

AI costs spike as subscriptions hit pricing wall -- firms turn towards Chinese LLMs, open-source models to extend budget

The cost of serving AI via a subscription model has steadily increased for AI firms, especially as the decrease in cost per token has not kept pace with the spike in token usage. According to SemiAnalysis, the subscriptions that both Anthropic and OpenAI offer are much cheaper than the actual cost you have to pay if you maximize their usage. The research firm purchased every subscription from the two AI providers and discovered that the approximate maximum possible spend (assuming API pricing) is far larger than what users pay every month. For example, Claude Max 20x costs $200 a month, but maximizing it would cost $8,000 a month in token spend, while ChatGPT Pro 20x, which is also $200 monthly, has a maximum possible spend of around $14,000. Anthropic breaks even on its two lower plans (Claude Pro and Claude Max 5x) at 20% utilization, while OpenAI starts losing money if utilization on its base plans (ChatGPT Plus and ChatGPT Pro 5x) exceed 11.4%. Things are much worse for the two companies' top-end offerings, with Anthropic hitting 0% gross margin if utilization reaches 10%, while OpenAI is in the red if usage exceeds 5.7%. This is certainly unsustainable, but cutting features or raising subscription prices is likely off the table for these companies as well. It's not all bad news, though -- as new models arrive and more data centers go online, the cost of serving existing models is bound to decrease, with SemiAnalysis predicting that serving Opus 4.8-level models at $20 a month could become profitable soon. On the other hand, frontier models, like Mythos, will still be much more expensive to run, so it's likely that the latest, most advanced features could be reserved for API access only, meaning you'll need to pay for it on a per-token basis. Expensive frontier models have firms looking elsewhere As SemiAnalysis showed, subscription tiers are more affordable than API access. However, you'd still need the latter if you want to access the full capabilities of these AI models, and this is where budgets start breaking. Powerful agentic AI uses up to a thousand times more tokens than the average model, and big firms like Microsoft, Meta, and Amazon are backing off "tokenmaxxing" as costs spiral out of control. One unnamed company even blew through $500 million in one month after failing to impose a usage limit on its employee licenses. Because of this, some firms have started using tools that switch these expensive frontier models for cheaper, more affordable ones, including Chinese open-source models like DeepSeek. A Wall Street Journal report says costs could be reduced by up to 95% by allowing agents to switch between AI models as needed. "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." Flo Crivello, the founder of Lindy, a startup providing AI executive assistant services, also told WSJ that the company has moved towards DeepSeek V4, as it proved to be as capable as Sonnet while costing ten times less. Although it still reserves Anthropic's models for advanced work like coding, Crivello said that using the cheaper model has "saved the company millions of dollars." Other firms have begun building their own AI using open-source models, which are tailored to their specific needs and trained on in-house data. While this might seem complicated and expensive at first, it could save the company in the long run, as it would not have to rely on third-party providers for its AI needs. Some even claim it could outperform frontier models, as they're built for the firm's specific needs and applications. The availability of cheaper models and AI agents that optimize operational costs by using the more expensive options only as needed is putting pressure on OpenAI and Anthropic to lower their prices. OpenAI CEO Sam Altman has talked about the issue of ballooning AI token costs and said the company is looking for ways to help users "get more value for less spend" when using ChatGPT. Follow Tom's Hardware on Google News, or add us as a preferred source, to get our latest news, analysis, & reviews in your feeds.

OpenAI mulls slashing prices as it competes with Anthropic for users: WSJ

OpenAI is mulling sharp price cuts to its artificial intelligence offerings, as it looks to woo consumers away from rival Anthropic, the Wall Street Journal reported Wednesday evening stateside, citing sources familiar with the matter. "The company is weighing significant cuts to what it charges for tokens, the unit of measurement artificial-intelligence firms use to bill for their products," the report said, adding that it was "in anticipation of similar cuts the company expects at Anthropic," according to sources. The ChatGPT producer, which did not immediately respond to CNBC's requests for comment, currently charges consumers in tiered subscriptions of $8, $20 and $100 and above each month for access to its flagship GPT-5.5 models. Anthropic conversely charges users $17 each month with an annual subscription to Claude Pro, and $100 and above monthly for a subscription to Claude Max. The report on possible price cuts come as competition has been ramping up between the two companies. OpenAI on Monday confidentially filed for an initial public offering with the U.S. Securities and Exchange Commission, close on the heels of an IPO filing from Anthropic. Anthropic closed its Series H funding round on May 28 at a $965 billion valuation, slightly edging out OpenAI, which was valued at $852 billion in March. ChatGPT became the first app to reach 1 billion monthly app users in May -- roughly three years after its November 2022 launch -- surpassing the previous record set by Google Maps, which took around five years after launch to reach the same milestone, according to estimates from market intelligence firm Sensor Tower. Read the full WSJ report here.

How Much Does It (Really) Cost to Use Claude Fable, GPT-5.5, and Gemini 3.5 Flash?

There have been a lot of anxious murmurings lately about the price of AI going way up. Developers are spending more money training and running AI systems as competition thickens and the electrical grid gets squeezed. Customers, meanwhile, are shelling out more in order to get access to the latest models. Earlier this week, Anthropic -- moving towards what's expected to be a historic IPO -- released Fable 5, a dialed-down version of the secretive and supposedly extremely powerful Mythos. Fable 5 costs twice as much as its predecessor, Opus 4.8, even though some users have complained that the former's touchy safety guardrails render it effectively unusable in some contexts. Presumably with its ear to those anxieties, OpenAI is now weighing significant reductions to the price they charge for tokens (the basic unit for measuring AI usage), the Wall Street Journal reported on Thursday. To anyone who isn't deeply immersed in the finer intricacies of AI finance, this can all be a bit baffling. It would be extremely convenient if there were some simple method for converting one million "input tokens" to a particular task, for example, but unfortunately, that's not the case. Each task brings its own computational demands, which for pay-as-you-go models means users will have to pay different amounts depending on how they use AI. Subscription tiers offer a bit more simplicity, but these plans come with their own terms and prices, which vary between companies and models. To clarify things, here's what you need to know about the pricing models for three of the AI industry's most powerful models: Fable 5 First up, Anthropic's latest release, the fabled Fable 5. Subscribers to Claude Max, Pro, Team, and seat-based enterprise plans can use Fable 5 with their plan's existing token allowances until June 23. Beginning on that date, the company plans to revert to a pay-as-you-go model for all Fable 5 users, meaning the more intensively they use the model, the more customers will have to pay -- regardless of whatever subscription tier they might be subscribed to. Anthropic intends to reinstate the usual subscription-based token allowances for Fable "when sufficient capacity allows us to do so," according to a blog post published earlier this week. It's not yet clear what will happen to paid Claude subscribers who haven't exhausted their entire token allowance before the June 23 cut-off date; we've reached out to the company for answers and will update this story as soon as we know more. The key thing to remember here is that Fable 5 consumes more tokens than Anthropic's earlier models. So if you're currently paying $100/month for the Max 5x plan, you'll continue paying that same amount using Fable, but there's a good chance you'll hit your token limit faster. Starting on the 23rd, all users will need to pay $10 per million input tokens and $50 per million output tokens when using Fable. According to one common arithmetical shortcut, one token translates to roughly four written words; it therefore takes a hell of a lot of written prompts to get to a million tokens, meaning you can get plenty of value out of $10 if you're only using Fable to, say, write work emails or generate dinner recipes. Then again, if that's all you need AI for, you may as well use a free chatbot: Using Fable to respond to simple text chats is like driving a McLaren W1 to drive to your next-door neighbor's house. Fable 5 specializes in long-running autonomous tasks, like writing software code, which requires many, many more tokens -- we're talking in the hundreds of thousands to millions for both inputs and outputs. Your monthly bill will therefore be significantly higher than it would be if you were just feeding simple text prompts to the model. But if you're already paying, say, $200/month for the Max 20x plan, you may not be paying all that much more for usage credits than you are already: Using 10 million input tokens and 5 million output tokens would lead to a bill of $350 (($10 x 10) + ($50 x 5)). The price of using Fable 5, in other words, will depend entirely on the demands of the tasks you're using the model for -- that's, of course, the basis of the pay-as-you-go model. If you tend to hand models complex tasks that require many steps and long stretches of time, proceed with caution. GPT-5.5 Pro Released in April, GPT-5.5 Pro is the latest model powering ChatGPT. It's available via OpenAI's Pro plan (which costs $200/month), and also on the company's Business ($30/user/month) and Enterprise (custom pricing) tiers. Developers using GPT-5.5 through OpenAI's API, on the other hand, are charged by a pay-as-you-go model like the one that will start applying to Fable later this month. At $5 per million input tokens and $30 per million output tokens, it's significantly cheaper than Fable (and just a little bit more expensive than Anthropic's second-most valuable publicly available model, Opus 4.8) It also comes with 50% cheaper batch tokenization option, which essentially allows OpenAI's servers to handle bundles of similar requests in single "batches," boosting computational efficiency but also resulting in slower response times. Gemini 3.5 Flash Google has highlighted what it has said is a unique blend of speed and agentic capabilities with the most powerful version of Gemini, 3.5 Flash, which dropped last month. It's available for free with usage limits, and developers can build off the API for $1.50 per million input tokens and $9 per million output tokens -- by far the most affordable option of the three models we've looked at thus far. The bottom line Just as there's no standardized, industry-wide pricing model for AI, there's also a huge amount of variation in the benefits and drawbacks of each model. For many users who just need a chatbot to serve as a glorified search engine, the free versions of Claude, ChatGPT, or Gemini are probably fine. Anyone whose work demands a more advanced model for, say, coding or research purposes is probably better suited to pay for a subscription. Just pay attention to the fine print before you make a choice, and be on the lookout for key phrases like "usage limits" and "pay as you go."

OpenAI could cut ChatGPT prices to win you over from Claude

A potential price war could test customer loyalty as switching between AI providers remains relatively easy. The AI industry's battle for customers could soon be good news for users' wallets. OpenAI is reportedly preparing steep cuts to its token costs to claw back enterprise momentum lost to rival Anthropic. OpenAI is considering reducing the cost of tokens, the units used to measure and bill for AI usage, before a possible similar action by its fast-rising competitor, The Wall Street Journal reported, citing people familiar with the matter. The potential change comes as businesses increasingly push back against the soaring costs of deploying AI. Token costs can add up quickly for companies that use AI at scale. This is particularly true for enterprise customers running coding assistants, agents, and other productivity tools that use huge amounts of computing resources. OpenAI's pricing discussions also show how much pressure Anthropic has been able to put on the market. In recent months, the younger company has gained momentum, thanks to the popularity of its offering, Claude Code, among software developers. That success has apparently helped boost Anthropic's revenue and even helped the startup eclipse OpenAI's valuation at one point. Since then, OpenAI has intensified its coding ambitions, placing greater internal emphasis on Codex. But cheaper AI won't automatically mean healthier profits. Both Anthropic and OpenAI are already spending billions on infrastructure to train models and serve users. More aggressive price cuts could squeeze margins even further. Some corporate customers are also beginning to question if their AI spend is delivering enough bang for their buck. Those concerns have ignited wider conversations in Silicon Valley about "tokenmaxxing" -- the notion of consuming as many AI tokens as possible to boost productivity, even when the financial return isn't always obvious. A pricing war could give some clues about how sticky these AI platforms truly are, and investors are closely watching. OpenAI and Anthropic are the big revenue winners in this AI boom, but they have a shared problem: Customers are more likely to churn than in many legacy software businesses.

'The math doesn't work': Why your $200 AI subscription is secretly worth thousands

New analysis by research firm SemiAnalysis, suggests the math for power users doesn't match the subscription If you pay for ChatGPT or Claude and you genuinely lean on it by spending your day coding, running agents or even churning through long research sessions, then you're getting one of the best deals in tech right now. And yet, you're also the reason the deal won't last. At least that's the uncomfortable takeaway from a new analysis by research firm SemiAnalysis, which bought every paid tier from OpenAI and Anthropic and deliberately ran them into the ground. The team pushed each plan with long, agent-style coding tasks until it hit the weekly usage cap, then worked out what all those tokens would have cost at standard pay-as-you-go API rates. The gap between what you pay and what you get turned out to be enormous. What the numbers actually say The long-standing rule of thumb was that a $200-a-month plan tops out at around $2,000 of usage. SemiAnalysis found the real ceiling is far higher. In fact, in the most extreme case, roughly 70 times the sticker price. Here's how the tiers stack up, based on the firm's testing. The figures are the approximate API-equivalent value if you push a plan to its absolute limit (see chart above). In other words, a maxed-out $200 ChatGPT Pro plan could represent around $14,000 of compute a month if you bought the same volume through the API -- a roughly 70x markup in your favor. Claude's top tier lands near $8,000, about 40x. Even the entry-level $20 plans can return many times their price in raw usage. One important caveat to keep in mind: these are API-equivalent figures, not what the companies actually spend. It's essentially the retail price you'd pay to buy that many tokens through the API, which is marked up well above the providers' true compute cost. So OpenAI isn't literally losing $14,000 on you, of course, but the directional point, that heavy subscribers are heavily subsidized, holds up. Why this is happening Every request you send to an AI model is measured in tokens, which are small chunks of text, sometimes a whole word, sometimes part of one. The more a model reads and writes, the more computing power it burns. For instance, a quick question and a short answer might be a few hundred tokens. But the way people increasingly use these tools is far more demanding. When you hand an AI an agent-style job -- plan the steps, search files, run a tool, check the output, fix mistakes, try again -- it can chew through enormous amounts of text behind the scenes. And, that hidden work is what drives costs up, and the same one SemiAnalysis researchers used to push the plans to their limits. The result is that the subscription you pay for is fixed at $20 or $200, but the cost of serving you is not. For a power user, that can balloon way past what you're actually paying. So why do the AI companies allow it? Because most subscribers don't come anywhere near those ceilings. The plans only tip into money-losing territory past a surprisingly low usage threshold, and the typical user stays well under it. According to SemiAnalysis, OpenAI starts losing money on its cheaper plans once a user crosses roughly 11.4% of the allowance, and on its priciest tier, the margin vanishes at around 5.7%. Anthropic appears better cushioned, breaking even closer to 20% on lower tiers and about 10% at the top. The blunt translation of these figures is you don't have to obsessively max out a plan to become unprofitable for the company serving you. A genuinely heavy user can get there without really trying. For now, the labs seem willing to eat that cost. Cheap, generous subscriptions hook users, build daily habits and grab market share while the AI race is still wide open. But it's a subsidy, not a sustainable price, and subsidies have a way of ending. The first crack is already here (almost) You don't have to look far for evidence that the squeeze is starting. Anthropic's newest high-end model, Claude Fable 5, was reportedly bundled into its Pro, Max, Team and Enterprise plans only through June 22, 2026. After that date, access was set to move to metered usage credits unless extra capacity lets it return to the flat-rate plans. However, Anthropic suspended that model days after launch, so we don't truly know if that would have happened. Yet, the economics behind the scenes tell an even more fragile story. Fable 5 carried an API list price of about $10 per million input tokens and $50 per million output tokens, roughly double the rates of Anthropic's already-capable Opus 4.8. Giving away that level of heavy-duty reasoning inside a standard, all-you-can-eat subscription is a massive financial drain. It highlights the ultimate irony of the "slop" crisis: while the research proves AI needs pristine, expensive data and deeper reasoning loops to avoid collapsing into nonsense, the eye-watering cost of running those premium models means the average internet user won't get to use them for free. Even before the U.S. government abruptly pulled the model over national security and export concerns, Anthropic was already preparing to yank Fable 5 from basic subscription tiers. The high-quality web is getting pricier to build, and even costlier to sustain. What this means for your subscription Don't panic-cancel anything. If you're a heavy user, the rational move right now is to enjoy the discount while it's here. But it's worth bracing for a few shifts over the next year or two: * Smarter routing behind the scenes. Expect more systems that quietly send easy requests to cheaper, smaller models and reserve the expensive frontier models for genuinely hard tasks. Done well, you may not notice. * Usage-based pricing for the newest features. The latest, most powerful models cost the most to run. Rather than bundling them into a flat fee, companies may increasingly meter them by consumption, closer to how API billing already works, and exactly what the Fable 5 change looks like. * Tiering of the best stuff. Cutting-edge capabilities could land behind higher-priced or pay-per-use options, while everyday chat stays cheap. Final thoughts If your AI subscription feels almost too good for the price, that's because, at least for heavy users, it really is. This research highlights the numbers on a subsidy the industry has mostly kept quiet about, and Anthropic's looming Fable 5 change shows the correction has already begun. The era of unlimited frontier AI for a flat $20 or $200 isn't over yet, but the economics are pulling hard in the other direction. My advice is lock in the value while it lasts. Follow Tom's Guide on Google News and add us as a preferred source to get our up-to-date news, analysis, and reviews in your feeds. Subscribe to Tom's Guide on YouTube and follow us on TikTok. Finally, you can visit our dedicated Tom's Guide Savings Squad hub for expert help on getting the best products for less.

People are starting to think ChatGPT is too cheap -- and that might be a problem for OpenAI

Technology companies usually spend a lot of time trying to persuade customers that a subscription is worth the money. ChatGPT has stumbled into a very different problem. A growing number of users are looking at the price and wondering whether they're somehow getting away with something. The question has become harder to dismiss as ChatGPT has evolved. The $20-a-month ChatGPT Plus and $200-a-month ChatGPT Pro subscription prices haven't changed since OpenAI announced them. Yet ChatGPT is much more powerful, with many more features, even if it still has plenty of built-in wrinkles and limits. Nonetheless, questioning its value has become more common as the chatbot has expanded from being an impressive novelty into something people use every day. That combination of expanding capabilities and stable pricing has led many users to ask whether ChatGPT costs less than it should. It's been an issue from the beginning, with OpenAI CEO Sam Altman complaining that ChatGPT Pro loses money for the company due to its popularity a year and a half ago: It isn't unusual for a product to cost more to provide than customers realize. What's unusual is when customers start noticing the gap themselves. AI might be especially vulnerable to that dynamic as AI systems require enormous computing resources. Every response is powered by vast networks of specialized hardware operating in data centers that consume significant amounts of electricity. Those costs add up quickly, particularly when millions of people use the service every day. Some estimates indicate that power users could theoretically consume thousands of dollars' worth of compute resources a month, while paying only a fraction of that amount in subscription fees. Meanwhile, AI companies continue investing enormous sums in data centers, hardware, and electricity. Short-term splurge Part of the reason the debate has gained traction is that AI is not cheap to run. Every response generated by ChatGPT relies on huge amounts of computing power, specialized hardware, and data center infrastructure. Those systems consume enormous quantities of electricity, and the bills only grow larger as usage increases. AI models generate ongoing expenses every time someone submits a prompt. Millions of users asking questions each day creates a very different economic equation than most subscription services have to manage. Heavy ChatGPT users could therefore eat up far more computing resources than they are paying for at market price. At the same time, AI companies continue pouring billions of dollars into new data centers, cutting-edge hardware, and future model development. That reality has led some users to believe today's prices are less about profitability and more about securing market share while the AI industry is still taking shape. "All investment and business strategies are still operating on the "old rules" which have yet to be replaced because AI has yet to completely up end the global order. They all know we're hurtling towards a cliff, but the off ramp isn't visible yet, and they all assume it will magically appear before they run out of road," one Reddit user speculated. "The only logical way to "win the game" then is to keep speeding along so that you're the first one onto the off ramp. And nobody wants to be left behind so..." Underpriced AI ChatGPT occupies a rare position in the technology industry, seeming like a good bargain amid a growing chorus of complaints that technology and related services are actually getting worse every year. Many ChatGPT users genuinely feel they are getting more value from the service today than they did a year ago, despite paying the same monthly fee. And some think the question of underpricing ignores the bigger picture of how AI models are produced. "People calculate their usage using public API prices and assume Anthropic or OpenAI lost that amount on them. But API pricing is not the company's actual internal cost. It already includes profit margin, and we have no idea what their real cost is after caching, batching and infrastructure optimizations," another Reddit user pointed out. "I also believe the released models themselves are profitable. The companies still report losses because they are spending billions on training the next models, buying hardware and expanding infrastructure. So while GPT-5.4 is generating profit, they may be spending all of that money and more on GPT-5.5. For most people, the argument that ChatGPT is underpriced is actually pretty simple. They are not studying OpenAI's balance sheet or calculating data center costs. They are looking at their own habits and realizing they use the chatbot far more often than they ever expected. That helps explain why the conversation keeps coming up. People complain all the time when a product gets more expensive. They almost never complain that something feels too cheap. Whether ChatGPT is actually underpriced is a question OpenAI will have to answer eventually. For now, many users seem to have reached their own conclusion. They are paying the same price they paid months or even years ago, but they feel like they are getting a lot more in return. Follow TechRadar on Google News and add us as a preferred source to get our expert news, reviews, and opinion in your feeds.

OpenAI reportedly considering price cuts to push back against competitors

OpenAI is considering massive product-wide price cuts to address industry concerns and keep up with the rising popularity of competitor AI companies, according to the Wall Street Journal. Speaking to anonymous insiders, the publication reported that the Sam Altman-led company is debating reducing subscription usage costs to retain its customer base -- competitors like Anthropic are reportedly debating the same move. According to those close to the company, this may include lowering costs for highly sought after tokens, a response to an ebbing trend among tech companies known as "tokenmaxxing" or the burning out of processing tokens (and entire corporate budgets) in order to boost the productivity of their AI products. According to the Wall Street Journal, business executives across the industry have criticized AI costs. Altman recently said that high prices are a "a huge issue" for the company, but the decision hasn't been finalized just yet, insiders told the Journal. Meanwhile, investors are also beginning to cool on AI, amid ebbing stock market numbers from major players like Nvidia. Still, OpenAI is making moves to become a more profit-driven company. On Monday, OpenAI officially announced it was filing for an IPO, with a yet-undetermined timeline. Industry rumors suggest OpenAI could go public as early as September and could be valued at $1 trillion. Anthropic also recently filed for public status, suggesting the AI price wars are only just getting started. Disclosure: Ziff Davis, Mashable's parent company, in April 2025 filed a lawsuit against OpenAI, alleging it infringed Ziff Davis copyrights in training and operating its AI systems.

OpenAI Is Taking the "Crack Cocaine" Approach to Pricing

Can't-miss innovations from the bleeding edge of science and tech OpenAI burned through a staggering amount of money in 2025. According to audited financial figures obtained by AI skeptic Ed Zitron, who shared them with The Financial Times, the net loss attributed to the ChatGPT maker soared from $5 billion in 2024 to a stunning $39 billion in 2025. You can relitigate the numbers all day -- a source familiar with situation told the FT that a lot of those 2025 losses are a "non-cash accounting charge linked to the company's previous structure rather than its underlying operations -- but the financial pressure does seem to be taking a toll. After years of giving users largely unfettered access to its models for a monthly fee, OpenAI and many of its competitors are now debating whether to boost prices dramatically by transitioning to a token-based billing system, charging users more directly for the amount of computing power they consume instead of an open-ended monthly subscription. OpenAI currently offers both pay-as-you-go API access and monthly ChatGPT subscriptions. But how long the latter will stick around is looking increasingly uncertain. Earlier this month, OpenAI CEO Sam Altman argued that "we see a future where intelligence is a utility, like electricity or water, and people buy it from us on a meter." In other words, OpenAI's behavior sounds an awful lot like a drug dealer who floods the market with addictive drugs, then jacks up the prices once users are dependent on them. "They've kind of taken the crack approach to AI," one Reddit user argued. "Give it to people for free, get them hooked, then jack up prices." It's an insightful metaphor, considering where the majority of the AI industry's biggest players appear to be headed. And as the costs of building out data centers and maintaining access to cloud compute come due, it's likely we'll see even more similar behavior. The real costs behind AI subscriptions are staggering. According to a recent report by research company SemiAnalaysis, a $200 ChatGPT Pro subscription costs OpenAI as much as $14,000 if used to its maximum potential. Spiking API prices have caught some power users off guard. According to Axios, an unnamed firm's CFO accidentally racked up half a billion dollars in Claude usage fees in a single month. The financial reality is hard to ignore, with CEOs starting to reverse course on AI adoption as prices for access to the tools spiral out of control. "Fundamentally all of these AI providers are massively subsidizing token usage on these flat rate plans," another Reddit user wrote. "It's simply unsustainable." Even with soaring token-based API pricing, AI companies' balance sheets are firmly in the red. And they could stay there, especially considering the burgeoning AI price war that could force them to lower -- not raise -- prices just to stay competitive. More on OpenAI: OpenAI Execs Are Panicking

OpenAI Wants a Price War With Anthropic -- Is It Proving DeepSeek Right?

Open-source inference providers are already serving DeepSeek V4 at a fraction of closed-model pricing, giving corporate customers a viable exit before any price war even begins. OpenAI is considering slashing the prices it charges developers and enterprises, per the Wall Street Journal, in anticipation of similar cuts from Anthropic. Discussions are described as still in flux as both companies filed confidentially for IPOs this month, and neither has turned a profit. "I think we'll have a lot of ways we can help people get more value for less spend," Sam Altman said at a recent event, according to the Wall Street Journal. That quote landed against a backdrop of OpenAI posting a -122% adjusted operating margin in Q1 2026 -- meaning it lost $1.22 for every dollar it brought in. The pressure is real. As Decrypt previously reported, ChatGPT's share of global generative AI web traffic fell from 77.6% in May 2025 to 53.7% by April 2026. For the first time, more companies tracked by the Ramp AI Index are paying for Anthropic than for OpenAI. Anthropic's annualized run rate went from $9 billion at the end of 2025 to $47 billion by May 2026 -- a 422% jump in five months -- driven almost entirely by Claude Code, with Q2 2026 being the company's first profitable quarter ever. OpenAI has since made its own coding tool, Codex, a company priority. But it's playing catch up. Both companies are fighting a not so silent war to attract as many clients as possible in the middle of the world's biggest tech fever since the dot-com era. Companies of every sort are now racing to use AI in some way or another. Uber's CTO burned through its entire 2026 AI budget by April, some JP Morgan employees are spending more on AI use than their own salary, per the bank's chief data officer for its payments division. This is the practice Silicon Valley has taken to calling "tokenmaxxing" -- burning through as many AI tokens -- the bits of data processed by AI models -- as possible, often without clear return on investment. Palantir CEO Alex Karp compared it to a porn addiction at AIPCon last week. JP Morgan analysts published a note this month titled "AI Bills Are Out of Control." The companies most exposed to the blowback are the ones now contemplating a price war. Tommy Shaughnessy of Delphi Ventures laid out the structural trap in a widely shared X post this week: The $20/month flat fee rate was always priced below what heavy usage actually costs -- a loss-leader designed to drive adoption, not cover compute. Once a real business needs AI at scale, it moves to the API, paying per token, but consuming much more compute power. Not everyone agrees with this take. Some believe the oligopoly of AI in the Western hemisphere allows for companies to charge increasingly high prices for processing their prompts -- Chinese models charging so little being proof of this. If this is the case, there may be room for drastic price changes while still being on solid financial ground. Real enterprise deployments are moving to metered API pricing, and companies are burning credits far faster than flat fees ever suggested. Meanwhile, open-source inference providers (companies that provide compute power so AI models can process information) are scaling fast, with agentic tools being the catalyst for their growth. These platforms serve China's leading AI models like DeepSeek, GLM, MiMo, Kimi or Minimax, which compete with Claude Opus on coding benchmarks, at a roughly one-thirteenth the price of the closed alternative. "Chinese labs open source frontier-grade models," Shaughnessy wrote. "The model is the single biggest cost an inference provider has, and they get it for free." As long as that holds, the floor on intelligence pricing keeps falling toward zero -- and any margin recovery at OpenAI or Anthropic becomes a math problem with no clean solution. The whole thesis breaks only if China goes closed-source, Shaughnessy noted, which would be bullish for the U.S. labs. So far, most of China's AI labs appear committed to the opposite approach.

Anthropic's Fable 5 worth the price? OpenAI may soon become cheaper

Anthropic's launch of a version of its artificial intelligence model that it previously claimed was too powerful to release has garnered excitement as well as criticism over the cost. Called Fable 5, which some are calling "Mythos Lite", it is included in the Claude subscription but only until June 22. Users will then have to pay full price, which some online have said is too expensive. "A few prompts just to test it out and it ate 5% of my monthly allowance... I have no reason to use this without a trust fund," one user on Reddit wrote. Released on June 9, 2026, Fable 5 gives general users access to what Anthropic calls "Mythos-level" capability but with a built-in safety trigger that shuts down the model and reverts to the older Claude Opus 4.8 if they ask about high-risk topics such as cybersecurity, biology, chemistry or distillation. In April, Anthropic announced its Mythos model, which it said was too powerful for public launch because it is too effective at finding high-severity vulnerabilities, or potential weaknesses, in major operating systems and web browsers. How much does Fable cost? Anthropic says Fable costs $10 or approximately €9 per million input tokens and $50 per million output tokens. Put into context, it is double the price of the company's previously most expensive model, Claude Opus 4.8. Tokens are units of data that are processed by AI. A token can be a word, a punctuation mark, blank space, pixels in an image or even a blank space. It is estimated that in English, one token is around four characters or 75% of a word. So 1,000 tokens is considered to make up 750 words. Fable requires more compute and tokens as it can exercise more complex tasks, the company says. Fable can run multiple AI agents and work autonomously for days. "Fable 5 has the highest score of any model, with substantial gains in document-based reasoning, chart and table interpretation, and problem solving," Anthropic said. It can also extract precise numbers from detailed scientific figures and perform complex vision-based tasks such as rebuilding a web app's source code from screenshots. Still, whether that justifies the price is another matter as companies become more aware of the costs of AI. In comparison to rival OpenAI's model GPT-5.5, Fable is more expensive than the standard GPT-5.5, which is $5 per million input tokens and $30 per million output tokens. But Fable is cheaper than OpenAI's pro version of the GPT-5.5 model. The AI model cost is heating up between companies, with OpenAI reportedly considering cutting prices for paid access to its AI models, the Wall Street Journal reported on Wednesday, citing sources familiar with the matter. The companies are also competing on valuation, with OpenAI this week confidently filing for an initial public offering with the US Securities and Exchange Commission. It comes shortly after Anthropic made the same move last week.

CEOs Now Being Forced to Reverse Course, Cut AI Spending

Can't-miss innovations from the bleeding edge of science and tech The era of AI maximalism is grinding to a halt. It was only months ago that CEOs were forcing employees to use AI as much as possible for tasks like coding. But at some point during their AI binge, the big wigs stopped to check their tab, and are now having second thoughts. Their employees are hooked on AI coding tools, but the costs of using them are spiraling out of control. How businesses go about reconciling these costs with their AI evangelism is "going to be an absolute nightmare," an unnamed big tech executive told The Economist. It's an ironic reversal of fortunes for companies in the tech sphere, which have become one of the main adopters of AI. Bosses have been happy to slash their workforces and replace them with AI coding agents that can churn out mountains of code, encouraging their surviving employees to make use of AI help as much as possible. Some, like Amazon, instituted a leaderboard ranking employees by the number of AI tokens they used, as if they were competing in a video game. Meta also even factored AI usage into performance reviews. This wasn't an entirely top-down phenomenon. AI bros have embraced this maximalism in tongue-in-cheek fashion, giving it the aptly meme-y named ethos of "tokenmaxxing." All of this has backfired in predictable fashion. At one business, a single employee spent over $150,000 a month on AI tokens. An Nvidia executive admitted that he was spending more on AI costs for his research team than what he pays the actual employees. One unfortunate company reportedly blew through $500 million in a month on Claude usage fees. On average, new research from the Ramp AI Index found that the most "AI-pilled" businesses are spending around $7,500 per employee every month on AI. Now the noise coming out of the industry is cautionary rather than exuberant. AI isn't the problem, of course, but how you use it. Experts have advised imposing token limits on employees, being more selective about where AI is deployed, and using cheaper models. Signaling the vibe shift, Amazon and Meta have ditched those AI leaderboards, and a top Uber executive said AI wasn't yielding clear productivity gains compared to their expensive costs; soon after those comments, Uber imposed a $1,500 monthly token cap per employee. That AI customers are already reeling from AI costs and tapering off usage doesn't bode well for the model makers. Token costs right now could be the cheapest they'll ever be, as they're effectively subsidized by the companies providing the models to get customers on board. But can AI companies afford to keep their prices low, when their road to profitability remains elusive? It's a question that OpenAI might be speeding headlong into answering. While many raise their rates and switch over to usage based billing, the Sam Altman-led firm is reportedly considering slashing its rates to launch a price war with arch rival Anthropic, in anticipation of it doing the same. The gamble could secure it more customers in the short term and assure the business world that their models will still be worth the investment, but all eyes will be on both companies to see if cheap AI access is sustainable in the long run, or just a ditch measure before they start charging more again.

Inside the unpredictable AI costs hitting corporate budgets

For example, Uber recently capped monthly employee spending at US$1500 per coding tool after the ride-sharing giant chewed through its entire 2026 AI coding budget in just the first four months of the year, due to a soaring use of agentic coding software. As Australian organisations set their AI budgets for the new financial year, they are recognising this challenge and shifting to measuring success based on AI output and impact, rather than simply consumption, says Stu Scotis, Deloitte Australia chief technology officer and Deloitte Global Agentic AI leader. Sometimes tokenmaxxing is quite intentional, Scotis explains, especially when organisations use leaderboards or gamified usage. Other times it can be more subtle, as users start to use background agents to perform unnecessary tasks or provide complex prompting and context that result in high token use. "The business impact goes well beyond the cost of tokens, as poorly managed AI spend can erode value and distract teams on false measures of AI adoption," he says. "Well-managed usage gives organisations visibility of the AI value chain which is where, what and how much AI is executing across the organisation and the costs. This transparency enables forecasting and stronger decisions about where AI is delivering growth, efficiency, and measurable returns, or intervene if needed." The impact of optimising AI usage for appearance over actual business value is amplified by the nature of AI billing models. The more data processed by an AI query, the more AI tokens consumed and the greater the cost. This cost pressure is also reshaping the broader IT budget conversation. AI spend is variable and unbudgeted in ways that legacy SaaS licensing never was, leading CFOs to scrutinise the entire technology stack and accelerate rationalisation of overlapping point solutions. Moving beyond the compute problem When business AI costs rise, the instinct is to treat it purely as an unavoidable compute problem - blaming GPU time, model licensing and inference costs. While these compute pressures are real for CFOs, treating excessive AI token consumption as only as a compute problem misses the point, says Elastic ANZ country manager Jeremy Pell. Rather than relying on complex AI queries that draw on vast amounts of business data, the key to curbing token consumption is to present Large Language Models (LLM) with refined "hyper-contextualised data", Pell says. "The compute explosion is happening further upstream at the retrieval layer," he explains. "If an enterprise AI system relies on low-quality, redundant or poorly scoped data retrieval, it forces the LLM to consume exponentially more compute power and tokens to reach a usable answer. "At this point you aren't just paying to compute, you are paying to compute junk data." Building a superior data foundation Addressing spiralling AI token costs requires harmonised and integrated enterprise data, as well as a robust semantic layer that retrieves data for LLMs efficiently and effectively. This ensures AI consumes the optimal number of tokens to only process the right contextual data. "Australian organisations don't need a massive flood of model tokens for every query; they just need the exact, right drop of hyper-contextualised data," Pell says. "That's why true AI visibility is an observability problem before it's a budget problem." Distilling this hyper-contextualised data requires a single source of truth, which is especially challenging when unstructured data comprises about 90 per cent of enterprise data and grows three times faster than structured data. Monitoring all data sources, including cloud, on-premises and endpoints, is critical for performance management and cost control. By consolidating onto a single platform where search, retrieval, observability and security run on a single data store, enterprises gain the transparency required to ensure that AI tokens are not wasted. "Every organisation obviously needs to manage costs, but you can't let this hold you back from being innovative," Pell says. "You've still got to be out there trialling AI and using it across your business to unlock value. "It's not simply about addressing tokenmaxxing to regulate AI use, it's about building a vastly superior data and retrieval foundation underneath AI to make it highly efficient and ensure your AI spend leads to business outcomes." For more information, visit www.elastic.co

OpenAI Execs Are Panicking

Can't-miss innovations from the bleeding edge of science and tech An AI price war is brewing. Corporations are reeling after finding that the cost to access powerful AI tools is soaring -- despite showing no clear payoff. In one particularly unfortunate incident, according to Axios, the CFO of a company accidentally racked up half a billion dollars in Claude usage fees in a single month. Put simply, the horrible economics of AI are finally starting to rear their ugly head. Astronomical capital expenditures by AI companies are starting to trickle down to users -- and they're not liking what they're seeing. Meanwhile, as the Wall Street Journal reports, executives at OpenAI are pondering whether to kick off a price war with the company's biggest competitor, Anthropic. By dramatically lowering prices, the company's reportedly hoping to steal users, while also anticipating similar price cuts by its competitor. Put simply, pricing is turning into a major headache for AI leaders. "That went from, at the beginning of this year, an issue that never came up -- people were totally happy with the amount they were spending -- to all of a sudden, a huge issue," OpenAI CEO Altman admitted during an event last week. "I think we'll have a lot of ways we can help people get more value for less spend," he added. It's a major conundrum for all players involved. AI companies have been bleeding tens of billions of dollars as costs for data center construction projects mount. Cutting prices now could make the situation even more dire, deteriorating already disastrous profit margins. Anthropic and OpenAI have been caught in a heated race, with the former making major gains through its enterprise-focused coding tools as of late. Its recent advancements clearly rattled the latter, considering the latest news. Both companies have confidentially filed for an IPO within the last ten days, raising enormous stakes. However, the fact that both are scaring away new users thanks to soaring prices isn't exactly a vote of confidence to investors, which could soon force AI executives to rethink their business models. More on AI prices: Corporations Reeling From Huge AI Costs With No Clear Benefits

OpenAI May Slash Token Prices as AI Costs Become a 'Huge Issue'

OpenAI might be considering significantly reducing the prices it charges customers for using its AI models, according to a report. The ChatGPT maker is said to be evaluating a strategy where it may slash its token pricing, which is the usage-based metric commonly used by AI companies to bill customers. The move is reportedly driven by the company's intensifying rivalry with Anthropic, which has also been rumoured to lower its prices in the future. OpenAI's Token Price Reduction Citing sources familiar with the matter, the Wall Street Journal reported that OpenAI is discussing substantial reductions in token prices. By doing so, the San Francisco-based AI reportedly aims to strengthen its position in the rapidly growing enterprise AI market. Tokens, notably, are the units used by AI companies to measure and bill usage of their models. The report speculates that lowering the token prices could potentially make OpenAI's AI offerings more attractive to businesses that rely heavily on such tools for purposes like coding, customer support, research, and productivity tasks. The ChatGPT maker is reportedly considering the move in anticipation of similar pricing adjustments from Anthropic, which is deemed to be one of its biggest competitors. OpenAI CEO Sam Altman has already acknowledged concerns surrounding AI costs during the 'Intelligence at Work' event. The executive described costs as "a huge issue", suggesting that the company was exploring ways to help customers get more value for what they pay, while also reducing expenditure. The report also highlighted the growing concerns among businesses regarding AI-related spending. WSJ reported that several companies have begun reassessing budgets allocated to agentic AI systems. Earlier this year, an Uber executive reportedly stated that the company had already exhausted its 2026 budget for agentic AI usage, while another executive questioned whether AI-assisted coding improvements were translating into tangible customer-facing benefits. The development has surfaced amidst growing speculation towards OpenAI's potential public listing. The ChatGPT maker reportedly confidentially filed paperwork for an initial public offering (IPO) earlier this week. Altman is also said to have sent an internal Slack message to employees, informing them of the company's intentions to go public within the next year. OpenAI, however, has yet to publicly comment on the reported pricing discussions.

Tokenomics 2.0: The battle against AI costs

Staggering token spending is forcing Indian firms to explore ways to rein in AI cost. This is where open-source models, small language models & inference layer startups step in, writes Swathi Moorthy. 400 billion tokens. That was a monthly expense of $78,000 which worked out to $1 million a year. A large enterprise in a regulated industry ran up this bill. "This was an eye-wateringly high number for the firm," said Karan Kirpalani, chief product officer, Neysa Networks. It is not that the company, which was one of Neysa's key clients, used the most advanced model. The strict internal usage protocol didn't bring costs down. Latency issues persisted despite adopting models launched by frontier labs. The villain here is unoptimised AI workload. Talk to any CTO, they will tell you that tokenmaxxing is one of the biggest challenges they are facing today. While MakeMyTrip's Sanjay Mohan tells ET the monthly consumption is in millions Mohit Saxena of InMobi Group says it is in billions. The staggering levels of token spend is forcing Indian companies to explore ways to rein in AI costs. The immediate response is to opt for open-source models and small language models. An additional option is to turn to startups such as Pipeshift and Divyam.ai. Through a range of solutions for inference optimisation, GPU orchestration and model routing, they help improve usage and save cost. Sandeep Kohli, cofounder, Divyam.ai said that this is coming at the back of huge adoption of AI as large firms are investing significantly in building AI-native solutions. As intelligence is spread across multiple systems in enterprises inefficiencies can creep in. Not every aspect of the business requires the latest models, which are token hungry, and thus results in increased cost. "When you start paying on a per token basis to some of the frontier labs and AI hyperscalers, tokenomics start to become eyewatering so quickly that it is unbelievable. The question then is, when you are at scale, does every request or prompt need to be directed to cutting-edge frontier models, or can you have a team of models in your AI ecosystem, where you determine which request goes through which model," explained Neysa's Kirpalani. "(As much as) 80% of tasks can be solved by models that consume less tokens. There is a demand for rightsizing of model (usage) for the right task," Kohli said. The enterprise demand for optimisation has spawned the inference market or the business of efficient deployment of models. Kirpalani said that there is no definitive estimate of India's inference market. The global inference market could be worth about $125 billion in 2025 by conservative estimates, he noted. The sophistication premium Cost is shooting up as model sophistication increases, prompting enterprises to actively pursue inference optimisation. Token costs for latest models such as Claude Opus 4.8 and ChatGPT 5.5 have increased significantly. For instance, Opus 4.8 costs $25 per million output tokens, compared to $5 for Claude Haiku 3.5. In the case of GPT5.5, a million output tokens cost $30, up from $15 for GPT5.4. Pro versions of both the models cost $180. GPT3.5 costs $1.5. For comparison, DeepSeek's latest model costs $0.28-$0.87. But not every query/prompt needs GPT5.5 pro or Opus 4.8, and can be routed through DeepSeek or Claude Haiku or cheaper open source models. Divyam.ai's Kohli said that they have built a team of models for each of the enterprise's AI agents, which will route the queries to the right model. These are a mixture of frontier and open source models. "We keep updating the models along with the rate cards so that enterprises can get the latest model without them having to adapt everytime," Kohli said. Kohli said that their customers are able to see 50-70% savings in generative AI cost using model routing and inferencing. The firm is currently onboarding two US clients. Pipeshift CEO Arko C said that they have orchestrated their stack to reduce latency, which is critical in multiple Indian businesses. The company has partnered with the GPU-service provider Neysa, where the former will deploy open source models to address rising cost and latency. Nurix AI, which has deployed Pipeshift's tech stack, said that they are able to see 3x reduction on tokens, in a statement. Beyond model selection, MakeMyTrip's Mohan said, "There are use cases that need low latency, which do not require reasoning models. We are now focusing on small language models that run on CPUs." However, these are not without challenges. Sifting through AI washing Enterprises' immediate challenge is to find the right solution amid hype and AI washing surrounding the market. Kirpalani says clients are struggling to differentiate the signal from noise. "That has a direct correlation whether you were able to take your use case into production and see ROI and value capture successfully. So, unfortunately, there's some amount of disingenuity on that front as well," he said. "Beyond digital natives, there is a sort of push that we need to do in terms of explaining to people why Anthropic, OpenAI and Gemini are not the answer to everything, and demonstrate that some use cases can work well on open source," said Pipeshift's Arko. Kohli said one of the challenges they are facing is while the US counterparts are moving fast, the similar wave and urgency is missing in India, with firms still doing pilots. Divyam.ai is now shifting its focus on the US market, where there is a rising demand.

Prepare for the Shift from Flat-Rate to Metered AI Billing

AI pricing is on the brink of a significant shift, with affordability giving way to sustainability as companies face mounting financial pressures. AI Grid highlights how current pricing models, such as OpenAI's $20/month ChatGPT subscription, are heavily subsidized and unsustainable given the operational costs and investor demands. For instance, OpenAI is projected to incur $14 billion in losses by 2026, underscoring the financial strain of maintaining low rates. As venture capital funding pivots toward profitability, users can expect higher costs as companies adjust to cover expenses like infrastructure, hardware and energy. Prepare to navigate a changing AI landscape as this feature explores the implications of usage-based pricing models, the rising costs of running AI systems and the emergence of tiered pricing structures. Gain insight into how businesses and individuals can adapt by managing AI consumption strategically and prioritizing high-value applications. Additionally, discover the broader economic and policy impacts of these changes, from legislative challenges to the evolving role of AI in global workflows. Why Current AI Pricing Models Are Unsustainable If you're using popular AI tools like OpenAI's ChatGPT or GitHub Copilot, you're benefiting from pricing strategies designed to attract users rather than reflect the true costs of operation. For instance: * ChatGPT's $20/month subscription and $200/month Pro plan are priced far below the actual operational expenses required to sustain the service. * Companies like OpenAI and Anthropic are incurring substantial losses to maintain these low rates, with OpenAI projected to lose $14 billion by 2026. These pricing models are heavily subsidized by venture capital funding, which has been instrumental in driving user adoption. However, this approach is not sustainable in the long term. As venture capitalists shift their focus from growth to profitability, companies are being forced to reevaluate their pricing strategies. The result will likely be higher costs for users as businesses aim to cover operational expenses and meet investor expectations. Financial Pressures Are Reshaping the Industry The financial landscape of the AI industry is undergoing a significant transformation. Major players like OpenAI and Anthropic are preparing for initial public offerings (IPOs), which brings heightened scrutiny from investors. These investors are increasingly prioritizing profitability over user growth, pressuring companies to generate faster returns. This shift is expected to lead to higher prices for AI services as companies strive to meet these demands. The operational costs of running AI systems are another critical factor. Maintaining the infrastructure required for advanced AI models involves significant investments in: * Electricity and water for cooling massive data centers. * Specialized hardware, such as GPUs and TPUs, to support the computational demands of AI models. These expenses are substantial and continue to rise as AI systems grow more complex. Companies are likely to pass these costs onto users, further driving up the price of AI services. Expand your understanding of AI pricing with additional resources from our extensive library of articles. Usage-Based Pricing: The Emerging Standard The industry is moving away from flat-rate subscription plans and adopting usage-based pricing models. Under this approach, your costs will depend on how much you use AI services. For example: * GitHub Copilot has implemented AI credits to track and charge based on usage. * Google has reduced usage limits on its AI tools, encouraging users to pay for additional capacity when they exceed the free tier. While usage-based pricing aligns costs with consumption, it introduces new challenges for users. Those who rely heavily on AI for complex or large-scale tasks may face significantly higher expenses. This model also requires users to carefully monitor and manage their AI usage to avoid unexpected costs. The High Cost of Running AI Operating advanced AI systems is an inherently resource-intensive process. The infrastructure required to support these systems often costs more than the human labor involved in their development and maintenance. Key challenges include: * Resource constraints, such as electricity and water shortages, which are critical for the operation of data centers. * Legislative proposals, like the AI Data Center Moratorium Act, that could restrict the expansion of AI infrastructure and increase operational costs. As these challenges intensify, companies will likely pass the rising costs onto users. This trend underscores the need for businesses and individuals to plan their AI usage carefully and budget for higher expenses. Lessons from Other Industries The evolution of AI pricing mirrors the trajectory of other tech-driven industries, such as ride-sharing. In the early stages, companies like Uber and Lyft offered heavily subsidized services to attract users and dominate the market. Once they achieved a critical mass of users, prices began to rise. Similarly, AI companies are transitioning from growth-focused strategies to profitability, signaling an inevitable increase in costs for users. Open source AI: A Limited Alternative Open source AI models present a potential alternative to proprietary systems, but they come with significant trade-offs. While these models are often cheaper per token, they typically require more tokens to achieve results comparable to proprietary systems. This makes them less cost-effective for complex tasks. For businesses and individuals seeking advanced AI capabilities, open source models may not provide a viable solution to the rising costs of proprietary AI services. The Future: A Tiered AI Economy As the AI industry continues to evolve, a tiered pricing structure is likely to emerge. This model will cater to different user needs and budgets, potentially taking the following form: * Basic AI functionalities, such as simple text generation or image recognition, will remain relatively affordable and accessible. * Advanced features, including reasoning, complex problem-solving and large-scale data analysis, will become premium offerings with higher price points. * Metered, usage-based pricing will replace flat-rate plans, requiring users to carefully manage their AI consumption to control costs. This shift will make cost management a critical consideration for both individual users and businesses. Those who rely heavily on AI will need to evaluate their usage patterns and prioritize applications that deliver the highest value. Broader Economic and Policy Implications The rising cost of AI services will have far-reaching consequences for the global economy and public policy. Key implications include: * Businesses will need to focus on AI applications that offer the greatest return on investment, potentially limiting experimentation and innovation. * Legislative measures, such as the AI Data Center Moratorium Act, could slow the growth of AI infrastructure, further driving up costs and reducing accessibility. * Industries may need to rethink how AI is integrated into workflows and decision-making processes, balancing cost considerations with the benefits of automation and efficiency. These changes will reshape the role of AI in the global economy, creating both challenges and opportunities for businesses and individuals alike. As the industry adapts to new financial realities, users must prepare for a more complex and costly AI landscape. Media Credit: TheAIGRID Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.

OpenAI Weighs Major Pricing Shake-Up To Counter Anthropic As Both Companies Eye Trillion-Dollar IPO Dream

OpenAI is reportedly considering lowering the prices it charges for its artificial intelligence services as competition with rival Anthropic intensifies ahead of potential public market debuts. OpenAI Reportedly Considers Lower AI Pricing According to a Wall Street Journal report published Wednesday, OpenAI has discussed reducing the cost of tokens, the basic unit used to measure and bill AI usage. The discussions remain ongoing and no final decision has been made. OpenAI did not immediately respond to Benzinga's request for comments. The potential move comes as businesses increasingly scrutinize the cost of adopting AI technologies. OpenAI CEO Sam Altman earlier acknowledged that AI-related expenses have become a significant concern for customers and said the company is exploring ways to provide greater value while lowering costs. A pricing reduction could help OpenAI strengthen its position in the enterprise market, where Anthropic has gained momentum through its Claude Code software development platform, according to the report. OpenAI And Anthropic Advance Toward Potential IPOs The pricing deliberations come as both AI firms move closer to public listings. OpenAI announced Monday that it confidentially submitted a draft registration statement on Form S-1 to the Securities and Exchange Commission for a proposed initial public offering. Prediction market platform Polymarket has assigned high odds that OpenAI could surpass a $1.5 trillion valuation at the close of its first day of trading. Anthropic has also confidentially filed paperwork for a potential IPO. Analysts have suggested the company could eventually achieve a valuation exceeding $1 trillion. In May, Anthropic surpassed OpenAI as the world's most valuable startup after raising $65 billion in a Series H funding round that valued the company at $965 billion. Separately, reports have indicated that OpenAI is developing its application into a broader super app that could combine coding tools, AI agents and other services within a single platform. Disclaimer: This content was partially produced with the help of AI tools and was reviewed and published by Benzinga editors. Market News and Data brought to you by Benzinga APIs To add Benzinga News as your preferred source on Google, click here.

OpenAI considers drastic price cuts, anticipating war for users with Anthropic: Report

The company might lower prices for tokens, the central unit for gauging AI costs, though the discussions are still in flux, ⁠the report added. OpenAI is considering drastically reducing the prices it charges users as it ⁠seeks ⁠to win customers from its competitor Anthropic, the Wall Street Journal reported on ⁠Wednesday, citing people familiar with the ⁠matter. The company might lower prices for tokens, the central unit for gauging AI costs, though the discussions are still in flux, ⁠the report added. Reuters could not immediately verify the report.

OpenAI Weighs Price Cuts as Competition With Anthropic Takes Shape | PYMNTS.com

The Wall Street Journal reported Thursday (June 11) that the discussions are still in flux, citing people familiar with the matter. But CEO Sam Altman has already been signaling the direction publicly. At a recent event, he acknowledged that AI costs have become "a huge issue" for business customers. "I think we'll have a lot of ways we can help people get more value for less spend," he said. The problem with that promise is that both companies are already losing billions of dollars. The computing costs required to run AI systems at scale are enormous, and cutting token prices would compress margins further right as both OpenAI and Anthropic are pursuing IPOs that will put their economics in front of public investors for the first time. OpenAI filed confidentially for an IPO earlier this week. In a recent message to employees, Altman said the company plans to go public within the next year. The competitive pressure driving the price discussion is real. Anthropic's revenue surged after its coding tool Claude Code caught fire among software engineers, and the startup briefly overtook OpenAI's valuation. OpenAI has since made its own coding tool, Codex, a company priority. But enterprise enthusiasm for AI has started running into budget ceilings. An Uber executive said earlier this year the company had exhausted its 2026 spending on agentic AI. Another executive said last month that it was hard to connect AI-driven coding gains to actual product improvements customers could see. Those admissions have sparked a broader debate in Silicon Valley about "tokenmaxxing," which is burning through tokens at high volume without a clear return on the spend. That backdrop is what makes a price war both logical and risky. The two companies have captured most of the revenue flowing into new AI products, but investors have long pointed to one structural vulnerability: customers can switch between them easily. A price war tests that weakness directly, and whoever blinks first sets the floor for an industry that has not yet figured out how to grow profitably.

OpenAI's GPT-5.6 Release is Triggering an AI Price War

The AI landscape is bracing for significant shifts as OpenAI prepares to launch ChatGPT 5.6, codenamed "Kindle," a model poised to enhance reasoning, coding and vision processing capabilities. This release comes amid growing competition, with Anthropic's Claude Fable 5 setting new performance benchmarks but facing criticism over restrictive safeguards. As highlighted by Universe of AI, the introduction of GPT-5.6 not only raises the bar for technical advancements but also signals a potential price war, with OpenAI reportedly considering reduced pricing to broaden accessibility and challenge its rivals. Explore how this competitive environment is reshaping the AI market, from the implications of OpenAI's pricing strategy to the evolving role of safeguards in enterprise applications. Gain insight into the practical advancements ChatGPT 5.6 offers, such as improved agentic functionalities and streamlined coding workflows, as well as the broader challenges of scaling AI infrastructure. This breakdown provides a clear view of what's at stake as major players vie for dominance in a rapidly evolving industry. Claude Fable 5 Raises the Bar Anthropic's Claude Fable 5 has emerged as a formidable competitor, setting a new benchmark for AI performance. Its strengths lie in advanced reasoning and agentic workflows, which have garnered widespread acclaim from enterprise users. However, the model's restrictive safeguards have sparked criticism, with users reporting issues such as false positives and limited flexibility that hinder specific applications. In response, Anthropic has pledged to refine these safeguards and enhance transparency, aiming to strike a balance between safety and usability. This commitment has placed pressure on competitors like OpenAI and Google to address similar concerns in their own models, further intensifying the competitive landscape. What GPT-5.6 Brings to the Table OpenAI's GPT-5.6, codenamed "Kindle," represents a significant leap forward from its predecessor, GPT-5.5. The model introduces a range of advancements designed to enhance its utility for developers and enterprises alike. Key features include: * Enhanced reasoning capabilities: Improved problem-solving for tackling complex scenarios. * Streamlined coding workflows: Tools designed to support developers in creating and debugging code more efficiently. * Improved agentic functionalities: Greater autonomy for executing tasks with minimal human intervention. * Advances in vision processing: Enhanced capabilities for image recognition and front-end generation. These improvements aim to position GPT-5.6 as a versatile and powerful tool, directly challenging the dominance of Anthropic's Claude Fable 5. By addressing both technical and practical needs, OpenAI seeks to solidify its leadership in the AI sector. Here is a selection of other guides from our extensive library of content you may find of interest on ChatGPT 5. Price Wars Reshape the AI Landscape OpenAI's strategic decision to potentially lower the price of GPT-5.6 underscores the growing importance of affordability in the AI market. While the costs associated with training and deploying large-scale AI models remain substantial, reducing prices could attract a broader user base, including small and medium-sized enterprises. This approach, while potentially involving short-term financial trade-offs, reflects OpenAI's commitment to expanding accessibility and undercutting competitors like Anthropic. The resulting price war is expected to reshape the AI landscape, driving innovation while making advanced AI solutions more accessible to a wider audience. Ongoing Challenges in AI Development Despite rapid advancements, the AI industry continues to face several persistent challenges that complicate the deployment of large-scale models. These include: * Model safeguards: While essential for preventing misuse, safeguards often lead to usability issues, particularly for enterprise clients requiring greater flexibility in their applications. * Infrastructure limitations: Scaling AI models remains a significant hurdle, with many companies relying on external data centers, which can impact both performance and cost efficiency. These challenges highlight the complexity of deploying AI solutions at scale, even as the technology itself becomes more sophisticated. Addressing these issues will be critical for companies seeking to maintain a competitive edge. OpenAI's Focus on Enterprise Solutions To tackle these challenges, OpenAI has made strategic investments in secure cloud execution technologies, including its acquisition of Anna. These advancements are expected to bolster Codex's enterprise capabilities, allowing more secure and efficient AI deployments. By prioritizing enterprise applications, OpenAI is positioning itself as a leader in the business-oriented AI market. This focus on long-term value reflects a broader strategy to cater to the needs of enterprise clients while addressing the limitations of current AI infrastructure. Google's Strategic Advantage While Google's AI models currently trail behind in certain areas, the company's extensive infrastructure and global distribution networks provide a significant competitive advantage. With access to vast data centers and advanced cloud execution technologies, Google is uniquely positioned to scale its AI solutions efficiently. Over time, this focus on scalability and integration could enable Google to close the gap with competitors like OpenAI and Anthropic. By using its existing resources, Google aims to establish itself as a key player in the evolving AI market. What Lies Ahead The release of GPT-5.6 and the ensuing price war mark a critical turning point for the AI industry. As OpenAI, Anthropic and Google continue to push the boundaries of AI capabilities, they must also navigate complex challenges such as model safeguards, infrastructure scalability and ethical considerations. For users, this competition promises more advanced and affordable AI solutions, fostering innovation across industries. However, it also underscores the importance of transparency and responsible development as these powerful technologies continue to evolve. The coming years will likely see further advancements, reshaping the role of AI in both enterprise and consumer applications. Media Credit: Universe of AI Disclosure: Some of our articles include affiliate links. If you buy something through one of these links, Geeky Gadgets may earn an affiliate commission. Learn about our Disclosure Policy.

OpenAI Reportedly Plans AI Price Reductions Amid Rising Anthropic Competition

OpenAI is reportedly exploring substantial price cuts for its artificial intelligence offerings as competition with Anthropic intensifies across both consumer and enterprise markets. According to a Wall Street Journal report, the company is considering lowering the cost of tokens, the units used to measure and bill AI usage, in anticipation of similar pricing moves from Anthropic. These arguments show how quickly the AI sector has transformed with companies racing to control the market. OpenAI can maintain its dominance in the industry through cost effectiveness even amid increased competition.

OpenAI weighs slashing prices in attempt to lure users from rival Anthropic: report

OpenAI is considering slashing prices in an effort to win customers from its rival Anthropic - potentially igniting a price war ahead of planned public listings this year by both companies. The San Francisco-based artificial intelligence giant is contemplating big price cuts for its tokens, the unit of measurement AI companies use to charge for their products, according to a report in the Wall Street Journal. The potential price cuts come in response to similar reductions in pricing it's expecting from Anthropic. Businesses have acknowledge they're thinking twice about using costly AI tools and OpenAI Chief Executive Sam Altman has said this year that high prices are a growing challenge. "I think we'll have a lot of ways we can help people get more value for less spend," Altman said at a tech conference this year. Big price cuts could eat into profit margins for the competing companies, adding more pressure to their businesses which already burn through billions of dollars. AI requires costly computing to process queries and perform tasks. Anthropic's business lately has been surging, fueling perceptions that it's pulling ahead of OpenAI. Led by CEO Dario Amodei, Anthropic's valuation reached a whopping $965 billion in its latest $65 billion financing round -- surpassing OpenAI to become the most valuable AI startup. Anthropic's coding tool Claude Code has surged in popularity with software engineers, powering revenue to new heights. Signs of Anthropic's valuation run-up flashed last month on so-called secondary markets, where shares of still-private companies are traded. Buyers scooping up coveted Anthropic shares vaulted the AI giant's valuation on some trading platforms to $1 trillion. According to the report, some company leaders are now seeking to rein in AI spending after pouring huge sums into Anthropic's tools - top brass are debating whether so-called tokenmaxxing where companies go all in on AI to boost productivity is worth it. Such comments from many executives have triggered a debate within Silicon Valley about tokenmaxxing, or the practice of using as many tokens as possible to boost productivity, including in ways that don't generate returns on investment. Anthropic, OpenAI and Elon Musk's AI outfit housed within SpaceX are all expected to go public this year. SpaceX recently filed paperwork to launch its IPO around June 12, boldly aiming for a $1.5 trillion valuation.