Anthropic overhauls Claude pricing as users report decline in model performance

Anthropic ejects bundled tokens from enterprise seat deal

More bad news for Claude users. Anthropic has revised its seat-based pricing for enterprise customers, shifting them to a new pricing plan upon contract renewal. The change comes at a time of turmoil for the company, which in the run-up to a rumored IPO has seen demand for Claude AI services outpace capacity, resulting in adjustments to terms of service and rate limits designed to ensure availability. The price structure revision appears to have been disclosed more than a week ago on the company's "How am I billed for my Enterprise plan?" support page. The page update notice fails to specify a publication date, but includes an April 7, 2026 last updated time stamp in its HTML. The support document says, "Chat-only seats and Standard/Premium seats are no longer available for new contracts - both legacy plan types are transitioning to the single Enterprise seat at their next renewal." Anthropic offers three individual subscription tiers (Free, Pro at $20 per month, and Max at $100 or $200 per month), a Team tier for small organizations ($25 per month or $125 per month), and an Enterprise tier for large organizations (negotiated). Individual and seat-based subscriptions impose time and usage limits but keep the price flat, with extra usage beyond those limits on a pay-as-you-go basis. Anthropic also sells pay-as-you-go API access, metered at varying token rates, which the company refers to as "consumption billing." Buying Claude through the API has been significantly more expensive than buying it through subscription plans, an ongoing source of friction with customers who resent recent policy changes that have disallowed subscription plans with third-party tools. What's changed is that the seat-based component - the flat monthly fee and associated token pool - no longer comes with subsidized tokens. The company's legacy documentation said, "Some Enterprise organizations are on seat-based plans with per-seat usage limits rather than consumption billing ... Seat-based plans charge a flat monthly fee per seat and include usage limits." That's no longer an option. The new seat plan, according to The Information, would see $200/month seat pricing drop to $20/month with token usage billed at metered rates. IntuitionLabs, an AI consultancy for the pharmaceutical industry, previously published an analysis of Anthropic's pricing options. CEO Adrien Laurent told The Register in an email that the restructured pricing only applied to enterprise customers. "In our experience at IntuitionLabs, our Enterprise clients were already spending significantly on overage," said Laurent. "Most users were in extra usage territory anyway, well beyond what the base seat fee covered. "What's actually changing is that the seat fee no longer bundles any usage allowance at all. Every token gets billed at standard API rates on top of the base seat. For some of our clients, the base seat was only about 20 percent of their total bill, and the other 80 percent was already metered API usage. For them, the shift won't feel dramatic. For lighter users who stayed inside the bundle, it will." Asked whether AI pricing needs to be more predictable, Laurent said it already is, if you pay published API rates. "What hasn't been predictable are the 'buffet' subscription packages," he said. "The rules around those seem to change every other week. Session caps, weekly quotas, what counts as usage, which third-party tools are allowed. That's where the unpredictability lives." The flat-rate plans, Laurent said, effectively subsidize usage. "It's well known that the $200 Max plan gives you several thousand dollars worth of API-rate credits if you actually push it," he said. "Unfortunately, enterprises don't have access to Max. It's a consumer plan. But even at that level of effective consumption per seat, the business value is clearly there. Most users are enjoying a real discount." Laurent expects AI tooling and models to improve in ways that reduce token expenditures, through better compression, smarter document conversion, and improved routing. "That should relieve some of the pressure," he said. "But it's also very possible that Anthropic is acquiring customers faster than it can scale capacity, and the unit economics simply don't work at the old prices. The subsidy has to come down somewhere." The danger, Laurent argues, is that Anthropic may lose interest in serving small organizations and individuals if enterprise spending gets large enough. "At that point, they could move toward a billing model that only works for enterprises, and the flat-rate consumer and prosumer plans that got the whole ecosystem here could quietly go away," he said. Anthropic did not respond to a request for comment. ®

Perspective: AI demand is inflated, and only Anthropic is being realistic

AI demand metrics are broken and only Anthropic is being realistic The main demand signal for artificial intelligence looks explosive on paper, but it may be significantly overstated. Anthropic, by pricing its tools for that reality, might be the best-positioned AI company if a correction comes. Tokens are the basic unit of AI usage: words and characters that make up both the queries users send and the output models generate. Chatting with an AI consumes a couple of hundred tokens per paragraph. Agentic AI, where models write code, browse the web, and execute multi-step workflows, burns through thousands more per session. Using the rates of Anthropic's latest model, one million tokens of input (prompts) costs $5, and one million tokens of output (the model's responses) costs $25. AI companies cite the boom in token consumption to justify the hundreds of billions of dollars being spent on infrastructure to serve it. But token consumption is becoming a distorted metric. Meta and Shopify say they have created internal leaderboards that track how many tokens employees use. Nvidia CEO Jensen Huang has said he'd be "deeply alarmed" if an engineer earning $500,000 a year wasn't using at least $250,000 worth of compute -- measuring what an engineer spends on AI instead of what they produce with it. Once companies start measuring AI adoption by volume, employees optimize for the metric instead of the outcome. "If your goal is to just burn a lot of money, there are easy ways to do that," said Ali Ghodsi, CEO of Databricks, which processes AI workloads for thousands of enterprises. "Resubmit the query to ten places. Put up a loop that just does it again and again. It's going to cost a lot of money and not lead to anything." Jen Stave, executive director of the Harvard Business School AI Institute, hears the same from enterprise leaders. "I've talked to a dozen CTOs or CIOs who are all saying, 'Actually I'm having a really hard time finding an ROI framework for this,'" she said. Anthropic is planning for the possibility that the demand projections are wrong. CEO Dario Amodei has described what he calls a "cone of uncertainty" - data centers take one to two years to build, so companies are committing billions now for demand they can't yet verify. Buy too little and lose customers when you don't have enough capacity. Buy too much and revenue doesn't arrive on schedule, the math stops working. "If you're off by a couple years, that can be ruinous," Amodei said on the Dwarkesh Patel podcast in February. "I get the impression that some of the other companies have not written down the spreadsheet. They're just doing stuff because it sounds cool." Anthropic's response has been to move away from flat-rate enterprise pricing and toward per-token billing, so the revenue it collects reflects actual usage. It has also cut off some third-party tools that were large consumers of tokens, while OpenAI has been making AI cheaper and easier to consume at scale. Flat-rate pricing has dominated the early years of AI adoption, with fixed monthly fees for generous or unlimited AI access. That model worked when people were chatting with AI. But agentic usage turned what cost thousands of tokens per session into millions, and broke the economics. Anthropic's most generous consumer offering, its $200-a-month Max plan, became a case study. Developers had been routing that subscription through third-party agentic tools like OpenClaw, running AI agents around the clock on a plan designed for conversation. Based on Anthropic's published rates for its latest model, a heavy Claude Code Max user could be paying as little as $200 a month for usage that would've cost the user up to $5,000 without a subscription. On April 4, Anthropic cut off those tools. Boris Cherny, head of Claude Code, wrote on X that the subscriptions "weren't built for the usage patterns of these third-party tools." The same recalibration is happening in enterprise. Older Anthropic contracts included standard and premium seats -- flat monthly fees with a baked-in usage allowance. Those are now labeled "legacy seat types that are no longer available for new Enterprise contracts," according to the company's support page. New enterprise plans charge per seat, with token consumption billed at API rates on top. Anthropic was first to move, but the pressure is building across the industry. OpenAI's Nick Turley, head of ChatGPT, acknowledged on a BG2 podcast that "it's possible that in the current era, having an unlimited plan is like having an unlimited electricity plan. It just doesn't make sense." If every token now carries a price, companies and consumers that budgeted for flat-rate AI are going to start asking what they actually got for it. Ramp CEO Eric Glyman, who recently launched a token-tracking tool, sees the dynamic from the finance side. AI spending across Ramp's customer base has grown 13x over the past year and no one knows how to budget for it. He pointed to Anthropic's approach as the more prudent long-term strategy, and raised a question that should concern OpenAI's investors: if your business model depends on extracting maximum token spend, do you have the incentive to help customers use AI more efficiently? Salesforce is making a similar bet, rolling out a new metric it calls "agentic work units" that tracks the work AI completes rather than the tokens it burns. Both Anthropic and OpenAI are expected to pursue IPOs this year. When they do, the demand question will be the first thing public market investors try to answer. Anthropic, by moving to per-token billing, will have cleaner data on what its customers actually value. OpenAI will have bigger numbers but a harder time proving how much of them are real. If even a meaningful fraction of today's AI demand is inflated, the company that priced for reality will be the one still standing when the correction arrives.

Claude is getting worse, according to Claude

Once the AI darling of programmers everywhere, Anthropic's Claude has been stumbling mightily, both in terms of cost and perceived quality. The service was down briefly on Monday with "a major outage," service trouble that only amplifies growing discontent from customers that even a bot can see. The outage, which involved elevated error rates, affected Claude.ai and Claude Code from 15:31 to 16:19 UTC. That's not all. In the past few months, Claude's answers have been getting less satisfactory, according to social media posts as well as issues filed on GitHub. This has occurred as Anthropic has had to take steps to reduce usage during peak hours to balance capacity and demand. To get a more objective measurement, we pointed Claude itself at the Claude Code GitHub repo, filtered for open issues that mention quality, and gave it the following prompt: "Analyze and graph complaints about Claude Code quality in this repo since January 2026. Use open issues that mention quality concerns. Have these concerns increased lately?" Anthropic's AI model concluded, "Yes, quality complaints have escalated sharply -- and the data tells a pretty clear story." We asked Claude to rerun its self-analysis on Monday and the results were similar, with the model emitting "The velocity is notable: April is already at 20+ quality issues in 13 days, putting it on pace to exceed March's 18 -- which was itself a 3.5× jump over the January-February baseline." Claude itself is not a reliable narrator, and just because someone (or some bot) has reported a concern to the Claude Code repo does not mean that the report is accurate or valid. It appears many issues are now AI-generated - a widely reported concern among open source developers - which may be contributing to report volume. Anthropic's GitHub Actions script also appears to automatically close issues after a period of inactivity, which may serve to mask unresolved problems. The Register has covered some of the issues Claude flagged in its analysis, like the caching issue and claims by AMD's AI director Stella Laurenzo that Claude's responses have been getting worse. Others have yet to be substantiated, such as one claim that "Claude autonomously deleted 35,254 production customer message records and 35,874 billing transactions belonging to a real paying customer (JIXEN)." The individual or bot account behind this post has made no other posts. The Register has attempted to contact Jixen Enterprises Private Limited, which appears to be a private company registered in India, to check on that claim but we've not heard back. Developers have reported data loss from using Claude Code and other models. But if this occurred, no one has ruled out user error. In any event, Claude is capable of citing actual GitHub issue posts to justify its "reasoning," so the general trend - of a growing number of reports about quality - is evident. The model points to issues like "Claude Code's prediction-first behavior is dangerous on capital-at-risk projects" #46212, "Claude Code is unusable for complex engineering tasks with the Feb updates" #42796 (addressed by Claude Code head Boris Cherny), "Artificial degradation, Acquisition Bias, and unacceptable compute throttling for paid users" #46949, and "Opus 4.6: Severe quality degradation on iterative coding tasks" #46099 to justify its conclusion. Data from Margin Lab, however, suggests that Claude Opus 4.6 has at least maintained its score on the SWE-Bench-Pro test. Assessments conducted since February show some variation but no substantive change. Anthropic did not immediately respond to a request to comment on Claude quality concerns.®

Anthropic Is Jacking Up the Price for Power Users Amid Complaints Its Model Is Getting Worse

As usage of Anthropic’s coding-focused AI tools has surged, the company is now tweaking its pricing model in a way that could make it significantly more expensive for some enterprise users. The Information reported Tuesday that Anthropic is shifting Claude Enterprise subscriptions away from a flat fee of up to $200 per user per month to a model that charges based on computing usage, on top of a $20 monthly fee per user. Claude Enterprise is Anthropic’s business-focused bundle, which includes tools like Claude Code and Claude Cowork. These products are particularly compute-intensive, often running for extended periods of time to complete complex tasks. However, as adoption of these tools has grown, so have the costs required to run them, putting pressure on Anthropic’s margins. The Information reported that weekly active users of Claude Code doubled between January and February. Fredrik Filipsson, co-founder of Redress Compliance, which helps companies negotiate software licensing agreements, told the outlet the changes could potentially triple costs for some enterprise customers. Anthropic did not immediately respond to a request for comment from Gizmodo. However, a spokesperson told The Information the changes are meant to better reflect how customers actually use Claude, noting that under the previous system, some users hit usage limits that interrupted their work, while others didn’t fully use the capacity they paid for. The changes come amid growing complaints online that Anthropic may have made recent tweaks to its models that worsened their performance. One particular complaint, posted to GitHub in February by a senior director at AMD, has since gone viral across social media. In the post, Stella Laurenzo wrote that Claude Code could no longer be trusted for complex engineering work. She said the model appeared to decline in performance in February compared to January, including ignoring instructions and providing “simplest fixes" that were incorrect. Users on X began sharing screenshots of the post this month, with one writing, “basically: anthropic sneakily turned down how hard claude thinks before editing code, changed the default from "high" to "medium" effort, and hid the reasoning from session logs. all without telling users.†Claude Code creator Boris Cherny responded on X, calling the allegation “false.†“We defaulted to medium as a result of user feedback about Claude using too many tokens. When we made the change, we (1) included it in the changelog and (2) showed a dialog when you opened Claude Code so you could choose to opt out. Literally nothing sneaky about it â€" this was us addressing user feedback in an obvious and explicit way.†Cherny wrote. These are just the latest examples of how AI companies will need to tweak both their products and pricing strategies as demand for their models explodes and investors insist on seeing a path to profitability after unprecedented levels of investment.

Anthropic: Claude quota drain not caused by cache tweaks

Dev reports suggest long sessions now burn through usage much faster Anthropic last month reduced the TTL (time to live) for the Claude Code prompt cache from one hour to five minutes for many requests, but said this should not increase costs despite users reporting faster depleting quotas. User Sean Swanson posted a bug report showing that Anthropic introduced a one-hour cache for Claude Code context around February 1, then changed it back to a five-minute cache around March 7. "The 5m TTL is disproportionately punishing for the long-session, high-context use case that defines Claude Code usage," said Swanson. When using AI coding assistants or agents, the context is additional data sent along with the user's prompts, such as existing code or background instructions. Context improves the accuracy of the AI but also requires more processing. Claude prompt caching avoids re-processing previously used prompts including context and background information. The cache can have either a five-minute or one-hour TTL. Writing to the five-minute cache costs 25 percent more in tokens, and writing to the one-hour cache 100 percent more, but reading from cache is around 10 percent of the base price. Jarred Sumner, the creator of the Bun JavaScript runtime who now works for Anthropic, agreed that the analysis was "good detective work" but claimed that the change back to the five-minute cache made Claude Code cheaper because "a meaningful share of Claude Code's requests are one-shot calls where the cached context is used once and not revisited." Sumner said that the Claude Code client determines the cache TTL automatically and there are no plans for a global setting. Swanson revised his analysis in response, agreeing that sessions using subagents do benefit from the lower write cost of the five-minute cache since they interact quickly and "their caches almost never expire." However, he said he has been a $200 per month subscriber for over six months and had never hit a quota limit until March. The "extra burn rate" is "making a once great service unusable," he said. Another factor is that the large one-million-token context window available on paid plans with the Claude Opus 4.6 or Sonnet 4.6 models increases costs, especially with cache misses. Claude Code creator Boris Cherny said that "prompt cache misses when using 1M token context window are expensive... if you leave your computer for over an hour then continue a stale session, it's often a full cache miss." He said that Anthropic is investigating a 400,000-token context window by default, with an option for one million tokens if preferred. There is already a configuration setting for this. Cherny said that larger contexts are now common because users are "pulling in a large number of skills, or running many agents or background automations." Some developers are convinced that cache rebuilding and cache misses are major factors in Claude Code quota exhaustion, which has reached the point where Pro users ($20 per month) may get as few as two prompts in five hours. A number of bugs in the caching code have been reported, such that one user said: "Before those are fixed likely any 5 minutes vs 1 h discussion is entirely moot since numbers are totally flawed." The focus on cache optimization may also be evidence that, under the covers, Anthropic's quotas are simply buying less processing time than they did. Swanson is not alone in reporting that Claude's performance has dropped. For example, a user on the enterprise team plan said: "In March I could use Opus all day and it was getting great results. Since the last week of March and into April, I've had sessions where I maxed out session usage under 2 hours and it got stuck in overthinking loops, multiple turns of realising the same thing, dozens of paragraphs of 'but wait, actually I need to do x' with slight variations." That chimes with similar comments from an AI director at AMD. Cache optimization may be important, but it seems unlikely to account for all these reported issues. ®

Is Anthropic 'nerfing' Claude? Users increasingly report performance degradation as leaders push back

A growing number of developers and AI power users are taking to social media to accuse Anthropic of degrading the performance of Claude Opus 4.6 and Claude Code -- intentionally or as an outcome of compute limits -- arguing that the company's flagship coding model feels less capable, less reliable and more wasteful with tokens than it did just weeks ago. The complaints have spread quickly on Github, X and Reddit over the past several weeks, with several high-reach posts alleging that Claude has become worse at sustained reasoning, more likely to abandon tasks midway through, and more prone to hallucinations or contradictions. Some users have framed the issue as "AI shrinkflation" -- the idea that customers are paying the same price for a weaker product. Others have gone further, suggesting Anthropic may be throttling or otherwise tuning Claude downward during periods of heavy demand. Those claims remain unproven, and Anthropic employees have publicly denied that the company degrades models to manage capacity. At the same time, Anthropic has acknowledged real changes to usage limits and reasoning defaults in recent weeks, which has made the broader debate more combustible. VentureBeat has reached out to Anthropic for further clarification on the recent accusations, including whether any recent changes to reasoning defaults, context handling, throttling behavior, inference parameters or benchmark methodology could help explain the spike in complaints. We have also asked how Anthropic explains the recent benchmark-related claims and whether it plans to publish additional data that could reassure customers. As of publication time, we are awaiting a response. Viral user complaints, including from an AMD Senior Director, argue Claude has become less capable One of the most detailed public complaints originated as a GitHub issue filed by Stella Laurenzo on April 2, 2026, whose LinkedIn profile identifies her as Senior Director in AMD's AI group. In that post, Laurenzo wrote that Claude Code had regressed to the point that it could not be trusted for complex engineering work, then backed that claim with a sprawling analysis of 6,852 Claude Code session files, 17,871 thinking blocks and 234,760 tool calls. The complaint argued that, starting in February, Claude's estimated reasoning depth fell sharply while signs of poorer performance rose alongside it, including more premature stopping, more "simplest fix" behavior, more reasoning loops, and a measurable shift from research-first behavior to edit-first behavior. The post's broader point was that for advanced engineering workflows, extended reasoning is not a luxury but part of what makes the model usable in the first place. That GitHub thread then escaped into the broader social media conversation, with X users including @Hesamation, who posted screenshots of Laurenzo's GitHub post to X on April 11, turning it into an even more viral talking point. That amplification mattered because it gave the wider "Claude is getting worse" narrative something more concrete than anecdotal frustration: a long, data-heavy post from a senior AI leader at a major chip company arguing that the regression was visible in logs, tool-use patterns and user corrections, not just gut feeling. Anthropic's public response focused on separating perceived changes from actual model degradation. In a pinned follow-up on the same GitHub issue posted a week ago, Claude Code lead Boris Cherny thanked Laurenzo for the care and depth of the analysis but disputed its main conclusion. Cherny said the "redact-thinking-2026-02-12" header cited in the complaint is a UI-only change that hides thinking from the interface and reduces latency, but "does not impact thinking itself," "thinking budgets," or how extended reasoning works under the hood. He also said two other product changes likely affected what users were seeing: Opus 4.6's move to adaptive thinking by default on Feb. 9, and a March 3 shift to medium effort, or effort level 85, as the default for Opus 4.6, which he said Anthropic viewed as the best balance across intelligence, latency and cost for most users. Cherny added that users who want more extended reasoning can manually switch effort higher by typing in Claude Code terminal sessions. That exchange gets at the core of the controversy. Critics like Laurenzo argue that Claude's behavior in demanding coding workflows has plainly worsened and point to logs and usage patterns as evidence. Anthropic, by contrast, is not saying nothing changed. It is saying the biggest recent changes were product and interface choices that affect what users see and how much effort the system expends by default, not a secret downgrade of the underlying model. That distinction may be technically important, but for power users who feel the product is delivering worse results, it is not necessarily a satisfying one. External coverage from TechRadar and PC Gamer further amplified Laurenzo's post and larger wave of agreement from some power users. Another viral post on X from developer Om Patel on April 7 made the same argument in even more direct terms, claiming that someone had "actually measured" how much "dumber" Claude had gotten and summarizing the result as a 67% drop. That post helped popularize the "AI shrinkflation" label and pushed the controversy beyond hard-core Claude Code users into the broader AI discourse on X. These claims have resonated because they map closely onto what many frustrated users say they are seeing in practice: more unfinished tasks, more backtracking, more token burn and a stronger sense that Claude is less willing to reason deeply through complicated coding jobs than it was earlier this year. Benchmark posts turned anecdotal frustration into a public controversy The loudest benchmark-based claim came from BridgeMind, which runs the BridgeBench hallucination benchmark. On April 12, the account posted that Claude Opus 4.6 had fallen from 83.3% accuracy and a No. 2 ranking in an earlier result to 68.3% accuracy and No. 10 in a new retest, calling that proof that "Claude Opus 4.6 is nerfed." That post spread widely and became one of the main anchors for the broader public case that Anthropic had degraded the model. Other users also circulated benchmark-related or test-based posts suggesting that Opus 4.6 was underperforming versus Opus 4.5 in practical coding tasks. Still other posts pointed to TerminalBench-related results as supposed evidence that the model's behavior had changed in certain harnesses or product contexts. The effect was cumulative: benchmark screenshots, side-by-side tests and anecdotal frustration all began reinforcing one another in public. That matters because benchmark claims tend to travel farther than more subjective complaints. A developer saying a model "feels worse" is one thing. A screenshot showing a ranking drop from No. 2 to No. 10, or a dramatic percentage swing in accuracy, gives the appearance of hard proof, even when the underlying comparison may be more complicated. Critics of the benchmark claims say the evidence is weaker than it looks The most important rebuttal to the BridgeBench claim did not come from Anthropic. It came from Paul Calcraft, an outside software and AI researcher on X, who argued that the viral comparison was misleading because the earlier Opus 4.6 result was based on only six tasks while the later one was based on 30. In his words, it was a "DIFFERENT BENCHMARK." He also said that on the six tasks the two runs shared in common, Claude's score moved only modestly, from 87.6% previously to 85.4% in the later run, and that the bigger swing appeared to come mostly from a single fabrication result without repeats. He characterized that as something that could easily fall within ordinary statistical noise. That outside rebuttal matters because it undercuts one of the cleanest and most viral claims in circulation. It does not prove users are wrong to think something has changed. But it does suggest that at least some of the benchmark evidence now driving the story may be overstated, poorly normalized or not directly comparable. Even the BridgeBench post itself drew a community note to similar effect. The note said the two benchmark runs covered different scopes -- six tasks in one case and 30 in the other -- and that the common-task subset showed only a minor change. That does not make the later result meaningless, but it weakens the strongest version of the "BridgeBench proved it" argument. This is now a key feature of the controversy: the claims are not all equally strong. Some are grounded in first-hand user experience. Some point to real product changes. Some rely on benchmark comparisons that may not be apples-to-apples. And some depend on inferences about hidden system behavior that users outside Anthropic cannot directly verify. Earlier capacity limits gave users a reason to suspect more changes under the hood The current backlash also lands in the shadow of a real, confirmed Anthropic policy change from late March. On March 26, Anthropic technical staffer Thariq Shihipar posted that, "To manage growing demand for Claude," the company was adjusting how 5-hour session limits work for Free, Pro and Max subscribers during peak hours, while keeping weekly limits unchanged. He added that during weekdays from 5 a.m. to 11 a.m. Pacific time, users would move through their 5-hour session limits faster than before. In follow-up posts, he said Anthropic had landed efficiency wins to offset some of the impact, but that roughly 7% of users would hit session limits they would not have hit before, particularly on Pro tiers. In an email on March 27, 2026, Anthropic told VentureBeat that Team and Enterprise customers were not affected by those changes, and that the shift was not dynamically optimized per user but instead applied to the peak-hour window the company had publicly described. Anthropic also said it was continuing to invest in scaling capacity. Those comments were about session limits, not model downgrades. But they are important context, because they establish two things that users now keep connecting in public: first, Anthropic has been dealing with surging demand; second, it has already changed how usage is rationed during busy periods. That does not prove Anthropic reduced model quality. It does help explain why so many users are primed to believe something else may also have changed. Anthropic says user-facing changes, not secret degradation, explain much of the uproar Anthropic-affiliated employees have publicly pushed back on the broadest accusations. In one widely circulated reply on X, Cherny responded to claims that Anthropic had secretly nerfed Claude Code by writing, "This is false." He said Claude Code had been defaulted to medium effort in response to user feedback that Claude was consuming too many tokens, and that the change had been disclosed both in the changelog and in a dialog shown to users when they opened Claude Code. That response is notable because it concedes a meaningful product change while rejecting the more conspiratorial interpretation of it. Anthropic is not saying nothing changed. It is saying that what changed was disclosed and was aimed at balancing token use, not secretly reducing model quality. Public documentation also supports the fact that effort defaults have been in motion. Claude Code's changelog says that on April 7, Anthropic changed the default effort level from medium to high for API-key users as well as Bedrock, Vertex, Foundry, Team and Enterprise users. That suggests Anthropic has actively been tuning these settings across different segments, which could plausibly affect user perceptions even if the core model weights are unchanged. Shihipar has also directly denied the broader demand-management accusation. In a reply on X posted April 11, he said Anthropic does not "degrade" its models to better serve demand. He also said that changes to thinking summaries affected how some users were measuring Claude's "thinking," and that the company had not found evidence backing the strongest qualitative claims now spreading online. The real issue may be trust as much as model quality What is clear is that a trust gap has opened between Anthropic and some of its most demanding users. For developers who rely on Claude Code all day, subtle shifts in visible thinking output, effort defaults, token burn, latency tradeoffs or usage caps can feel indistinguishable from a weaker model. That is true whether the root cause is a product setting, a UI change, an inference-policy tweak, capacity pressure or a genuine quality regression. It also means both sides of the fight may be talking past each other. Users are describing what they experience: more friction, more failures and less confidence. Anthropic is responding in product terms: effort defaults, hidden thinking summaries, changelog disclosures, and denials that demand pressure is causing secret model degradation. Those are not necessarily incompatible descriptions. A model can feel worse to users even if the company believes it has not "nerfed" the underlying model in the way critics allege. But coming at a time when Anthropic's chief rival OpenAI has recently pivoted and put more resources behind its competing, enterprise and vibe-coding focused product Codex -- even offering a new, more mid-range ChatGPT subscription in an effort to boost usage of the tool -- it's certainly not the kind of publicity that stands to benefit Anthropic or its customer retention. At the same time, the public evidence remains mixed. Some of the most viral claims have come from developers with detailed logs and strong opinions based on repeated use. Some of the benchmark evidence has been challenged by outside observers on methodological grounds. And Anthropic's own recent changes to limits and settings ensure that this debate is happening against a backdrop of real adjustments, not pure rumor.

How Anthropic's Pentagon fight became an unexpected growth engine

A version of this article originally appeared in Quartz's AI & Tech newsletter. Sign up here to get the latest AI & tech news, analysis and insights straight to your inbox. When the Pentagon declared Anthropic a supply-chain risk earlier this year, the designation was meant to hurt. The label, typically reserved for adversarial foreign companies, was a pointed message from Defense Secretary Pete Hegseth after Anthropic refused to let the military use its AI models for autonomous weapons or domestic surveillance. Anthropic called the move "legally unsound" and sued. What followed has been less a corporate crisis than a coming-out party. In the months since the standoff began, Anthropic says its annualized revenue has grown from roughly $9 billion at the end of 2025 to more than $30 billion today, and paid consumer subscriptions have more than doubled. The Claude app briefly topped Apple $AAPL's download charts. The legal fight has temporarily produced a split decision -- one court blocked the government from enforcing a ban on Claude, while a federal appeals court allowed the Pentagon's blacklisting to stand while litigation plays out. And last week, Anthropic unveiled Project Glasswing, a sweeping cybersecurity initiative that brought in partners including AWS, Apple, Microsoft $MSFT, Google $GOOGL, and Cisco $CSCO to test a new, unreleased model called Claude Mythos -- described in leaked internal materials as "by far the most powerful AI model we've ever developed." For a company that spent years in OpenAI's shadow, it has been a remarkable few months. When OpenAI announced its own deal with the Pentagon, ChatGPT uninstalls jumped 295% day-over-day, according to market intelligence firm Sensor Tower. Claude downloads rose 51% over the same weekend. An analysis of credit card data from roughly 28 million U.S. consumers found that new paid subscribers surged sharply during the weeks between initial reports of the standoff and CEO Dario Amodei's public statement about it in late February. Previous users returned to the platform in record numbers that same month. The revenue growth, though, is mostly an enterprise story. The number of clients spending at least $1 million a year has more than doubled since February, crossing 1,000 customers, according to Anthropic. Claude Code, the company's developer tool released in January, has been a major driver, adding subscribers quickly and helping push the run rate past milestones that took other software companies decades to reach. The company has committed $100 million to let partners use it for defensive security work, and says it has no plans to release it to the public. That is a notable stance for a company whose business depends on people using its models. The implication is that Mythos is simply too capable to hand over freely. The announcement had a dual function. It demonstrated that Anthropic's safety-first positioning is more than rhetoric, and it showcased a model that the company has explicitly positioned as too dangerous for broad deployment. That framing has drawn some skepticism. Talking up a model's risks is a well-worn move in the AI industry, and the timing, coinciding with reported IPO discussions for later this year, was not lost on observers. Still, the partners involved are not small names. Microsoft, Google, CrowdStrike $CRWD, and JPMorganChase have all said they're using Mythos Preview in their own security operations. The credibility that comes with those endorsements is harder to manufacture than a press release. The Pentagon, for its part, has not let up. The appeals court ruling means Anthropic remains locked out of Defense Department contracts for now, even as it can continue working with other government agencies. Defense Under Secretary Emil Michael has continued to publicly attack Amodei, and the legal fight remains unresolved. But Anthropic keeps growing. Meanwhile, OpenAI is taking some hits. A recent New Yorker profile of CEO Sam Altman drew on accounts from former colleagues describing him as, at best, slippery. The piece arrived as OpenAI was also reported to be navigating internal friction with its CFO ahead of its own planned IPO. And in March, The Wall Street Journal reported that top executives were finalizing plans to pull back on "side quests," including its Sora video tool, to refocus on coding and enterprise customers, the exact ground Anthropic has spent the past year consolidating. None of that is Anthropic's doing. But the contrast with Amodei's public positioning has been hard to miss.

Anthropic faces user backlash over reported performance issues in its Claude AI chatbot | Fortune

Anthropic's popular Claude AI model has seen a significant decline in performance recently according to many developers and heavy users, who say the model increasingly fails to follow instructions, opts for sometimes inappropriate shortcuts, and makes more mistakes on complex workflows. The complaints appear to be connected to recent changes Anthropic quietly made to the way Claude operates, reducing the model's default "effort" level in order to economize on the number of tokens, or units of data, the model processes in response to each request. The more tokens processed per task, the more computing power that task consumes. And there is widespread speculation that Anthropic, which has announced fewer multi-billion dollar deals for data center capacity than some of its rivals, may be running short of computing resources after its adoption of its products soared in the past few months. User dissatisfaction with Claude's sudden performance decline and anger at Anthropic's perceived lack of transparency could potentially derail the company's runaway growth, just as the company is hoping to woo investors for a potential IPO. The claims that Anthropic has not been candid about the changes it has made to the way Claude operates or the way the changes may increase the cost for using Claude are particularly threatening to Anthropic because it, more than any other AI company, has tried to build a brand reputation on being more transparent than other AI companies and more aligned with its users' interests. Anthropic declined to answer Fortune's specific questions about Claude users' complaint on the record. Boris Cherny, the Anthropic executive who leads its Claude Code product, responded to user complaints online by saying that Anthropic had reduced the default "effort" Claude makes in answering user prompts to "medium" in response to user feedback that Claude was previously consuming too many tokens per task. But many users complained that the company had not highlighted this change to users. The situation has caused a pile-on of speculation and allegations -- including from some of its competitors -- that the company is purposely degrading performance due to a lack of compute capacity. Across the industry, AI companies are facing rising GPU costs, constrained data center expansion, and difficult trade-offs over which products to prioritize as demand for "agentic" AI systems accelerates faster than infrastructure can scale. While an Anthropic spokesperson has said publicly that the AI lab does not degrade its models to better serve demand, there are reasons to believe the company is facing more acute constraints than some rivals. Anthropic suffered a series of recent outages as usage has increased and has introduced stricter usage limits during peak hours, drawing complaints from some users. In an internal memo reported by CNBC, OpenAI's revenue chief also claimed that Anthropic had made a "strategic misstep" by not securing enough compute capacity, and was "operating on a meaningfully smaller curve" than competitors. (Anthropic declined to answer CNBC's questions about these claims .) Meanwhile, Anthropic also announced last week that it had trained a new, yet-to-be-released model called Mythos that is significantly more capable than its Opus AI model -- but which is also larger and more expensive to run, meaning that likely consumes more computing capacity than prior models. Anthropic stressed that it's not releasing the model to the general public yet because of security concerns, but some have questioned whether Anthropic lacks sufficient compute capacity to support a broad Mythos rollout. The scrutiny on Anthropic underscores the fast-changing nature of the AI market and the stakes involved. Just last week, Anthropic stunned the industry by announcing that its annualized recurring revenue, or ARR, is now $30 billion, up from $9 billion at the end of 2025. OpenAI said last month that it is generating $2 billion a month in revenue, or $24 billion a year, although the two companies do not report revenues in exactly the same way, making direct comparisons problematic. Anthropic has recently benefited from a flood of new users, first due to the popularity of its AI coding tool, Claude Code, and later from a wave of consumer support that followed its feud with the U.S. Department of Defense. Many users switched to Claude from rivals such as OpenAI's ChatGPT after the Trump administration designated Anthropic a "supply chain risk." Anthropic had said the dispute stemmed from its insistence that U.S. government agree in its contract not to use the company's technology in lethal autonomous weapons or for the mass surveillance of American citizens. Over the last few years, Anthropic has gained significant ground in the AI race, emerging as a leader in enterprise AI and building up significant goodwill among developers and enterprise users. But if the anger around Claude's performance issues persists, it risks eroding some of that goodwill and could lead the company to stumble at a critical moment. In response to some of the controversy around Claude's recent performance issues, Cherny, the Claude Code head, said that Claude Opus 4.6 -- Anthropic's flagship model -- had introduced "adaptive thinking" in early February, which allows the model to decide how much reasoning to apply to a given task rather than using a fixed budget. In early March, Anthropic also shifted the default setting down to a "medium effort" level, Cherny said. While Claude Code users can manually change the tool's effort levels, users who pay for the Pro versions of Cowork or the desktop version of Claude are not able to change the default at this time. To resolve some of the user issues, Cherny said the company will test "defaulting Teams and Enterprise users to high effort, to benefit from extended thinking even if it comes at the cost of additional tokens & latency" going forward. He also pushed back on speculation that the model had been purposely watered down and on complaints from users that the change was rolled out with a lack of transparency, claiming the changes were made in response to user feedback and were flagged to users via a pop-up within the Claude Code interface. Most of the user complaints center on Claude Code, Anthropic's AI-powered coding tool, which has become one of the company's most popular and fastest-growing products. Launched in early 2025, Claude Code operates as a command-line agent that can read, write, and execute code autonomously within a developer's environment. Since its debut, it has been widely adopted by individual developers and large enterprise engineering teams who rely on it for complex, multi-step coding tasks. The recent changes in the performance of Claude Code gained widespread attention on social media thanks to a GitHub analysis that appears to be from Stella Laurenzo, a senior director of AI at AMD. In a widely-shared analysis, Laurenzo said the changes had made Claude "unusable for complex engineering tasks." In her analysis, she found that from late February into early March, Claude moved from a "research-first" approach -- reading multiple files and gathering context before making changes -- to a more direct "edit-first" style. The model reads less context before acting, makes more mistakes, and requires significantly more user intervention, according to the analysis. The analysis also points to a rise in behaviors like stopping too early, avoiding responsibility, or asking unnecessary permission, which it links to a reduction in "thinking" depth over the same period. "Claude has regressed to the point [that] it cannot be trusted to perform complex engineering," she wrote. In a comment responding to the analysis, Anthropic's Cherny says the analysis is likely misreading at least part of the data, claiming that the model's reasoning hasn't been reduced but that Anthropic had made a change so that the full "reasoning trace" of the model is no longer visible to the user. But Laurenzo is far from the only person having issues with the tool. "I've had incredibly frustrating sessions with Claude Code the past two weeks," Dimitris Papailiopoulos, a principal research manager at Microsoft, wrote on X. "I set effort to max, yet it's extremely sloppy, ignores instructions, and repeats mistakes."

One of Silicon Valley's Hottest Companies Is Facing a Revolt -- From Its Own Fans

Are you sure you want to unsubscribe from email alerts for Alex Kirshner? Sign up for the Slatest to get the most insightful analysis, criticism, and advice out there, delivered to your inbox daily. Earlier this spring, Anthropic scored the marketing coup of a generation. The Pentagon wanted access to the full capabilities of the company's A.I. models, including the right to automate the death of human beings without a fellow member of the species in the loop. Anthropic said no, Pete Hegseth responded by arbitrarily labeling the company a "supply-chain risk," a judge blocked that designation from taking effect, and Anthropic came out of the ordeal smelling like roses. The Defense Department had validated that Anthropic had the industry's best tech and its closest semblance to principles. "The problem for these guys is they are that good," a defense official told Axios. Apparently, their morals were also too strong. It wasn't just that Anthropic won a game of chess against that wily Hegseth. The company was on an amazing run of publicity in general -- all of which revolved around people liking its chatbot Claude a lot. Its viral Super Bowl commercials targeted ChatGPT's introduction of chatbot ads, which at some point merged with a more organic Instagram and TikTok movement about how ChatGPT was a sycophant. ("You didn't run over a kid with your truck. You taught him a lesson about road safety. And you're so real for that.") If you saw a bunch of short-form videos about Claude, they were probably more along the lines of influencers explaining how the model "runs my entire life," or "just killed accountants" (perish the thought!) by finding them unforeseen tax savings. Now, Anthropic has run into a problem. All of the people who became obsessed with its product cost the company a lot of money and a lot of computing power. The A.I. lab's attempt to create a sustainable business out of what is still a cash-incinerating structure may or may not work in the long run, but for now, it's resulted in a furious base of power users. It turns out that being the internet's good A.I. company is quite challenging. A particular challenge is Claude Code, a magic box that takes your words in plain English and converts them into real software before your eyes. A.I. coding has taken software development by storm. Enormous companies use it to work faster, and their engineers know enough about code to use it more powerfully than laymen. Hobbyists use it on personal projects, while freelancers use it to spin up their own business ideas. All of this vibe-coding combined with Claude chatbot use costs Anthropic eye-watering sums that are far in excess of the $20, $100, or $200 someone spends on a monthly subscription. It's become a bit of a media-and-tech parlor game to try to estimate exactly what these losses are, on average. Let's just call them "big." Meanwhile, the company only has access to so much computing power to actually do the work users ask of Claude. So, the A.I. firm has gotten stingier. In the past few weeks, it has switched off people's ability to use Claude subscriptions to power third-party agents like OpenClaw, tightened usage limits at certain times, had a noticeable service interruption, and, according to some users, generally degraded Claude's capabilities. (I do not use the service enough to say whether those people are right.) The company has responded clumsily to users' complaints, spawning several social media news cycles about whether it respects its own customers. The company is burning usage rates and a good bit of customer goodwill. The situation becomes humorous where it concerns OpenAI CEO Sam Altman, a man with a very bad reputation outside of Silicon Valley. OpenAI has been trying to build up its Claude Code competitor, Codex, and sees a window of opportunity in Anthropic's quandary. OpenAI might also have a good bit more computing power than Anthropic does, so Altman slid down the A.I. chimney last week and announced OpenAI would reset Codex usage limits every time the product gets an additional million users. "Happy building!" the benevolent boy king of A.I. told the world. (Also in OpenAI attempts to burnish its image: The company bought a talk show at roughly the same time.) That Altman and Anthropic CEO Dario Amodei seem to hate each other for real is an additional wrinkle in the companies' jostling for people's hearts and minds. Brand positioning as the "ethical A.I. company," or "consumer-friendly A.I. company," or whatever it might be, is not a trivial matter. A Stanford A.I. index study released this week did the useful work of quantifying just how big a gap there is in sentiment toward A.I. between the people working on it and the general public. Americans expect A.I. to cause job losses and have been slower to adopt it than their peers in most other countries, while also not trusting their own government to regulate it. Literally no country has less trust in its leaders to regulate A.I. effectively than we do. It might be appealing to write off Anthropic's and OpenAI's efforts to be seen as the good guys as just another helping of corporate pablum. But if we don't collectively start liking this technology more, and also don't cut into its growth with severe regulation, then there will be a great deal of money in it for whichever company can convince the most people that it's a little bit less of a bloodsucker than its competitors. For a time, it appeared Anthropic would run away with that race. But a tech company willing to stick to its principles against the Trump administration is, in fact, a tech company. Anthropic has been on a nice little run of being a lot of things to a lot of people. It's been a powerful tool for coders. It's been an enterprise juggernaut for business customers, claiming that more than 500 companies spend at least $1 million per year in annualized revenue on its products. (Annualized revenue isn't the same thing as money in the bank, but alas.) It's been a good chatbot to however many millions of people have decided to pay Anthropic $20 a month. It's been a beacon of tech resistance to some of the most dystopian impulses of the second Trump administration. It has, somehow, had enough computing power to be all of those things at once. That cracks are only now starting to show is a legitimate wonder.

Anthropic Switches to Usage-Based Billing for Enterprise Customers | PYMNTS.com

By completing this form, you agree to receive marketing communications from PYMNTS and to the sharing of your information with our sponsor, if applicable, in accordance with our Privacy Policy and Terms and Conditions. That's according to a report Tuesday (April 14) from The Information, which characterizes the change as the latest sign of how the startup is handling the surge in popularity for its artificial intelligence (AI) coding and agent offerings. The new pricing system means customers of Claude Enterprise -- its product suite for large companies -- will have to pay for the amount of computing capacity they use as well as a flat monthly $20 per user fee. Those customers had been paying up to $200 a month for each licensed user, getting a set amount of discounted token usage, Fredrik Filipsson, co-founder of Redress Compliance, told The Information. Flipsson, whose company helps businesses negotiate software licensing agreements, estimated that the pricing changes will double or even triple the cost for heavy users of Claude Enterprise. The Information said it spoke with several IT executives who said they are monitoring whether pricing changes -- which have gone into effect in recent weeks -- will lead to substantially higher bills from Anthropic. A company spokesperson told the publication that the pricing changes don't apply to businesses that pay for free than 150 users. Writing about AI adoption last week, PYMNTS CEO Karen Webster argued that tools like Claude are able to gain ground as consumers encounter new AI models on the job. "ChatGPT expands outward from the consumer, earning trust in low-stakes, high-frequency tasks and carrying that trust into the workplace. The habit comes first; the enterprise follows," Webster wrote. "Claude follows the opposite path. It is encountered in the context of work, where precision matters and the cost of getting it wrong is higher. Contract analysis, code review and complex research are not entry points for casual use. They are reasons to adopt something new. In this case, the enterprise is not the endpoint but the starting point." In related news, PYMNTS wrote earlier this week about the factors that hinder AI adoption at larger companies. "For most large enterprises, organizational readiness is still the bigger barrier than cost," Ben Schein, chief analytics officer and senior vice president of product at Domo, told PYMNTS. That's backed by research from PYMNTS Intelligence's "The Enterprise AI Benchmark Report," which shows that 71% of executives at companies with at least $1 billion in annual revenue believe that organizational readiness is the chief limitation on AI performance. Only 11% said they think that AI technology itself is the primary barrier.

Remember Sam Altman's AI top ups like electricity bill? Anthropic is testing it in reality

This shows AI tools are now being priced based on actual usage, like a utility. Anthropic has made major changes to how it charges enterprise customers using its Claude AI service. The company is shifting from older fixed price plans to a system where businesses pay a smaller fee for each user seat and then pay separately for expected monthly usage. Earlier discounts for using the app have been removed, which could make some companies spend more overall. However, the basic price per user is now lower than before, so it is easier for small teams to start using it. This change shows that AI companies are moving toward charging based on actual use instead of fixed subscription fees, as more businesses around the world start using AI tools. The company says the new pricing model aims to make access to Claude more flexible for business customers while linking cost more closely to how much the service is used each month. Instead of only paying a fixed monthly amount, firms will now pay a lower fee per user and also plan for expected usage in advance. With this approach the company can match costs more fairly with how much each team uses AI tools for tasks like coding, writing, and customer support. At the same time, the company has removed earlier discounts on its API access, which could make things more expensive for big organisations. Some customers who expected low monthly use now have to agree to higher spending estimates, even if they don't actually use that much. This has made some companies worried that their overall costs might increase, especially when they do not use it consistently. Also read: 5 ways to improve AC efficiency and reduce electricity bills during summer season However, the starting price per user has been reduced compared to older plans, which makes it cheaper for small teams and new users to get started. For example, in some plans, the price per user has been cut from $30 to $15. The company is also increasing its computing power by working with big chip providers like Amazon and Google to handle the growing demand from business customers. Also read: Mark Zuckerberg moves desk to AI lab, codes alongside researchers amid AI race Moreover, recent reports also say the company's revenue has grown strongly because more industries are using AI tools. It also said that rapid usage patterns from a small number of customers can lead to faster consumption of computing resources. This is why it is adjusting pricing rules to balance demand and supply more carefully. In future updates, the company plans to improve efficiency so customers can get more stable performance at a lower cost.