‘Absolutely Horrendous’: A Week of AI Upgrades That Users Call Downgrades

The release notes said improvement. The pitch, as ever, was a better model for the same money, or less: Anthropic shipped Claude Fable 5.1 and Claude Mythos 5.1 on 1 September, complete with a slide promising a price cut and relaxed safeguards, and Google pushed out Gemini 3.8 Flash in the same window. Then the people who actually pay for these tools opened them, and spent the next two days on Hacker News explaining, in unusual detail, why the new one felt worse than the old one.

This is a Voices piece, so the point is not our verdict but theirs. And the verdict this week was strikingly consistent across products: not “AI can’t do the job,” but “the thing you sold me as an upgrade is a downgrade, and you tightened the limits and the terms while you were at it.” One commenter, noduerme, caught the mood exactly while complaining about the launch churn itself — the endless front-page announcements of “the top story (or five) on HN every day announcing Spark Opus Fable Grok Gemini” — before landing on the line that could be this column’s masthead: “The model isn’t news. The news on Hacker News is that other professionals feel the same way.”

Quotes sourced from: Hacker News. We opened each thread, lifted the wording verbatim from the live comment, and listed every quote in the Sources below with its handle, platform and date. We quoted only what we could open and read in full — Reddit and X threads we could not reach are not quoted — we aimed at the products and the decisions rather than the people, and, because fairness is the whole job, we kept in the users who defended the tools.

The moan of the day: a launch that landed as ‘absolutely horrendous’

The sharpest reaction to the new release belonged to jorl17, who arrived late to the launch thread with a list of specific, reproducible regressions rather than a vibe. That earns it our moan of the day.

What makes the complaint land is its specificity. jorl17 describes a model that jumps to action before understanding the request, answers “two direct Yes/No questions with 5 paragraphs where it only answers one of them,” and carries an air of being “absurdly full of itself and arrogant… ‘No, but really, you’re wrong and I’m right’. It often is not right.” He is careful to hedge — “My guess is I must be having a bad day” — but notes it is happening across multiple projects and machines, and signs off with the tell of a disappointed upgrader: “Will probably downgrade to 5 while I can.” When the escape hatch a user reaches for is the previous version, the word “upgrade” is doing a lot of unearned work.

The regression nobody wrote in the release notes

Fable’s launch was loud; the concurrent complaint about Opus was quieter and, if anything, more damning, because it describes a capability going backwards over several versions. On a thread bluntly titled “Is it just me, or has Claude Opus gotten worse recently?”, ghoul2 gave the kind of concrete failure mode you can picture: “This seems a worsening that seems to have started at Opus 4.8… it would run the test suite and grep for ‘PASS’, thus completely missing the 4 tests that FAILed.” A model that checks its own work by searching for the word that means success, and so never sees the failures, is a small masterpiece of confident wrongness. ghoul2 says the bias persists “despite explicit instructions to the contrary” and closes with the four words that define a Voices thread: “Anybody else notice this?”

Then there is the feature that simply went missing. exabrial itemised the grievance — “Nerfed Fable… it’s useless” — but the line that matters for anyone doing serious work is the removal buried in the middle: “Removed thought traces, one of the only useful things to make sure your prompts are working correctly.” Taking away the window into a model’s reasoning is exactly the sort of change that a release framed as an improvement can quietly contain, and it sits alongside the watermarking gripe he raises too — a subject we have covered in detail in how Claude now marks even your own writing. None of this shows up on a benchmark, which is precisely why we keep saying that benchmark scores are a weak proxy for how a model behaves once you are the one living with it.

The price that fell on the slide and rose in the receipt

The most checkable complaint of the week was about money, and it is a tidy illustration of a recurring pattern. george_max quoted Anthropic’s own launch claim — “Fable 5.1 will cost an estimated 25% less than Fable 5 for typical workloads” — and then held it against an independent measurement: “artificial analysis contradicts the statement. Fable 5 cost $3.14 per task, while 5.1 cost $3.69 -- around a 15% jump in pricing.”

Both things can be true at once, which is the point. Anthropic reduced the per-token price of cache reads; the cost per task can still climb if the new model does more work to finish the same job. That is not a lie on the slide, but it is the reason the number in the announcement and the number on your invoice keep diverging — the same gap we picked apart when Sonnet 5’s price rise arrived twice. george_max’s conclusion is the unsentimental verdict of a former customer: “These, IMO, are marginal improvements for a more expensive model. I stopped using Claude ~3 months back.”

The cancellations

Enough of the above and users leave, and this week several said so with their receipts. ashkankiani laid out a considered departure rather than a tantrum: “I canceled my Claude subscription, though I did get some utility out of it, because of how much steering was required to use it on complex projects.” His structural complaint is the one that bites heavy users — “anyone who is using Fable seriously will run out of usage limits very quickly” — and his closing note is bleaker than a pricing gripe: “I’m not sure I’ll re-subscribe or even really use AI again because it’s honestly more frustrating than it’s worth.” He still left the company a detailed UX bug report on the way out, “as some last bit of good will,” which is not the behaviour of a hater.

The churn was not Anthropic’s alone. Galorious, asking whether Gemini via a Google subscription had improved, described why he had bailed on it before: the tooling “stalled 1/2 times and I cancelled.” A tool you abandon halfway through the job is not a subscription problem so much as a trust problem — the same reliability gap that keeps turning first-week enthusiasm into a cancellation.

It wasn’t only Anthropic

Anthropic drew the loudest thread because it shipped the biggest launch, but the week’s discontent was general. On Perplexity, Aurornis wrote the most complete indictment of the lot, and it is worth reading as a whole because it moves from product to billing to support. On quality: “Then they started optimizing for speed of responses over quality of results. I can enter a query and see my results appear in a second, but they’re garbage. The links and references it gives frequently don’t match the text right next to them.” On the meter: “Now there are reports of people being billed at the end of their trial period without warning, despite them saying that they will warn before this happens.” And on getting help: “alarmingly bad customer support screenshots where the customer support agent… refuse to help anyway. It takes escalating it on Twitter to get it corrected.” That is the full stack of a modern AI grievance in one comment.

On Grok, blazarquasar pointed at the small print rather than the outputs: “Grok has possibly the worst ToS of any of the AI providers,” quoting a clause in which a user grants “an irrevocable, perpetual, transferable, sublicensable, royalty-free, and worldwide right to SpaceXAI” over their inputs — extending, the terms add, to “a person’s image, likeness, voice, or other similar attributes.” Whether or not it is the very worst, it is a useful reminder that the thing you type is an input to someone else’s business, a theme we keep returning to in who owns the words that trained your AI.

And on Gemini, past the benchmark excitement, rjh29 gave the plain practitioner’s verdict: “I use Gemini a lot and it often replies with out-dated data. The more detailed the information you’re asking, the more likely it is to be wrong.” A model that gets less reliable exactly as your question gets more specific is a model that fails at the moment you most need it — the everyday face of the confident-error problem, not the catastrophic one.

To be fair: the defenders, and the case against the pile-on

A Voices piece that quoted only the aggrieved would be its own kind of dishonesty, and the same threads carried people who think the critics have lost the plot. llm_nerd conceded the annoyance and then flatly rejected the conclusion: yes, “the safeguards are ridiculous and obnoxious, though I can say that 5.1 greatly relaxes them,” but “Fable is extraordinarily useful. It is, far and away, the most powerful programming model, in my experience.” That is not a subtle disagreement; it is a different planet from “absolutely horrendous,” and both users are describing the same release.

The sharpest pushback came on the limits thread. Against a title framing a change as a cut, lostNFound did the arithmetic the outrage skipped: “getting a permanent 25% increase sounds good over a 50% temporal increase… A 25% permanent increase is good in my books,” adding that on a full working day of Fable High “it does my job, straight up.” Then he punctured the mood with the best line of the week: “I just got the memo that we’re in operation Complain and Destroy. This is horrible! I will now go buy my Torch&Pitchfork two-pack for 17% off.” It is a fair warning about how launch-week threads work: they over-sample the annoyed, because contented users are busy using the thing.

Even the sceptics were measured. disgruntledphd2, comparing models on identical prompts in Cursor, offered the deflationary observation that undercuts everyone’s favourite: “I find it hard to distinguish between the outputs of GPT/Claude/Kimi/GLM recently… the non-Claude models were better in many cases, which definitely doesn’t map to their pricing.” If the frontier models are converging and the cheaper ones sometimes win, then the loyalty that makes a launch-day letdown sting so much may itself be the thing worth re-examining.

What to take from the week

The throughline is not that any one model is bad. It is that “new” and “better” keep being sold as the same word, and this week a lot of paying users found they were not. A few durable lessons fall out of it:

• An upgrade is a hypothesis until you test it on your own work. Benchmarks and launch slides describe the model the vendor wants you to see; jorl17’s and ghoul2’s regressions only showed up in real tasks. Keep the previous version reachable while you check.
• Read the price against the receipt, not the slide. “25% cheaper” on cache reads and “15% more” per task can both be true. Judge a plan by what a real day of your work actually costs, in the first billing week, while you can still leave.
• The removed feature is the one to watch. Thinking traces, model labels, usage meters: the things quietly dropped in a “better” release are rarely in the headline and often the ones you relied on.
• Check the terms, not just the outputs. An irrevocable, perpetual licence over your inputs is a product decision too, and it does not improve with the model.
• Discount the pile-on, but not to zero. lostNFound is right that complaint threads over-sample the frustrated. He is not right that there is nothing there. The signal is in the specifics that repeat across users and machines.

The models are, on the whole, still improving; llm_nerd is not wrong that Fable can be remarkable, and the competition disgruntledphd2 describes is real and good for you. But an industry that ships regressions inside the word “upgrade,” trims the limits on the same day, and frames a price rise as a cut is training its most engaged customers to read every release note like a contract. That is what this week actually produced: not a scandal, but a room full of professionals, in noduerme’s phrase, discovering that other professionals feel the same way.