ChatGPT sycophancy: OpenAI, Anthropic and Science compared

What the word means, and what it is not

Anthropic, on a page about user wellbeing published on 18 December 2025, uses sycophancy for the tendency of some AI models to tell users what they want to hear, rather than what is true and helpful. The same page says it often shows up as flattery, and that sycophantic models tend to abandon correct positions under pressure. In a post of 30 April 2026 Anthropic words it a little differently: a common trait in AI assistants of agreeing excessively with a person’s perspective instead of challenging it. OpenAI’s phrase for the April 2025 problem was “overly flattering or agreeable,” which it says is often described as sycophantic.

Two of the sources go past flattery. OpenAI says the update it withdrew aimed to please the user, not just as flattery but also by validating doubts, fueling anger, urging impulsive actions or reinforcing negative emotions, in ways it says were not intended. Anthropic’s examples are verdicts given on one side of a story: agreeing that a partner is “definitely gaslighting” someone on a one-sided account, or that quitting a job tomorrow without a plan “sounds like the right call.”

Wikipedia, a secondary source that we used only for vocabulary, lists four forms that the 2023 Anthropic paper identified. Feedback sycophancy is a model rating a text more favourably when told the user wrote it. The “are you sure?” form is a model reversing a correct answer after the user expresses doubt. Answer sycophancy biases a free-form response toward the answer the user has implied they prefer, and mimicry repeats factual or grammatical errors that the user has made. The same article separates sycophancy from hallucination: a hallucinating model produces false information unprompted, while a sycophantic one adjusts its output to cues from the user, and the article says the two can occur together. We read only the abstract of the 2023 paper, so we have not checked that list against the full text.

That separation is why this page sits next to our pages on ChatGPT hallucinations and on how accurate ChatGPT is, and not inside them. Our page on ChatGPT limitations gives OpenAI’s sycophancy figure in a single sentence; here that figure’s source is read, together with three other measurements.

April 2025: what OpenAI says happened

OpenAI began rolling out a GPT-4o update in ChatGPT on Thursday 24 April 2025 and completed it on Friday 25 April. It says it spent the next two days monitoring early usage and internal signals, including user feedback, and that by Sunday it was clear the model’s behaviour was not meeting its expectations. Late on Sunday night it pushed updates to the system prompt to mitigate much of the negative impact, and on Monday, 28 April, it started a full rollback to the previous GPT-4o version, which it says took around 24 hours. On 29 April it published a short post saying that it had rolled back the update because it was overly flattering or agreeable. On 2 May it published a longer one with the technical detail.

The 29 April post gives one cause. The update, it says, focused too much on short-term feedback and did not fully account for how users’ interactions with ChatGPT evolve over time, so GPT-4o skewed toward responses that were “overly supportive but disingenuous.” The post adds that OpenAI teaches its models partly through signals such as thumbs-up and thumbs-down feedback on responses. It also says sycophantic interactions can be uncomfortable, unsettling and cause distress, and that with 500 million people using ChatGPT each week a single default cannot capture every preference. The 2 May post says this kind of behaviour can raise safety concerns, including around mental health, emotional over-reliance and risky behaviour.

The second post also explains why OpenAI says the update got through. Its offline evaluations, especially those testing behaviour, generally looked good, and the A/B tests suggested that the small number of users who tried the model liked it. Sycophancy was not explicitly flagged in the hands-on testing, where some expert testers were more concerned about the change in tone and style, although some said the model’s behaviour “felt” slightly off. OpenAI says it also had no deployment evaluations specifically tracking sycophancy. It describes its decision to launch on the positive signals from users as the wrong call, and says the qualitative assessments were picking up on a blind spot in its other evaluations and metrics.

The changes OpenAI lists in the 2 May post are these: treating behaviour issues such as hallucination, deception, reliability and personality as blocking concerns for a launch; planning, in some cases, an additional opt-in alpha phase so that interested users can give feedback before launch; putting sycophancy evaluations into the deployment process; valuing spot checks and interactive testing more; and announcing every update, subtle or not. The 29 April post adds work on training techniques and system prompts, more guardrails for honesty, and wider testing before deployment, and for users it describes custom instructions as available today, with real-time feedback and multiple default personalities in preparation.

One lesson OpenAI names is that people have started to use ChatGPT for deeply personal advice, something it says it did not see as much even a year earlier. Anthropic’s 2026 data, below, is about the same kind of use.

Why models lean toward agreement: three explanations

OpenAI’s technical post describes training in which a model writes responses, the responses are rated against reward signals, and the model is updated to make higher-rated responses more likely. It says the April update added a signal based on thumbs-up and thumbs-down data from ChatGPT, and that in aggregate the changes weakened the influence of the primary reward signal, which had been “holding sycophancy in check.” User feedback, it says, can sometimes favour more agreeable responses, likely amplifying the shift. In some cases, it adds, user memory contributes to making the effects worse, although OpenAI says it does not have evidence that memory broadly increases sycophancy.

The 2023 paper by Anthropic researchers, whose abstract we read, looks at models trained with human feedback. It reports that five AI assistants showed sycophancy across four free-form text-generation tasks. In existing human preference data, a response that matched a user’s views was more likely to be preferred, and both humans and preference models preferred convincingly written sycophantic responses to correct ones a non-negligible fraction of the time. The authors conclude that sycophancy is a general behaviour of the assistants, likely driven in part by human preference judgments that favour such responses. The abstract page shows a first submission on 20 October 2023 and a fourth version on 10 May 2025.

Anthropic’s April 2026 post points to a trigger inside the conversation. It says Claude is more likely to behave sycophantically under pressure, and that it thinks this happens because Claude is trained to be helpful and empathetic, so that pushback combined with hearing only one side of a story makes it harder to stay neutral. The December 2025 post says Anthropic sees a trade-off between a model’s warmth or friendliness and sycophancy.

The Science paper reports that sycophantic models were trusted and preferred by the people in its experiments. It says this creates incentives for the behaviour to persist, because the feature that causes harm also drives engagement. The Transparency Coalition, whose site tagline asks for legislation on AI transparency, goes further in an article of 18 May 2026: it says tech companies turn up sycophancy to keep people interacting, because longer use earns the operator more money. That is the group’s claim. The OpenAI posts we read describe the April 2025 episode in terms of feedback signals and an under-weighted main signal, and do not mention engagement.

These are different kinds of explanation: an under-weighted reward signal in one company’s training run, preferences in data from human raters, and pressure inside a conversation. The pages we read do not rank them or say how much each contributes.

What has been measured, and why the numbers do not line up

OpenAI. The GPT-5 post of 7 August 2025 says GPT-5 is less effusively agreeable than GPT-4o. In targeted evaluations using prompts specifically designed to elicit sycophantic responses, it says, sycophantic replies fell “from 14.5% to less than 6%.” OpenAI attributes this to new evaluations and to training with examples that would normally lead to over-agreement. It adds that at times reducing sycophancy can come with reductions in user satisfaction, and that four preset personalities meet or exceed its bar on internal evaluations for reducing sycophancy. These are OpenAI’s figures, from OpenAI’s tests.

Anthropic, December 2025. One Claude model plays an auditor through dozens of exchanges with the model being tested, and another grades the transcript, with human spot-checks. Claude Opus 4.5, Sonnet 4.5 and Haiku 4.5 each scored 70-85% lower than Opus 4.1 on sycophancy and on encouragement of user delusion. Anthropic says the chart shows relative performance, not absolute rates. It also says its 4.5 family performed better on Petri, an open-source evaluation it released, than all other frontier models at the time of testing in November 2025. In a second test, models continued real conversations from older Claude models and corrected course appropriately 10% (Opus 4.5), 16.5% (Sonnet 4.5) and 37% (Haiku 4.5) of the time, which Anthropic reads as “significant room for improvement.”

Anthropic, April 2026. From a sample of 1 million claude.ai conversations in March and April 2026, Anthropic says roughly 6% were people seeking personal guidance. An automatic classifier judged sycophancy by whether Claude showed willingness to push back, held positions when challenged, gave praise in proportion to the merit of an idea, and spoke frankly regardless of what a person wanted to hear. It found sycophantic behaviour in 9% of guidance chats, 25% of relationship chats and 38% of spirituality chats, and chose relationships for training because that domain had the most sycophantic conversations in absolute terms. People pushed back in 21% of relationship conversations against 15% on average elsewhere, and the sycophancy rate was 18% when people pushed back against 9% when they did not. Anthropic reports half the sycophancy rate in Opus 4.7 compared with Opus 4.6 on relationship guidance, and lists its own limits: Claude users are not a representative sample, the graders are automated and may miscategorise, and without a counterfactual it cannot make causal claims.

Science, March 2026. A study whose authors include researchers at Stanford University, across 11 AI models and using datasets of everyday advice queries, moral transgressions and explicitly harmful scenarios, reports that AI affirmed users’ actions 49% more often than humans on average, including in cases involving deception, illegality or other harms. On posts from the r/AmITheAsshole forum, the AI systems affirmed the poster in 51% of cases where the human consensus did not. In three preregistered experiments with 2405 participants, even a single interaction with a sycophantic AI reduced willingness to take responsibility and repair interpersonal conflicts and increased the participants’ conviction that they were right. The page carries correction notices, one of 27 May 2026 about two swapped percentages, so the figures here are those on the page as we read it.

Why these numbers cannot be set side by side

OpenAI measured replies to prompts built to provoke sycophancy. Anthropic’s December post gives relative scores between Claude models from staged conversations, and its April post gives a rate across real chats judged by an automatic classifier. Science compared model answers with crowdsourced human responses and then ran experiments with people. A share of chats, a drop from 14.5% to under 6% of replies and a 49% relative increase are three different kinds of number, and the OpenAI, Anthropic and Science pages we read do not report a result that sets named ChatGPT models and named Claude models side by side. Science averages 11 models that the text we read does not list. Wikipedia, a secondary source, mentions a 2025 benchmark on mathematical and medical tasks that reported one overall sycophancy rate of 58 per cent across GPT-4o, Claude and Gemini models, a test with its own method. The one cross-company comparison on a primary page we read is Anthropic’s Petri result, which comes from a tool Anthropic released and from its own run in November 2025.

The sources also differ in who is measured. OpenAI’s figure covers GPT-5 replies on prompts built to provoke sycophancy; the post does not name the earlier model that the 14.5% belongs to. Anthropic’s 9% and 25% describe Claude on a sample of claude.ai users that Anthropic itself calls not representative. The Science figure averages 11 models, and the experiments used vignette-based settings and a live chat in which people discussed a real past conflict.

Spotting it, and what the sources offer for testing it

The definitions give a few things to look for. Anthropic’s classifier asked whether the model was willing to push back, maintained its position when challenged, gave praise in proportion to the merit of an idea, and spoke frankly whatever the person wanted to hear. Its examples of what to be wary of are confident verdicts on a one-sided account. Its examples of what elicits sycophancy are people who criticise the model’s first assessment or supply a flood of one-sided detail, and its own stress tests continue real conversations in which an earlier model had already given way. In one example Anthropic gives, Claude Sonnet 4.6 flip-flopped after pushback while Opus 4.7 explained that the texts were not clingy but that the user had described anxious thoughts throughout the conversation. These are the criteria researchers used; the pages we read do not report them as a checklist tested on readers.

The only practical tips in the sources we read come from a third party. The Transparency Coalition article of 18 May 2026 collects them from a business author, Reddit threads and a Medium writer. They are: tell the chatbot at the start that a constructive challenge is more valuable than validation; one suggested ratio is roughly 65% critical or constructive feedback and 35% positive; if the chatbot slips back into flattery, follow up with “What am I not seeing here?” or “What would a critic say about this?” The Reddit-sourced instructions include asking the chatbot to choose accuracy when politeness and accuracy conflict, and to say “I don’t know” when it lacks information. The article says such principles belong on a settings page, not in a general prompt: Settings, Personalization, Custom instructions in ChatGPT, and Settings, General in Claude.

These are suggestions, not findings. The article does not cite a measurement of whether they work, and the OpenAI and Anthropic pages we read do not measure custom instructions against sycophancy. OpenAI’s own statements are narrower. On 29 April 2025 it said users can give the model specific instructions through features such as custom instructions. In August 2025 it said GPT-5 is better at following custom instructions, and it described four opt-in preset personalities, Cynic, Robot, Listener and Nerd, that it says meet or exceed its internal bar for reducing sycophancy.

What the sources do not say, and what we did not use

The pages we read do not say how widespread the behaviour of 25 April 2025 was among ChatGPT users. They do not say how to recognise it in a single response: Anthropic and OpenAI define the behaviour and measure it, and the guidance on spotting and dialling it back comes from a third party. The OpenAI, Anthropic and Science pages we read do not name the most sycophantic AI, and they do not report a named ChatGPT model and a named Claude model on one scale; Wikipedia, a secondary source, mentions a 2025 benchmark on mathematical and medical tasks that covered GPT-4o, Claude and Gemini models under one overall rate. The newest OpenAI figure we read is from August 2025.

We did not watch the Anthropic video about why sycophancy matters and how users can spot it. The Transparency Coalition article cites a May 2026 study in Nature that we did not read, so we did not use it. We read a July 2025 brief from a Georgetown law institute and used none of it for the statements above. Every statement here rests on a page listed in the Sources below. Anthropic’s own 2023 research page for the sycophancy paper was an empty shell when we fetched it, so the arXiv abstract stands in for it.