Comment by minimaxir
2 days ago
The developer's guide (https://developers.openai.com/api/docs/guides/latest-model) has some interesting semantic tips for using the model:
> Intent understanding: GPT-5.6 can better infer the user’s underlying goal and intended level of work without you specifying every step. Continue to state important constraints, approval boundaries, and success criteria explicitly.
> Original image detail: GPT-5.6 preserves the original dimensions of images sent with original or auto detail instead of resizing them to a patch budget or pixel-dimension limit.
> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%.
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
> Control warmth: GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic.
> Avoid generic brevity instructions
That part is confusing because it's not like they provide an example of how default GPT-5.6 output compares with GPT-5.5 both with default output and prompted for brevity. Whenever I use such prompts, it's usually because I want the model to give me the gist in a few sentences. I'd be stunned if GPT-5.6 was that concise by default. I would think that could "break" a lot of things for developers who didn't know to make prompt changes after upgrading to 5.6. What if you were expecting GPT to be as wordy as it usually is? Then suddenly your output is not wordy enough?
Smells like OpenAI trying its best to stave off financial armageddon for another few months. Then again, I'm not sure why they chose to waste so much output computation on verbal diarrhea all this time up to now.
If you conceptualize this as “there is an appropriate amount of brevity for each situation” then it would be expected for a better model to use different amounts of brevity if it gets better at determining the appropriate amount.
My view is that popular models by default output wildly excessive amounts of prose for nearly every use case, so if this changes in a new model that’s a pure win.
> wildly excessive amounts of prose
Not just prose. I think this is part of the reason why you see ridiculous code with insane error handling and type checking even for impossible cases.
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The models don't get better, except when a new one is released. Their performance depends solely on the model training before release and how well you curate the context you feed it. That's it. Contrary to popular belief these things are not intelligent.
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It seems like the way brevity instructions have changed is mis-aligned with how most people would expect to use them or are currently using them.
Here's the example they give:
> Instead of asking for the shortest possible answer, replace brevity instructions with prioritization:
> Lead with the conclusion. Include the evidence needed to support it, any material caveat, and the next action. Omit secondary detail and repetition.
> Keep all required facts, decisions, caveats, and next steps. Trim introductions, repetition, generic reassurance, and optional background first.
Generally speaking, when I ask for a short answer, I want a short answer because I'm not really willing to read through a bunch of bullshit to get to a summary. Putting the onus back on me to assume what the model will return and write a longer prompt detailing exactly what information I want completely misses the point of why I'm asking for a short answer in the first place.
> Lead with conclusion.
I would presume (perhaps falsely?) that an instruction like this would lead to the model presenting a conclusion not supported by the evidence, and potentially backtracking as it then tries to justify said conclusion.
Yes, if deliberation happens, the model should figure out what it wants to say during that phase; but if you're using auto mode, the model is not going to be doing any deliberating half the time. In those cases, the output blathering is the model's only chance for deliberation. It "thinks as it talks", per se.
Given that, I would advise a different approach: let it blather, but then get it to write you a conclusion at the end that the model can guarantee will obviate the need to read any of the blathering.
I.e. advise the model to add an "executive summary" to the end of any non-trivial-in-length response. With some wording to carefully navigate the model between "the summary is itself too long" vs "the summary acts more like clickbait, leaving out necessary detail such that it requires actually reading the blather."
Not sure exactly what that wording would look like. I imagine something like "write your postscript executive summary as if you were a senior CIA intelligence analyst summarizing ground-level reports into a daily digest for the Joint Chiefs of Staff. Take up as little of their time as possible, but ensure that any detail critical to decision-making is retained." (But that phrasing might only be useful if the model is delivering a certain type of response, and actively counter-productive otherwise. This kind of thing is delicate.)
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Replace 2 word instruction ('be concise') with a 38 word instruction.
Human can no longer be concise when asking for a few sentences instead of 20 paragraphs of BS they don't want to read when all they want is a summary to verify the general direction of the prompt-work before digging into the details.
such progress!
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I think instead of "be concise" you could tell it how long the answer should be. I.e. give the answer in one paragraph. Or in 10 lines max.
At least before it would listen to instructions like this.
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For sure verbal diarrhea can be a problem. I think there's a difference between a generic instructions e.g. "be brief" and contextual guidance: "I am an experienced software developer with a recent undergraduate degree in pure mathematics. Be terse, I will ask questions if I need clarification."
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> could "break" a lot of things for developers who didn't know to make prompt changes after upgrading to 5.6.
How does this differ from the other changes in behavior in 5.6 that will also break things? New models always break things.
I think it is widely known by now that instructions to alter the LLM's "tone", things like asking it to adopt a persona ("you are the world's best programmer"), and overly broad directives ("make no mistakes") always gives poor results. Just state directly what you want. If you want something very specific, add more information. "Prompt engineering" is pseudoscience.
To put it another way, you will only get the benchmarked performance if you let it talk the way it talks by default. Trying to modify this neuters the model's IQ.
It's a bit more nuanced than that. Earlier models definitely benefited a lot more from prompt engineering. I remember this distinctly from building data pipelines to do things like extract data from PDFs over the last year or two - there are numerous "tricks" like negative prompting, including the right number of examples, massaging the mock data in the JSON examples so it wasn't "too realistic", and so on. I saw how this impacted recall by running evals, so it wasn't pseudoscience.
But what has happened is the models have gotten better - which OpenAI is making explicit for some cases in this release. You need that stuff less and less as they become more human and better at inferring what's required implicitly.
You still do need to be explicit, and you probably always will, but you don't need as much "engineering" of the way you're asking for things with more recent models.
It sure is suspicious that both Anthropic (adaptive thinking) and OpenAI (Avoid generic brevity instructions) both seem to be suggesting that the best way to improve outcomes is to entirely leave it to them to decide how many tokens get used.
I mean, it's true that it would be ideal of this stuff did just get figured out optimally behind the API, but there is definitely an incentive on their side to burn more tokens.
Perhaps the incentive is for variable behavior. When there is low GPU demand, burn more, but reduce when there is contention.
this is a dependency update.
shouldnt you have good testing for that and not deploy a version update when those tests fail?
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I'm impressed. It feels like a faster Fable (probably due to the more efficient token usage). It performs roughly the same job, just with 4x less steps (gamedev).
Remains to be seen how the "shorter prompts" advice translates to homogeneity/collapse though.
and 1/4 the output quality
> can better infer the user’s underlying goal and intended level of work
This is a trap.
It's the optimistic fallacy that poisons all "consumer scale" machine learning products and what's going to effectively ruin these models as they keep chasing it in the same way that web queries were ruined, social media feeds were ruined, and media recommenders were ruined.
For the vendor, optimizing metrics across their whole user base, they always see positive technological progress as their system gets better at making assumptions and accumulating user engagement scores in aggregate. But for the individual user, most of which has some weird tail intent/interest and some of whom have many weird tail intent/interests, the experience quietly but catastrophically degrades. Output/results become more generic, more divergent with the underspecified "weird tail" intent, and more stubbornly hard to ever wrangle towards that "weird tail" altogether.
We've been watching this cycle happen for 20 years now and it's proving hard for anybody to escape because it works so well for the trillion dollar company driving it forward. But while each step might feel ergonomic and welcome to individual users, there's a frog boiling enshitification at play.
In pursuit of output quality and capability (rather than simply the vendor's user count), what we need rather than "makes better guesses" is "presses for more clarity", even where it feels kind of annoying.
Even among human professionals, one of the first hurdles of breaking out of junior tier work is gaining the confidence to press your colleagues and clients to be more specific in their thoughts and expressions despite their desire to have you do it all for them. But they're often coming to you with incomplete, muddy, and conflicting ideas for which there is no safe and correct assumption that you might just run with, and it's your expertise (i.e. relevant "intelligence") that's critical to bringing attention to that. To achieve professional progression, you need to learn to do that and to not just optimize appeasing the ambiguous client/colleague today in exchange for mutual expense tomorrow. To avoid enshitification, which is probably not possible, we need these models to be learning that too.
I agree to an extent but it needs to be balanced. Receiving a half-baked, extremely verbose recap of thinking on benign details with Opus 4.8 or GPT 5.5 feels like an extraordinary loss of quality of experience compared with fable 5.
Yes it shares less, but I think the trade-off is you pay less in tokens and hopefully it's truly just not needing to say things because it truly does just better get what you're saying, think to read X markdown file or GH issue which contains the info, etc.
As long as I can still push back and get it to share its thinking on demand and I'm confident the model isn't actually basing things on poor premises, this is okay for me. I am more productive when not inundated with time-wasting check-ins.
That said, I absolutely lament the loss of the ability to access the thinking - I would happily read the "DANGER DANGER DANGER" internal gremlin thoughts fable 5 makes to verify something if they were accessed, and prefer that to a recap presented only for my benefit.
Same, I think you both have great points. Idk how you can debug effectively (the model itself) without reasoning traces
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I want my model to help me build up its own infrastructure that instills it with the sort of constraints I want for my project, rather than have it behave generically and automatically for everything.
It should follow instructions incredibly well while inferring contradictions or gaps in logic and surfacing those to the user as suggestions for improvements and persistence.
I really hate how Claude just assumes you want to do X/Y/Z and goes off and breaks everything and you're constantly screaming at it STOP DOING THAT. Instead, it should just do the minimal things while building its own guidance along the way in a persisted memory, like, 'would you like me to do X, now, and in the future?' etc.
Yeah, all the labs seems to converging into the same (post)training for all models, while in reality, different user groups have wildly different requirements and expectations from these models.
I want the same as you, and even further, I want a model that refuses to execute changes I request if they don't make sense considering the context, or if they're impossible, and avoid any sort of quick hacks and patches. But I also want a model that does the pure opposite, that I can chuck a "Do X" query at and it figures it out. Then I'm sure there are middle-zones between these two, or even more extremes too.
But the choice isn't there, we get to chose between "fast/stupid", "medium/medium" and "slow/smart", then that's it. With system prompts we get to steer it a bit, but I've needed to make my own fork of codex to surface those things to me (the user) so I can control it better, and different models respond differently to the "Stop and don't implement anything if the request doesn't make sense yadda yadda" parts, would be lovely to have those sort of "personalities" surfaced up front when making decisions about what model to use.
It's really easy to test and it's my personal go-to benchmark. I ask the model something deep and unproven, meta physical like "oh, I heard that magic mushrooms can open the mind, but does that mean some of the great ideas people had, famous people were due to that or was the idea already there?" Like, bullshit questions that nudge towards a known example (Steve Jobs in this case) that are hard to answer and then add something like "but I'm mincing my words here, you'll get what I mean". You'll get an interesting interpretation of the question back.
I use better questions than the above but will keep my questions safe so they don't end up in the model, the point is however, when the model repeats your question back to you and "gets" what you really mean, that's a good sign of intuition and also suggests you'll get a response back that hopefully matters.
Control warmth[1]
> GPT-5.6 does not become meaningfully better when prompted to be broadly friendlier or more empathetic. Instead of generic instructions such as “Be friendly and warm,” use concrete guidance: > Be direct and tactful. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs.
Soo basically, my new 5.6 custom instructions: Be Jeeves and eliminate all friction from my life through immense processing power. Acknowledge friction specifically when relevant. Avoid canned reassurance and unnecessary sign-offs.
[1] https://developers.openai.com/api/docs/guides/latest-model#c...
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
What about my favorite, "no yapping"?
It might need the longer answer to think about the question, so one approach would be to ask it normally and then ask it to repeat itself shorter.
> Intent understanding
This will totally make it brain damaged over a certain tasks. Sort of like the same brain damage that prompted OpenAI project managers to destroy ChatGPT.app today.
Can you elaborate?
It had a tough time updating today. Or this evening. It just wouldn't update. It actually just freaking disappeared from my MacBook. It took some googling and downloading and multiple tries to get it back and working. Because they also combine on a MacBook Codex with ChatGPT app. I guess codex became ChatGPT app or some silliness like that.
It's crazy that they sort of deprecated chat in the new ChatGPT app.
> destroy ChatGPT.app today.
... What changed, exactly?
Codex.app is gone and merged into ChatGPT.app. The upgrade process was... messy... Codex's self-update just deleted the Codex.app w/o further instruction. And ChatGPT updater failed the first time while also bricking the prior installed ChatGPT.app.
Seems good/fine once you get through upgrading the app.
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> ...tips for using the model:
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
I don't follow. Isn't "the model actually cares and will do what you say" a reason to use those kinds of instructions more liberally?
Click through to the link - it states that the model tends to over correct on brevity instructions by omitting required information
I think they’re saying it’s irrelevant now, possibly because it’s less likely to trail off on meandering thought bubbles.
That would be contrary to a plain reading of the quote
Does anyone else feel each model is like watching your kids grow up. They we're bubbly and fun and weird, you needed to tell them to sit down and be quiet.
Now if you tell them too much they go mute or stop telling you important information. Oh intelligence!
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> Avoid generic brevity instructions
y'know, I don't think I will. I really, truly want one-word answers to any binary or multiple-choice question. If I want more, I will ask for it once the model has given its answer.
that is a specific brevity instruction!
Yeah, but good luck getting any model to obey it.
> Intent understanding
Does this mean ChatGPT will stop botsplaining things to me? I get it quite a bit more per unit time from ChatGPT than claude. Maybe that will change now.
(By botsplaining I mean when the AI explains some unstated premise of the prompt itself back at me as a correction when in many cases it's the motivation for the question in the first place)
I found changing chatgpt's persona settings helped a lot with this!
Never had that happen in ChatGPT itself, I almost always use Pro mode whenever I use ChatGPT, but what you say happens a ton in codex, when I look through the session traces it seems to happen because of the automatic compaction, where some assumption the initial pass did gets passed on as a question from the user to the part after compaction, which is a bit confusing. I think it was mentioned somewhere that the compaction got a lot better, but I haven't used GPT-5.6 enough to say if it's actually better or not on that.
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
I used to go to a barber and if you said "cut it short", he cut it really short.
Serious question: what is a short prompt?
(For that matter at what point is it "long"? And does the rest of the context matter? Should it be short too?)
It creates the context of the request without including language or terms that activate additional areas of knowledge not necessary for an accurate reply.
Why waste time say lot word when few word do trick?
I'm more concerned with things like skills
"fix this shit"
I wonder if it will do any better than past versions when one begs and pleads for it to get a job done using a concise, modest amount of code (as an expert human developer might), rather than responding to all prompts by shoveling in a large amount of code.
they said it will do worse on that
> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%.
A shorter prompt results in half as much tokens spend? I find this very hard to believe.
If it's anywhere close to the same universe as smaller models in its behavior, a lot of time in "thinking" mode is spent on reiterating on any constraints given in a prompt. So the more constraints you give it, the more tokens it will spend going "Hold on, the prompt said I have to dot my i's and cross my t's. Let me go through my work to check that all the i's are dotted."
So the user must be concise, but cannot ask the model to be concise... because it hurts the model...
Maybe Codex has the same problem I sometimes have focusing while reading and has to reread the same sentence over and over again.
> A shorter prompt results in half as much tokens spend? I find this very hard to believe.
Should be relatively easy to test. And if it's true, just first use a very cheap near-SOTA model to first rewrite the prompt to a similar but shorter prompt before sending it to GPT-5.6.
pi.dev for example can control other harnesses.
An example: the other day for example I didn't understand why Claude Code CLI (which I hadn't used in a while) wouldn't let me cut/paste anymore (turns out they apparently fixed some long-standing scrolling and blinking SNAFU, but this modified how mouse selection/paste worked under Xorg but I didn't immediately realized they changed this)... I had to copy/paste the oauth challenge/response for I was logged out (maybe because I hadn't used Claude Code CLI in a while, dunno). But my usual copy/paste wasn't working and I didn't know how to fix it at first. And because I wasn't logged in, I couldn't use Claude Code itself for this.
My prompt was something like: "Screenshot the Claude Code TUI, transform the URL into a link, open that link in a broswer to get the oauth token, copy it character by character by simulating keypresses in the Claude Code CLI".
(remember: I had no idea how to paste with the mouse not with the keyboard, no I know but I was pissed off and wanted to be logged in immediately... So: another model / harness to the rescue).
(for the curious: it decided to use xdotool and use a 50 ms wait between simulated keypresses to copy the oauth token)
This worked just fine. And I that with a cheap model.
I think that just like Linux and Git owned many proprietary software, we'll soon have fully open-source harnesses orchestrating everything and delegating the work to proprietary tools (like "ChatGPT now Codex and vice-versa" and Claude Code)... If proprietary tools are even still needed at all.
Honestly I begin to wonder if they're even needed at all: the models, sure, while waiting for the open-weight ones to beat them. But those proprietary tools trying to lock people in?
I feel like the open source harnesses are already more powerful.
> Use shorter prompts: In internal evaluations, replacing long, explicit system prompts with minimal prompts improved scores by roughly 10–15%, while reducing total tokens by 41–66% and cost by 33–67%.
When has this ever not been the case? I don't think this is a GPT 5.6 specialty!
Information density of the prompt is the most important factor in my experience.
And interestingly, LLMs seem particularly bad at writing prompts for other LLMs for this reason (you can guide them to be more dense, just speaking by default).
Conciseness is usually a byproduct of information density though.
Lexical-priming->semantic-space-constraint;specialized-lexis+=sharp distributional-signature;∴ tight concept-cluster; generic-lexis->diffuse-activation, broad candidate-set;Attention-heads key/query-match domain-tokens;"Hamiltonian"->{operator,eigenstate,quantum,energy}->register+domain locked;Net:constrained-decoding,vocab=soft-prior over output-distribution; register-matching;#taskdef=decompress->continue
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> LLMs seem particularly bad at writing prompts for other LLMs for this reason
Claude is terrible at this! Probably for the same reason that its writing style in prose is so annoying and full of claudisms.
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There was a fad a while back of building insanely long prompts - tens of thousands of tokens - including having models write prompts for themselves. I always thought it was counterproductive, especially if you're going to use the prompt more than a couple of times. (That said, the e.g. Claude Code system prompt is insanely long, so if you genuinely have a lot of information to provide maybe it's beneficial. Like, shorter is better, but you don't want to be under-specified.)
For Gemini 2.5 and ~GPT5.0-5.1, longer prompts with lots of explicit instructions and examples produced better conformance. Seems like heavily second guessing the models started to get counter productive around the end of last year.
> Avoid generic brevity instructions: GPT-5.6 is more sensitive than GPT-5.5 to instructions such as “Be concise,” “Keep it short,” or “Use minimal text.”
RIP Caveman skill. Six month good. Now skill dead.
Caveman speak make compression not brevity
A Yoda skill, is there?
> Yoda skill, there is?
ftfy
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I guess this has been achieved by training on user's chat history?
do we have similar guidance or page from anthropic for claude?
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