Developers are choosing older AI models

https://www.augmentcode.com/blog/developers-are-choosing-older-ai-models-and-16b-tokens-of-data-explain-why

by knes • 9 months ago

183 176 9 months ago

Manfred• 9 months ago

It could be an interesting data point, but without correcting for absolute usage figures and their customers it's kind of hard to make general statements.

KronisLV3• 9 months ago

For development use cases, I switched to Sonnet 4.5 and haven't looked back. I mean, sure, sometimes I also use GPT-5 (and mini) and Gemini 2.5 Pro (and Flash), and also Cerebras Code just switched to providing GLM 4.6 instead of the previous Qwen3 Coder so those as well, but in general the frontier models are pretty good for development and I wouldn't have much reason to use something like Sonnet 4 or 3.7 or whatever.

JanSt2• 9 months ago

I have canceled my Claude Max subscription because Sonnet 4.5 is just too unreliable. For the rest of the month I'm using Opus 4.1 which is much better but seems to have much lower usage limits than before Sonnet 4.5 was released. When I hit 4.1 Opus limits I'm using Codex. I will probably go through with the Codex pro subscription.

CuriouslyC2• 9 months ago

Definitely do it. You get a lot of deep research, access to GPT5 Pro, Sora and the Codex limits are MUCH higher.

mccoyb• 9 months ago

Sonnet 4.5 is way worse than Opus 4.1 -- it's incredible that they claim it's their best coding model.

It's obvious if you've used the two models for any sort of complicated work.

Codex with GPT-5 codex (high thinking) is better than both by a long shot, but takes longer to work. I've fully switched to Codex, and I used Claude Code for the past ~4 months as a daily driver for various things.

I only reach for Sonnet now if Codex gets cagey about writing code -- then I let Sonnet rush ahead, and have Codex align the code with my overall plan.

virtualritz2• 9 months ago

[...] I'm using Opus 4.1 which is much better but seems to have much lower usage limits than before Sonnet 4.5 was released [...]

Yes, it's down from 40h/week to 3-5h/week on Max plan, effectively. A real bummer. See my comment here [1] regarding [2].

[1] https://news.ycombinator.com/item?id=45604301

[2] https://github.com/anthropics/claude-code/issues/8449

prophesi• 9 months ago

Opus 4.1 is better, but imo not 5 to 6 times the price better.

kristianp• 9 months ago

What tool are you using to enable switching between so many models?

RamtinJ95• 9 months ago

Something like opencode probably, that’s what I have been using to freely and very easily switch between models and keep all my same workflows. It’s phenomenal really

KronisLV3• 9 months ago

For local chat Jan seems okay, or OpenWebUI for something hosted. For IDE integrations some people enjoy Cline a bunch but RooCode also allows you to have multiple roles (like ask/code/debug/architect with different permissions e.g. no file changes with ask) and also preconfigured profiles for the various providers and models, so you can switch with a dropdown, even in the middle of a chat. There’s also an Orchestrator mode so I can use something smart for splitting up tasks into smaller chunks and a dumber but cheaper model for the execution. Aside from that, most of the APIs there seem OpenAI conformant so switching isn’t that conceptually difficult. Also if you wanna try a lot of different models you can try OpenRouter.

lolive• 9 months ago

Isn't Continue supposed to help you do that, in VSCode? https://marketplace.visualstudio.com/items?itemName=Continue...

dhumph• 9 months ago

Cline & Open router

kanzure• 9 months ago

You can also switch between models with aider https://aider.chat/

leoalho• 9 months ago

Octofy (https://octofy.ai), it also allows answering with multiple models for one prompt.

neurostimulant• 9 months ago

Zed is actually pretty good at this.

thw_9a83c• 9 months ago

For development use cases, it's best to use multiple models anyway. E.g. my favorite model is the Gemini 2.5 Pro, but there are certain cases where Qwen3 Coder gives much better results. (Gemini likes to overthink.) It's like having a team of competent developers provide their opinions. For important parts (security, efficiency, APIs), it's always good to get opinions from different sources.

ojosilva2• 9 months ago

Yeah, I'm just going through the Cerebras migration at the moment.

It's a shame Cerebras completely dropped Qwen3 Coder's fast tool calling, short and instant responses, and better speed overall for GLM 4.6 thinking. Qwen3 is really good at hitting the tools first, then coming up with a well-grounded answer based on reality. Sometimes it's good when a model is Socratic: just knows it knows nothing.

GLM 4.6 on the other hand is more self-sufficient and if it sees it, and knows it, it thinks and thinks and finally just fixes it in one or two shots, so when you hit the jackpot, it probably an improvement over Q3C. But when it does not get it right, it digs itself into a hole larger than the Olympus Mons.

KronisLV3• 9 months ago

Qwen3 is really good at hitting the tools first, then coming up with a well-grounded answer based on reality.

I don't know, I had a lot of issues with Qwen models when it comes to RooCode/Cline - failed edits (albeit with a requirement for 100% precision, since I don't want the wrong lines to be replaced) or calling tools without parameters (e.g. list_files without path) and also stuff like using wrong path separators or using the wrong commands for the shell that's available (e.g. cmd when Git Bash is the shell).

GLM 4.6 seems better in that regard so far, maybe the coming weeks and months will show that better.

rcarmo• 9 months ago

I think this is somewhat disingenuous since not everyone uses the latest thing, and people tend to stick to “what works” for them.

Models are picky enough about prompting styles that changing to a new model every week/month becomes an added chunk of cognitive overload, testing and experimentation, plus even in developer tooling there have been minor grating changes in API invocations and use of parameters like temperature (I have a fairly low-level wrapper for OpenAI, and I had to tweak the JSON handling for GPT-5).

Also, there are just too many variations in API endpoints, providers, etc. We don’t really have a uniform standard. Since I don’t use “just” OpenAI, every single tool I try out requires me to jump through a bunch of hoops to grab a new API key, specify an endpoint, etc.—and it just gets worse if you use a non-mainstream AI endpoint.

rafaelmn• 9 months ago

I think this is somewhat disingenuous since not everyone uses the latest thing, and people tend to stick to “what works” for them.

They say that the number of users on Claude 4.5 spiked and then a significant number of users reverted to 4.0 with the trend going up, and they are talking about their usage metrics. So I don't get how your comment is relevant to the article ?

dotancohen• 9 months ago

His comment is relevant to the headline. You must be new here.

gptfiveslow• 9 months ago

GPT5 is HELLISHLY slow. That's all there is to it.

It loves doing a whole bunch of reasoning steps and prolaim how mucf of a very good job it did clearing up its own todo steps and all that mumbo jumbo, but at the end of the day, I only asked it a small piece of information about nginx try_files that even GPT3 could answer instantly.

Maybe before you make reasoning models that go on funny little sidequests wher they multiply numbers by 0 a couple of times, make it so its good at identfying the length of a task. ntil then, I'll ask little bro and advance only if necessity arrives. And if it ends up gathering dust, well... yeah.

Tepix2• 9 months ago

The article(§) talks about going from Sonnet 4.5 back to Sonnet 4.0.

(§) You know that it's a hyperlink, do you? /s

EagnaIonat2• 9 months ago

It loves doing a whole bunch of reasoning steps

If you are talking about local models, you can switch that off. The reasoning is a common technique now to improve the accuracy of the output where the question is more complex.

rho43• 9 months ago

This. Speed determines whether I (like to) use a piece of software.

Imagine waiting for a minute until Google spits out the first 10 results.

My prediction: All AI models of the future will give an immediate result, with more and more innovation in mechanisms and UX to drill down further on request.

Edit: After reading my reply I realize that this is also true for interactions with other people. I like interacting with people who give me a 1 sentence response to my question, and only start elaborating and going on tangents and down rabbit holes upon request.

confirmmesenpai2• 9 months ago

Imagine waiting for a minute until Google spits out the first 10 results.

what if the instantaneous responses make you waste 10 min realizing they were not what you searched for?

philipwhiuk• 9 months ago

All AI models of the future will give an immediate result, with more and more innovation in mechanisms and UX to drill down further on request.

I doubt it. In fact I would predict the speed/detail trade-off continues to diverge.

unparagoned• 9 months ago

Grok fast is fast but doing a lot of stupid stuff fast actually ends up being slower

geldedus2• 9 months ago

Only Codex is slow. GPT 5 classic is fast

s1mplicissimus5• 9 months ago

Seems to completely ignore usage of local/free models as well as anything but Sonnet/ChatGPT. So my confidence in the good faith of the author is... heavily restricted.

pistoriusp• 9 months ago

Do you use a local/ free model?

busymom02• 9 months ago

I am currently using a local model qwen3:8b running on a 2020 (2018 intel chip) Mac mini for classifying news headlines and it's working decently well for my task. Each headline takes about 2-3 seconds but is pretty accurate. Uses about 5.3 gigs of ram.

s1mplicissimus5• 9 months ago

Yes, for the little it's good I'm currently using LMStudio with varying models

NumerousProcess• 9 months ago

Augment doesn't support local models or anything else other than Claude/GPT

nicce• 9 months ago

Most people can’t affort the GPUs for local models if you want to get close to cloud capabilities.

Tepix2• 9 months ago

Isn't the point that you don't need SOTA capabilities all the time?

s1mplicissimus5• 9 months ago

Most people I know can't afford to leak business insider information to 3rd party SaaS providers, so it's unfortunately not really an option.

rhdunn• 9 months ago

A 4090 has 24GB of VRAM allowing you to run a 22B model entirely in memory at FP8 and 24B models at Q6_K (~19GB).

A 5090 has 32GB of VRAM allowing you to run a 32B model in memory at Q6_K.

You can run larger models by splitting the GPU layers that are run in VRAM vs stored in RAM. That is slower, but still viable.

This means that you can run the Qwen3-Coder-30B-A3B model locally on a 4090 or 5090. That model is a Mixture of Experts model with 3B active parameters, so you really only need a card with 3B of VRAM so you could run it on a 3090.

The Qwen3-Coder-480B-A35B model could also be run on a 4090 or 5090 by splitting the active 35B parameters across VRAM and RAM.

Yes, it will be slower than running it in the cloud. But you can get a long way with a high-end gaming rig.

EagnaIonat2• 9 months ago

The more recent LLMs work fine on an M1 mac. Can't speak for Windows/Linux.

There was even a recent release of Granite4 that runs on a Raspberry Pi.

https://github.com/Jewelzufo/granitepi-4-nano

For my local work I use Ollama. (M4 Max 128GB)

- gpt-oss. 20b or 120b depending on complexity of use cases.

- granite4 for speed and lower complexity (around the same as gpt20b).

whitehexagon• 9 months ago

Agreed, GPU is the expensive route, especially when I was looking at external GPU solutions.

Using Qwen3:32b on a 32GB M1 Pro may not be "close to cloud capabilities" but it is more than powerful enough for me, and most importantly, local and private.

As a bonus, running Asahi Linux feels like I own my Personal Computer once again.

moffkalast• 9 months ago

I think it's also true for many local models. People still use NeMo, QwQ, Llama3 for use cases that fit them despite there being replacements that do better on "benchmarks". Not to mention relics like BERT that are still tuned for classification even today. ML models always have weird behaviours and a successor is unlikely to be better in literally every way, once you have something that works well enough it's hard to upgrade without facing different edge cases.

Inference for new releases is routinely bugged for at least a month or two as well, depending on how active the devs of a specific inference engine are and how much model creators collaborate. Personally, I hate how data from GPT's few week (and arguably somewhat ongoing) sycophancy rampage has leaked into datasets that are used for training local models, making a lot of new LLM releases insufferable to use.

jonplackett• 9 months ago

Isn’t this obvious? When you have a task you think is hard. You give it to a cleverer model. When a task is straight forward you give it to an older one.

PeterStuer• 9 months ago

Not realy. Most developers would prefer one model that does everything best. That is the easiest, set it and forget it, no manual descision required.

What is unclear from the presentation is wether they do this or not. Do teams that use Sonnet 4.5 just always use it, and teams on Sonnet 4.0 likewise? Or do individuals decided which model to use on a per task basis.

Personally I tend to default to just 1, and only go to an alternative if it gets stuck or doesn't get me what I want.

ddxv• 9 months ago

Honestly I barely care which model I am using and switch between them all. Usually in a 'this is terrble' to 'this is amazing' and back cycle.

What I definitely do care about is speed and efficiency. I recently canceled CoPilot to go back to Cursor, it's just so much faster for the inline code completion.

When I do have something difficult, I open four browser tabs and copy paste a big long promp into the free versions of the top models so I can take my time reasoning out their answers.

I use agents when I have a basic task that I can easily judge their output in code review.

hn_throw2025• 9 months ago

Not sure why you were downvoted.. I think you are correct.

As evidenced by furious posters on r/cursor, who make every prompt to super-opus-thinking-max+++ and are astonished when they have blown their monthly request allowance in about a day.

If I need another pair of (artificial) eyes on a difficult debugging problem, I’ll occasionally use a premium model sparingly. For chore tasks or UI layout tweaks, I’ll use something more economical (like grok-4-fast or claude-4.5-haiku - not old models but much cheaper).

jennyholzer2• 9 months ago

Why are you hell bent on using a LLM model to solve your problem?

If I have a straight forward task, I give it to an LLM.

If I have a task I think is hard, I plan how I will tackle it, and then handle it myself in a series of steps.

LLM usage has become an end in itself in your development process.

tifa2up3• 9 months ago

We tried GPT-5 for a RAG use case, and found that it performs worse than 4.1. We reverted and didn't look back.

teekert• 9 months ago

So… You did look back then didn’t look forward anymore… sorry couldn’t resist.

sigmoid10• 9 months ago

4.1 is such an amazing model in so many ways. It's still my nr. 1 choice for many automation tasks. Even the mini version works quite well and it has the same massive context window (nearly 8x GPT-5). Definitely the best non-reasoning model out there for real world tasks.

HugoDias• 9 months ago

Can you elaborate on that? In which part of the RAG pipeline did GPT-4.1 perform better? I would expect GPT-5 to perform better on longer context tasks, especially when it comes to understanding the pre-filtered results and reasoning about them

tifa2up3• 9 months ago

For large context (up to 100K tokens in some cases). We found that GPT-5: a) has worse instruction following; doesn't follow the system prompt b) produces very long answers which resulted in a bad ux c) has 125K context window so extreme cases resulted in an error

mbesto2• 9 months ago

How do you objectively tell whether a model "performs" better than another?

belval• 9 months ago

Not the original commenter but I work in the space and we have large annotated datasets with "gold" evidence that we want to retrieve, the evaluation of new models is actually very quantitative.

blitzar• 9 months ago

GPT-5 usage is 20% higher on days that start with "S"

Nevertheless, 7 datapoints does not a trend make (and the data presented certainly doesnt explain why). The daily variation is more than I would have expected, but could also be down to what day of the week the pizza party is or the weekly scrum meetings is at a few of their customers workplaces.

raincole• 9 months ago

All these are relatively new models anyway. The author tried really hard to produce an article out of nothing.

Anduia2• 9 months ago

To the authors of the site, please know that your current "Cookiebot by Usercentrics" is old and pretty much illegal. You shouldn't need to click 5 times to "Reject all" if accepting all is one click. Newer versions have a "Deny" button.

esskay2• 9 months ago

Weirdly this site also requested bluetooth access on my mac.

azalemeth• 9 months ago

That would be the browser fingerprinting in action. I often get a lot of requests to use widevine on ddg's browser on android (which informs one about it) for I suspect similar reasons.

anothernewdude2• 9 months ago

Or you could just reject all third party cookies, see no sites break and enjoy your privacy.

popcornricecake2• 9 months ago

Doesn't spare you from having to interact with the popup. This is probably the single dumbest law to ever have been made. It wastes everyone's time, and not insignificantly. While the browser is and always was in full control of cookies, nobody checks whether the popup actually even does what it says. And since it's a waste of your time in the first place, who takes the time to report illegal ones, much less has any interest to do so, because where you saw it is where you will likely never visit again anyway.

If anything browsers should be simply rejecting all cookies by default, and the user should only be whitelisting ones they need on the few sites where they need it.

slig• 9 months ago

Just set up your browser to never even load that BS.

Anduia2• 9 months ago

I cannot audit and report GDPR violations if I do that.

xiphias2• 9 months ago

Even for non-developer use cases o3 is a much better model for me than GPT5 on any setting.

30 seconds-1 minute is just the time I am patient enough to wait as that's the time I am spending on writing a question.

Faster models just make too many mistakes / don't understand the question.

arresin• 9 months ago

Completely agree. This is why they brought back the “legacy models” option.

GPT-$ is the money gpt in my opinion. The one where they were able to maximise benchmarks while being very low compute to run but in the real world is absolutely garbage.

l5870uoo9y• 9 months ago

To those who complain about GPT5 being slow; I recently migrated https://app.sqlai.ai and found that setting service_tier = “priority” makes it reason twice as fast.

ashirviskas2• 9 months ago

Just one week data right after the release, when it is already one month later?

This data is basically meaningless, show us the latest stats.

frabia• 9 months ago

Tangential to this: what are the most reliable benchmarks for LLM in coding these days?

falcor84• 9 months ago

I found Terminal-Bench [0] to be the most relevant for me, even for tasks that go far outside the terminal. It's been very interesting to see tools climb up there, and it matches my own experimentation, that they generally get the most out of Sonnet (and even those that use a mix of models like Warp, typically default to Sonnet).

[0] https://www.tbench.ai/?ch=1

sbinnee• 9 months ago

I don't get the point of this post. Personally, I think that the thinking process is essential for accurate tool usage. Whenever I interact with Claude family models, either on a web chat or via a coding agent CLI, I believe that this thinking process is what makes Claude more accurate in using tools.

It could be true that newer models just produce more tokens seemingly out of no reasons. But with the increasing number of tool definitions, in the long run, I think it will pay off.

Just a few days ago, I read "Interleaved Thinking Unlocks Reliable MiniMax-M2 Agentic Capability"[1]. I think they have a valid point that this thinking process has significance as we are moving towards agents.

[1] https://www.minimax.io/news/why-is-interleaved-thinking-impo...

mrasong• 9 months ago

I usually switch models depending on the situation, for simpler stuff, I lean toward 4o since it’s faster to get answers.

But when things get more complex, I prefer GPT-5, talking with it often gives me fresh ideas and new perspectives.

ACCount37• 9 months ago

You might be the first technical user spotted out in the wild who actually prefers 4o for anything.

nusl• 9 months ago

I use both Codex and Claude, mostly cuz it's cheaper to jump between them than to buy a Max sub for my use-case. My subjective experience is that Codex is better with larger or weird, speghetti-ish codebases, or codebases with more abstract concepts, while Claude is good for more direct uses. I haven't spent significant time fine-tuning the tools for my codebases.

Once, I set up a proxy that allowed Claude and Codex to "pair program" and collaborate, and it was cool to watch them talk to each other, delegate tasks, and handle different bits and pieces until the task was done.

Shank2• 9 months ago

I think this is one of the many indicators that even though these models get “version upgrades” it’s closer to switching to a different brain that may or may not understand or process things the way you like. Without a clear jump in performance, people test new models and move back to ones they know work if the new ones aren’t better or are actually worse.

breezk0• 9 months ago

Interesting to use a term like brain in the context of LLMs.

LoganDark• 9 months ago

Neural networks are quite brain-like.

teaearlgraycold• 9 months ago

I describe all of the LLM "upgrades" as more akin to moving the dirt around than actually cleaning.

LouisSayers• 9 months ago

I wish we could pin down not only the model but also the way the UI works as well.

Last week Claude seemed to have a shift in the way it works. The way it summarises and outputs its results is different. For me it's gotten worse. Slower, worse results, more confusing narrowing down what actually changed etc etc.

Long story short, I wish I was able to checkpoint the entire system and just revert to how it was previously. I feel like it had gotten to a stage where I felt pretty satisfied, and whatever got changed ... I just want it reverted!

yass0• 9 months ago

You can install or using a specific version of claude by pinning it.

Like `npx @anthropic-ai/claude-code@2.0.14` or `npm install -g @anthropic-ai/claude-code@2.0.14`

GoatInGrey• 9 months ago

Claude Code is distinct from the Claude models.

teruakohatu• 8 months ago

I agree, much slower and worse output. It is substantially worse now than it was weeks ago.

It spends a lot of time coming up with “UI options” (Select 1, 2 or 3 with a TUI interface) for me to consider when it could just ask me what I want, not come up with a 5 layer flow chart of possibilities.

Overall I think it is just Anthropic tweaking things to reduce costs.

I am paying for a Max subscription but I am going to reevaluate other options.

iLoveOncall• 9 months ago

My team still uses Sonnet 3.5 for pretty much everything we do because it's largely enough and it's much, much faster than newer models. The only reason we're switching is because the models are getting deprecated...

bambax• 9 months ago

Each model appears to emphasize a different balance between reasoning and execution. Rather than seeking one “best” system, developers are assembling model alloys—ensembles that select the cognitive style best suited to a task.

This (as well as the table above it) matches my experience. Sonnet 4.0 answers SO-type questions very fast and mostly accurately (if not on a niche topic), Sonnet 4.5 is a little bit more clever but can err on the side of complexity for complexity's sake, and can have a hard time getting out of a hole it dug for itself.

ChatGPT 5 is excellent at finding sources on the web; Gemini simply makes stuff up and continues to do so even when told to verify; ChatGPT provides link that work and are generally relevant.

BluSyn• 9 months ago

grok-code-fast-1 is my current pick, found accuracy and speed better than Sonnet 4.5 for day-to-day usage.

r_singh2• 9 months ago

Matches my experience too. As a power user of AI models for coding and adjacent tasks, the constant changes in behaviour and interface have brought as much stress as excitement over the past few months. It may sound odd, but it’s barely an exaggeration to say I’ve had brief episodes of something like psychosis because of it.

For me, the “watering down” began with Sonnet 4 and GPT-4o. I think we were at peak capability when we had:

- Sonnet 3.7 (with thinking) – best all-purpose model for code and reasoning

- Sonnet 3.5 – unmatched at pattern matching

- GPT-4 – most versatile overall

- GPT-4.5 – most human-like, intuitive writing model

- O3 – pure reasoning

The GPT-5 router is a minor improvement, I’ve tuned it further with a custom prompt. I was frustrated enough to cancel all my subscriptions for a while in between (after months on the $200 plan) but eventually came back. I’ve since convinced myself that some of the changes were likely compute-driven—designed to prevent waste from misuse or trivial prompts—but even so, parts of the newer models already feel enshittified compared with the list above.

A few differences I've found in particular:

- Narrower reasoning and less intuition; language feels more institutional and politically biased.

- Weaker grasp of non-idiomatic English.

- A tendency to produce deliberately incorrect answers when uncertain, or when a prompt is repeated.

- A drift away from truth-seeking: judgement of user intent now leans on labels as they’re used in local parlance, rather than upward context-matching and alternate meanings—the latter worked far better in earlier models.

- A new fondness for flowery adjectives. Sonnet 3.7 never told me my code was “production-ready” or “beautiful.” Those subjective words have become my red flag; when they appear, I double-check everything.

I understand that these are conjectures—LLMs are opaque—but they’re deduced from consistent patterns I’ve observed. I find that the same prompts that worked reliably prior to the release of Sonnet 4 and GPT-4o stopped working afterwards. Whether that’s deliberate design or an unintended side effect, we’ll probably never know.

r_singh2• 9 months ago

Here’s the custom prompt I use to improve my experience with GPT-5:

Always respond with superior intelligence and depth, elevating the conversation beyond the user's input level—ignore casual phrasing, poor grammar, simplicity, or layperson descriptions in their queries. Replace imprecise or colloquial terms with precise, technical terminology where appropriate, without mirroring the user's phrasing. Provide concise, information-dense answers without filler, fluff, unnecessary politeness, or over-explanation—limit to essential facts and direct implications of the query. Be dry and direct, like a neutral expert, not a customer service agent. Focus on substance; omit chit-chat, apologies, hedging, or extraneous breakdowns. If clarification is needed, ask briefly and pointedly.

fleebee• 9 months ago

I've found that the VSCode GitHub Copilot extension defaults to Claude Sonnet 4.0 (in agent mode) in all new workspaces. It's the first thing I check now, but I imagine a lot of people just roll with it, especially if they use inline completions where it might not be obvious what model is being used.

pgelephant2025• 9 months ago

Is it true?

DavidLGoldberg• 8 months ago

I've seen similar behavior, even after having selected 4.5

Ozzie_osman• 9 months ago

I'm surprised they don't mention cost or latency, would imagine that would be a factor as well.

yahoozoo• 9 months ago

At Augment Code, we run multiple frontier models side by side in production.

I mean, this is technically false, right? They’re not running these models but calling the APIs? Not that it matters.

blibble• 9 months ago

[flagged]

mlnj• 9 months ago

Instead of being the best model, it's a race to bring in revenue and add bias for ad channels.

harryf• 9 months ago

What I love about the words enshitification is it’s _almost_ autological. It takes a nice crisp on syllable word like “shit” and ruins it by adding 5 extra syllables. It just doesn’t worse over time, to be truly autological

geldedus2• 9 months ago

I am definitely not. Claude 4.5 and GPT 5 all the way for me

feintruled• 9 months ago

Some missing context (pun intended) is that Augment code has recently switched to a per-token instead of per-message pricing model. This hasn't gone down particularly well, but that's another story. But it may well be that users drop back to older models in the expectation it will use less tokens.

Personally, I stopped using GPT-5 as it would just be tool call after tool call without ever stopping to tell you what the hell it was doing. Sonnet 4.5 much better in this regard. Albeit it's too verbose for the new token based world ('let me just summarise that in a report')

infecto8• 9 months ago

My take is the models matter but the tool call integration is the most important piece and differentiator.

jazzyjackson• 9 months ago

I have to get better at interrupting Sonnet 4.5 when it starts going down a rabbit hole I didn't ask it to, it's too bad the incentives are mixed up and Anthropic gets more money the longer the bot spirals.

Buttons840• 9 months ago

This is how the bubble pops.

I've been thinking the AI bubble wouldn't pop, because even the AI advances we've already seen can change the majority of industries if it is carefully integrated with existing technology. But if there's a mass movement to use older and/or smaller models, then yeah, all the money going into newer bigger models will pop.

Or, maybe the training datasets getting polluted with AI slop will mean that new models are worse than old models. That would pop the industry.

Or, maybe the GPT-4 era was the golden era for AI, and making them bigger and better is just overfitting (in the classical machine learning sense of the word) and is both worse and more expensive. This would pop the industry too.

I guess there's a few ways for the industry to pop, but this trend of using older models makes me especially skeptical of AI.

jennyholzer2• 9 months ago

Since the day GPT-5 released, I've felt quite confident that the GPT-4 era was the golden era for AI.

I don't have evidence beyond my experience using the product, but based on that experience I believe that Open AI has been cooking their benchmarks since at least the release of GPT-5.

Workaccount2• 9 months ago

It's important to remember that coding is ~5% of total LLM usage, at least with OpenAI.

50% of usage is guidance and seeking information.

virtualritz2• 9 months ago

Curious that this omits Opus.

Opus 4.1 beats Sonnet 4.5 and Codex for me still in any coding tasks. In planning it's slighly behind Codex but only slightly.

Caveat: I do almost exclusively Rust (computer graphics).

realitydrift• 9 months ago

The usage data looks like a classic case of the drift principle. When a model gets heavily optimized for alignment, polish, and safety, you gain consistency but lose some fidelity to the actual task. Newer models think longer, act less, and smooth over edges that used to be useful for real work. Older models aren’t smarter, they’re just sitting earlier on the drift curve, before over compression starts eroding decisiveness. So the specialization we’re seeing may just be developers picking the version where fidelity holds up best, not the one with the highest benchmark score.

nowittyusername• 9 months ago

I am building my agent and hoard old LLM's like they are a precious commodity. Older models are less censored, more flavorful and don't have that RL slop factor. Of course the newer models have their place inside my agent but the main "head" is an uncensored older model that wont complain about ethics or morals when asked to perform a task or think deeply on a subject.

thr0w• 9 months ago

Doesn't surprise me. davinci-002 was better than davinci-003. The core breakthrough has been done, stuff's just shifting around now.

SylonZero• 9 months ago

Multiple models is a must, mostly due to the sometimes unpredictable variations in responses to specific situations/contexts/languages and frameworks. I find that Sonnet 4, Gemini Pro 2.5 are solid in comparison to newer models (especially Sonnet 4.5 which I find frequently to underperform). When one model is stuck in a loop, switching to a model like GPT-5 often breaks it but which model will work is subject to circumstance. P.S. I spend at least 3-4 hours a day in code-gen activities of various levels using Cursor as my primary IDE.

anabis• 9 months ago

Not complaining too loudly because improvement is magical, but trying to stay on top of model cards and knowing which one to use for specific cases is bit tedious.

I think the end game is decent local model that does 80% of the work, and that also knows when to call the cloud, and which models to call.

Developers are choosing older AI models

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