What a time to be alive

They likely sit on releases ready to go.

Eu auto brands colluded for years to synchronize new tech into their model lines. Could it be the AI SaaS sector is showing its first steps towards "maturity"? /s

Opus really shines for completing long-running tasks with no supervision. But if you are using Claude Code interactively and actively steering it yourself, Sonnet is good enough and is faster.

I don't believe anyone saying Sonnet yields better results than Opus though, as my experience has been exactly the opposite. But trade-off wise, I can definitely see it being a better experience when used interactively because of its speed and lower cost.

This is why you have PR departments. Being on top of the HN front page, news sites, etc matters a lot. Even if you can't be the first, it's important to dilute the attention as much as possible to reduce the limelight your competitors get.

None of them seem to have published any papers associated with them on how these new models advanced the state-of-the-art though. =^(

Given the GPT5 rumors, August is just getting started.

as if they wait competitor first then launch it at the same time to make market decide which one is best

There's so many leakers in every lab

It's definitely a coincidence

Get a subscription and use claude code - that's how you get actual reasonable economics out of it. I use claude code all day on the max subscription and maybe twice in the last two weeks have I actually hit usage limits.

Claude Code is the superior interface in my opinion. Definitely start there.

Lets see: We have GitHub, and GitHub Enterprise Server, and a GitHub API. Then there's the command line and a desktop version, and one that is just browser based I guess. Then you have different pricing plans, Free, Team, and Enterprise? How is Enterprise different than GitHub Enterprise Server? It's very easy to find evidence to confirm our bias.

Claude code is actually one of the most straightforward products I've used as far as onboarding goes. You download the tool, and follow the instructions. You can use one of the 3 plans, and everything else is automatic. You can figure out token usage and what models and versions to use and how to use MCP servers and all of that -- there's a lot of power -- but you don't need to do ANY of that to get started trying it out.

You're not being:

> That critic who doesn't try the stuff he criticizes

You're being:

> That critic who is trying to confirm their biases

Yes. You basically need an LLM to provide guidance on product selection in this brave new world.

It is actually one of my most useful use cases of this tech. Nice to have a way to ask in private so you don’t get snarky answers like: it’s just like buying shoes!

How do they know when it's time? Corporate espionage? Or do they just have Next Thing queued up months in advance and ready to go.

"Prep the next three point releases now, but don't release any until I say so. None needs to be noticably better or even different, just has to have a higher number." -CEO of AI companies

china will do that for them

I'm all buckled up and ready for what's effectively GPT 4.6

But is it just a coincidence

Well, it's expensive compared to other models. But it's often much cheaper than human labor.

E.g. if need a self-contained script to do some data processing, for example, Opus can often do that in one shot. 500 line Python script would cost around $1, and as long as it's not tricky it just works - you don't need back-and-forth.

I don't think it's possible to employ any human to make 500 line Python script for $1 (unless it's a free intern or a student), let alone do it in one minute.

Of course, if you use LLM interactively, for many small tasks, Opus might be too expensive, and you probably want a faster model anyway. Really depends on how you use it.

(You can do quite a lot in file-at-once mode. E.g. Gemini 2.5 Flash could write 35 KB of code of a full ML experiment in Python - self-contained with data loading, model setup training, evaluation, all in one file, pretty much on the first try.)

GLM 4.5 / Kimi K2 / Qwen Coder 3 / Gemini Pro 2.5

> yet the general consensus and my own experience seem to be that Sonnet is much much better

Given that there’s nothing close to scientific analysis going on, I find it hard to tell how big the “Sonnet is overall better, not just sometimes” crowd is. I think part of the problem is that “The bigger model is better” feels obvious to say, so why say it? Whereas “the smaller model is better actually” feels both like unobvious advice and also the kind of thing that feels smart to say, both of which would lead to more people who believe it saying it, possibly creating the illusion of consensus.

I was trying to dig into this yesterday, but every time I come across a new thread the things people are saying and the proportions saying what are different.

I suppose one useful takeaway is this: If you’re using Claude Max and get downgraded from Opus to Sonnet for a few hours, you don’t have to worry too much about it being a harsh downgrade in quality.

I use both. Sonnet is faster and more cost efficient. It's great for coding. Where Opus is noticeably better is in analysis. It surpasses Sonnet for debugging, finding patterns in data, creativity and analysis in general. It doesn't make a lot of sense to use Opus exclusively unless you're on a max20 plan and not hitting limits. Using Opus for design and troubleshooting and Sonnet for everything else is a good way to go.

I now eagerly await Sonnet 4.1, only because of this release.

With aggressive Claude Code use I didn't find Sonnet better than Opus but I did find it faster while consuming far fewer tokens. Once I switched to the $100 Max plan and configured CC to exclusively use Sonnet I haven't run into a plan token limit even once. When I saw this announcement my first thing was to CMD-F and see when Sonnet 4.1 was coming out, because I don't really care about Opus outside of interactive deep research usage.

The finest of AI, probably using electricity/water for 100s of homes can not even beat a very simple children game with millions of texts guides etc. about it.

When can we replace doctors with it?

Other than it starting out trying to produce a full and complete web app (or whatever) for my daily yak shaving session instead of the normal "let's talk about and work through this thing" the new Opus 4.1 seems to 'get it' a lot quicker than the old daffy robot did. It asked pertinent questions to understand the system we are working on and accomplished the goal of updating the design document so I don't have to keep explaining details at the start of every chat session. Something, by the way, it always previously failed to do causing me to have to explain stuff each and every time before forward progress could be made.

I do agree it did hit the token limit a lot quicker than before where I could chat for hours without worrying about it.

Either way, still have one last yak to shave for this project so we'll see how efficient it is with that. If it accomplishes the task before burning through all the tokens then win, win, I suppose.

> I've basically wasted the morning on Claude Code when I should've just been doing it all myself.

Welcome to the machine

https://www.youtube.com/watch?v=tBvAxSx0nAM&t=45s

They need to leave some room to release 10 more models. They could crank benchmarks to 100% but then no new model is needed lol? Pretty sure these pretty benchmark graphs are all completely staged marketing numbers since they do solve the same problems they are being trained on – no novel or unknown problematic is presented to them.

I am still very early, but output quality wise, yes, there does not seem to be any noticeable improvement in my limited personal testing suite. What I have noticed though is subjectively better adherence to instructions and documentation provided outside the main prompt, though I have no way to quantify or reliably test that yet. So beyond reliably finding Needles-in-the-Haystack (which Frontier models have done well on lately), Opus 4.1 seems to do better in following those needles even if not explicitly guided to compared to Opus 4.

I will only add that it's interesting that in the results graphic, they simply highlighted Opus 4.1 - choosing not to display which models have the best scores - as Opus 4.1 only scored the best on about half of the benchmarks - and was worse than Opus 4.0 on at least one measure.

"You pay $20/mo for X, and now I'm giving you 1.05*X for the same price." Outrageous!

Good! I'm glad they are just giving us small updates. Opus 4 just came out, if you have small improvements, why not just release them? There's no downside for us.

Given the Gregorian Calendar and the planet's path through its orbit, August is just getting started.

I think this means that GPT5 is better - you can't launch a worse model after the competitor supersedes you - you have to show that you're in the lead even if its just for a day.

If only there were more leakers in the FBI or DOJ.

It's not a coincidence or a cartel, it's PR counterprogramming.

In every price comparison I make. Claude (API) always comes out cheapest if you manage to keep most of your context cached. 90% price reduction for input is crazy.

Large models are for querying the model

Small models are for querying the context

Opus is cheap if you use it for its niche

I don't doubt Opus is technically superior, but it's not practically superior for me.

It's still pretty much impossible to have any LLM one-shot a complex implementation. There's just too many details to figure out and too much to explain for it to get correct. Often, there's uncertainty and ambiguity that I only understand the correct answer (or rather less bad answer) after I've spent time deep in the code. Having Opus spit out a possibly correct solution just isn't useful to me. I need to understand _why_ we got to that solution and _why_ it's a correct solution for the context I'm working in.

For me, this means that I largely have an iteratively driven implementation approach where any particular task just isn't that complex. Therefore, Sonnet is completely sufficient for my day-to-day needs.

Every time that Sonnet is acting like it has brain damage (which is once or twice a day), I switch to Opus and it seems to sort things out pretty fast. This is unscientific anicdata though, and it could just be that switching models (any model) would have worked.

Opus seems better to me on long tasks that require iterative problem solving and keeping track of the context of what we have already tried. I usually switch to it for any kind of complicated troubleshooting etc.

I stick with Sonnet for most things because it's generally good enough and I hit my token limits with it far less often.

Im on the Max plan and generally Opus seems to do better work than Sonnet. However, that’s only when they allow me to use Opus. The usage limits, even on the max plan, are a joke. Yesterday I hit the limits within MINUTES of starting my work day.

If I'm using cursor then sonnet is better, but in claude code Opus 4 is at least 3x better than Sonnet. As with most things these days, I think a lot of it comes down to prompting.

I've also noticed Sonnet starting to degrade. It's developing some of the behaviours that put me off the competition in the first place. Needless explanations, filler in responses, wanting to put everything in lists, even increased sycophancy.

I feel like this is just related to my projects getting bigger. Claude Code is trying to keep up with my project evolving from 2k lines of code to 100k lines. Of course it’s going to feel worse.

i think its probably mostly vibes but that still counts, this is not in the charts

> Windsurf reports Opus 4.1 delivers a one standard deviation improvement over Opus 4 on their junior developer benchmark, showing roughly the same performance leap as the jump from Sonnet 3.7 to Sonnet 4.

That's why they named it 4.1 and not 4.5

I don't think this could even be called an improvement? It's small enough that it could just be random chance

This is so people don't immediately migrate for GPT5

Their API pricing is bonkers, their subscription is a great deal for what you get

Not for me.

How does running it multiple times performs?

LLMs are non-deterministic, I think benchmarks should be more about averages of N runs, rather than single shot experiments.

What type of work do you do and what type of code do you produce?

Because I've found it to work pretty amazingly for things that don't need to be exact (like data modeling) or don't have any security implications (public apps). But for everything else I end up having to find all the little bugs by reading the code line by line, which is much slower than just writing the code in the first place.

The only way you could be 10x more productive is omit you were doing nothing before.

can you share your workflow?

On the other hand, they have always exposed their raw chain of thought, so you know exactly what you're paying for, unlike OpenAI who hides it. Similarly they allow an actual thinking budget rather than vague "low, medium, high", again unlike OpenAI. They also allow API access to all their models without draconic send-us-your-personal-data-KYC, once more unlikely OpenAI.

They might not fit your personal definition of "openness", but they do fit many other equally valid interpretations of that contept.

This legitimately made me chuckle.

Agree 100%

If you look at the past, whenever Google announces something major, OpenAI almost always releases something as well.

People forget realize that OpenAI was started to compete with Google on AI.

Given the majesty and nobility of HN commenters, augustness is just getting started.

Any source or just vibes?

In my experience it take weeks if not months to coordinate a release, from testing to documentation to drafting press releases in multiple languages to benchmarks and website updates.

I’m old and I’ve been in this industry most of my life. I have never once seen or heard of all of that work being done and the company just waiting on competitors before pulling the trigger.

Download Claude Code

Create a new directory in your terminal

Open that directory, type in "Claude" to run Claude

Press Shit + Tab to go into planning mode

Tell Claude what you want to build - recommend something simple to start with. Specify the languages, environment, frameworks you want, etc.

Claude will come up with a plan. Modify the plan or break it into smaller chunks if necessary

Once plan is approved, ask it to start coding. It will ask you for permissions and give you the finished code

It really is something when you actually watch it go.

That's fine if you use it for private use. Doesn't work if you're building a product using Claude.

huh?

Claude Mad is tens of hours of opus a month, or you can pay per token and have unlimited.

Or did you mean “I wish it was cheaper”?

only 10x? I'm at least 100x as productive. I only type at a measly 100wpm, whereas Claude can output 100+ tokens a second

I'm outputting a PR every 6 minutes. The reviewers are using Claude to review everything. It used to take a day to add 100 lines to the codebase.. now I can add 100 lines in one prompt

If I want even more productivity (at risk of making the rest of my team look slow) I can tell Claude to output double the lines and ship it off for review. My performance metrics are incredible

I also have this feeling that I'm 2-10x more productive. But isn't it curious how a lot of devs feel this way, but no devs that I know have the experience that any of their colleagues have become 2-10x more productive?

How do you maintain high confidence in the code it generates ?

My current bottleneck is having to review the huge amounts of code that these models spit out. I do TDD, use auto-linting and type-checking.... but the model makes insidious changes that are only visible on deep inspection.

Good one, made my day

Not sure that this is true. Are there a lot of people waiting anxiously to adopt the next model on the day of release and expecting some huge work advantage?

It's a risky game to leak the secrets of the gang that has a legal monopoly on violence.

> Get a subscription and use claude code

I find the token/credit restrictions on Opus to be near useless even when using Claude Code. I only ever switch to it so get another model's take on the issue. Five minutes of use and I have hit the limit.

Is it considerably more cost effective than cline+sonnet api calls with caching and diff edits?

Same context length and throughput limits?

Anecdotally I find gpt4.1 (and mini) were pretty good at those agentic programming tasks but the lack of token caching made the costs blow up with long context.

Is there any documentation on what the max sub usage limit is? A coworker tried it and was booted off Opus within just a couple hours due to "high usage". I haven't made the jump since I expect my $3k/mo on API would just instantly fly by a $200/mo sub and then I'd just be back on API again, but if it could carve out $1k-2k of costs for a little bit of time managing sub(s) it might be worth it.

Is there a way to sign up for Claude code that doesn't involve verifying a phone number with Anthropic? They don't even accept Google Voice numbers.

Maybe I'm out of touch, but I'm not handing out my phone number to sign up for random SaaS tools.

That is a big improvement.

I’ve always wondered about this actually. My assumption is that they always “pick the best” result from these tests.

Instead, ideally they’d run the benchmark tests many times, and share all of the results so we could make statistical determinations.

Because the offerings are not simple. Your Nike example is silly; everyone knows what to do with shoes and shorts and shirts, and why they might want (or not want) to buy those particular items from Nike.

But for someone who hasn't been immersed in the "LLM scene", it's hard to understand why you might want to use one particular model of another. It's hard to understand why you might want to do per-request API pricing vs. a bucketed usage plan. This is a new technology, and the landscape is changing weekly.

I think maybe it might be nice if folks around here were a bit more charitable and empathetic about this stuff. There's no reason to get all gatekeep-y about this kind of knowledge, and complaining about these questions just sounds condescending and doesn't do anyone any good.

If anything, Anthropic has the product lineup that makes the most sense. Higher numbers mean better model. Haiku < Sonnet < Opus which translates to length/size. Free < Pro < Max.

Contrast to something like OpenAI. They've got gpt4.1, 4o, and o4. Which of these are newer than one another? How do people remember which of o4 and 4o are which?

Because few seem to want to expend the effort to dive in and understand something. Instead they want the details spoonfed to them by marketing or something.

I absolutely loathe this timeline we're stuck in.

Claude Code is really good for this situation.

If you like an IDE, for example VS Code you can have the terminal open at the bottom and run Claude Code in that. You can put your instructions there and any edits it makes are visibile in the IDE immediately.

Personally I just keep a separate terminal open and have the terminal and VSCode open on two monitors - seems to work OK for me.

I've made my own fork of Codex that always uses flex, or you can route agents through litellm and make it add the service_tier parameter. I haven't really seen native support for it anywhere.

This is like being told to buy Nike shoes. Then when you proudly display your new cleats, they tell you "no, I meant you should by basketball shoes. The cleats are terrible."

Ha - the $200 plan should be renamed to "Claude Mad Max" :)

Big if true

<raises hand> Our automated test folks were chronically behind, struggling to keep up with feature development. I got the two assigned to the team that was the most behind set up with Claude Code. Six weeks later they are fully caught up, expanding coverage, and integrating AI code review into our build pipeline.

It's not 10x, but those guys do seem like they've hit somewhere around 2x improvement overall.

If you’re using an LLM near the limits of what it can do then a small improvement in performance is noticeable.

Absolutely.

My coworkers/partners and I haven’t stopped talking about it for weeks. I’m one of them I guess, but we’ll see. The ARC graph I saw, if accurate, is really incredible.

Slipped in the bathroom and hung himself on the shower curtains. Oh, what a shame.

When it's "that's why they incremented the version by a tenth instead of a half" you know things have really started to slow for the large models.

In this case, they tried something and were told they were doing it wrong, and they know there's more than one way to do it wrong - wrong model, wrong tool using the model, wrong prompting, wrong task that you're trying to use it for.

And of course you could be doing it right but the people saying it works great could themselves be wrong about how good it is.

On top of that it costs both money and time/effort investment to figure out if you're doing it wrong. It's understandable to want some clarity. I think it's pretty different from buying shoes.

> Why is this the top comment on so many threads about tech products?

Because you overestimate the difference that the representative person understands.

A more accurate analogy is that Nike sells green-blue shoes and Nike sells blue-green shoes, but the blue-green shoes add 3 feet to your jump and green-blue shoes add 20 mph to your 100 yard dash sprint.

You know you need one of them for tomorrow's hurdles race but have no idea which is meaningful for your need.

When you walk into a store, you can see and touch all of these products. It's intuitive.

With all this LLM cruft all you get is essentially the same old chat interface that's like the year 2000 called and wants its on-line chat websites back. The only thing other than a text box that you usually get is a model selector dropdown squirreled away in a corner somewhere. And that dropdown doesn't really explain the differences between the cryptic sounding options (GPT-something, Claude Whatever...). Of course this confuses people!

Hey, I'm open to the idea that I'm just stupid. But, if people in your target market (software developers) don't even understand your product line and need a HOWTO+glossary to figure it out, maybe there's also a branding/messaging/onboarding problem?

Eh, this seems like a take that reeks a bit of "everyone is stupid except me".

I do know the answer to OP's question but that's because I pickle my brain in this stuff. It is legitimately confusing.

The analogy to different SKUs strikes me also inaccurate. This isn't the difference between shoes, shirts, and shorts - it's more as if a company sells three t-shirts but you can't really tell what's different about them.

It's Claude, Claude, and Claude. Which ones code for you? Well, actually, all of them (Code, web/desktop Claude, and the API can all do this)

Which ones do you ask about daily sundry queries? Well, two of them (web/desktop Claude, but also the API, but not Code). Well, except if your sundry query is about a programming topic, in which case Code can also do that!

Ok, if I do want to use this to write code, which one should I use? Honestly, any of them, and the company does a poor job of explaining why you would use each option.

"Which of these very similar-seeming t-shirts should I get?" "You knob. How are posts like this even being posted." is just an extremely poor way to approach other people, IMO.

Which Nike shoe is best for basketball? The Nike Dunk, Air Force 1, Air Jordan, LeBron 20, LeBron XXI Prime 93, Kobe IX elite, Giannis Freak 7, GT Cut, GT Cut 3, GT Cut 3 Turbo, GT Hustle 3, or the KD18?

At least with those you can buy whatever you think is coolest. Which Claude model and interface should the average programmer use?

I wouldn't be too prescriptive. I have Pro, and it's fine. I'm not an incredibly heavy user (yet?); I've hit the rate limits a couple times, but not to the point where I'm motivated to spend more.

I haven't tried it myself, but I've heard from people that Opus can be slow when using it for coding tasks. I've only been using Sonnet, and it's performed well enough for my purposes.

As far as I can tell - that seems to have changed today!

What does Opus do extra?

So no human reads the actual code that you push to production? Are you not worried about security risks, spaghetti code, and other issues? Or does Claude magically make all of those concerns go away?

Not sure if joking...?

10x means to me that i can finish a month of work in max 2 days and go cloud watching. What does it mean for you?

You have to review your code for quality and bugs and errors now just as you did last month or last year. Did you never write bugs accidentally before?

We're all bottlenecked on reviewing now. That's a good thing.

No, the rotation of the Earth around its axis did so.

Sneakier is safer.

Also, the green-blue shoes charge per-step, but the blue-green shoes are billed monthly by signing up for BlueGreenPro+ or BlueGreenMax+, each with a hidden step limit but BlueGreenMax+ is the one that gives you access to the Cyan step model which is better; plus the green-blue shoes are only useful when sprinting, but the blue-green shoes can be used in many different events, but only through the Nike blue-green API that only some track&field venues have adopted...

They released this because competitors are releasing things

Is it though? People complain about sore feet and hear they wear the wrong kind of shoes so they go to the store where they have to spend money to find out while trying to navigate between dress shoes, minimal shoes, running shoes, hiking shoes etc etc., they have to know their size, ask for assistance in trying them on...

Claude.ai, ChatGPT, etc. are finished B2C products. They're black boxes, encapsulated experiences. Consumers don't want to pick a model, or know what model they're using; they just want to "talk to AI", and for the system to know which model is best to answer any given question. I would bet that for these companies, if their frontend observes you using the little model override button, that gets instrumented as an "oops" event in their metrics — something they aim to minimize.

What you're looking for, are the landing pages of the B2B API products underlying these B2C experiences. That would be https://www.anthropic.com/claude, https://openai.com/api/, etc. (In general, search "[AI company] API".)

From those B2B landing pages, you can usually click through to pages with details about each of their models.

Here's the model page corresponding to this news announcement, for example: https://www.anthropic.com/claude/opus

(Also, note how these B2B pages are on the AI companies' own corporate domains; whereas their B2C products have their own dedicated domains. From their perspective, their B2C offerings are essentially treated as separate companies that happen to consume their APIs — a "reference use-case" — rather than as a part of what the B2B company sells.)

Opus is slow, so sessions should be used in parallel, likely across work trees. You shouldn't sit and wait on an Opus agent.

My hot take is that your friend should show you what they’re using, not just dismiss Copilot and leave you hanging!

Sonnet works fine in many cases. Opus is smarter, and custom 'agents' can be set to use either.

I prefer configuring it to use Sonnet for things that don't require much reasoning/intelligence, with Opus as the coordinator.

Actually I think I was wrong, the PR material was just vague about it.

> I think it's pretty different from buying shoes.

Shoe shopping is pretty complex, more so than trialing an AI model in my opinion.

Are you a construction worker, a banker, a cashier or a driver? Are you walking 5 miles everyday or mostly sedentary? Do you require steel toed shoes? How long are you expecting them to last and what are you willing to pay? Are you going to wear them on long runs or take them river kayaking? Do they need to be water resistant, waterproof or highly breathable? Do you want glued, welted, or stitch down construction? What about flat feet or arch support? Does shoe weight matter? What clothing are you going to wear them with? Are you going to be dancing with them? Do the shoes need a break in period or are they ready to wear? Does the available style match your preferences? What about availability, are you ok having them made to order or do you require something in stock now?

By comparison I can try 10 different AI services without even needing to stand up for a break while I can't buy good dress shoes in the same physical store as a pair of football cleats.

Opus 4 came out 10 weeks ago. So this is basically one new training run improvement.

What's the average programmer? Is it someone who likes CLI tools? Or who likes IDE integration? Different strokes for different folks and surely the average programmer understands what environment they will be most comfortable in.

> It's Claude, Claude, and Claude. Which ones code for you?

Thanks for articulating the confusion better than I could! I feel it's a similar branding problem as other tech companies have: I'm watching Apple TV+ on my Apple TV software running on my Apple TV connected to my Google TV that isn't actually manufactured by Google. But that Google TV also has an Apple TV app that can play Apple TV+.

It's a much larger, more capable LLM than Claude Sonnet.

The environment isn't the only difference, it's not "do you prefer CLI or IDE or Web" because they behave differently. Claude Code and Claude web and Claude through Cursor won't give you identical outputs for the same question.

It's not like running a tool in your IDE or CLI where the only difference is the interface. It would be like if gcc ran from your IDE had faster compile times, but gcc run from the CLI gives better optimizations.

The fact that no one is recommending any baseline to start with proves the point that it's confusing. And we haven't even touched on Sonnet v Opus

I mean day to day. How is the coding experience different?

Ya know, in the over half a century I've been on this planet, choosing a new pair of shoes is so low on my 'life's little annoyances' list that it doesn't even rise above the noise of all the stupid random things which actually do annoy me.

Maybe the problem is I don't take shoes seriously enough? Something to work on...

> Different strokes for different folks and surely the average programmer understands what environment they will be most comfortable in.

That's a silly claim to me, we're talking about a completely new environment where you prompt an AI to develop code, and therefore an "average programmer" is unlikely to have any meaningful experience or intuition with this flow. That is exactly what GP is talking about - where does he plug in the AI? What tradeoffs are there to different options?

The other day I had someone judge me for asking this question by dismissively saying "dont say youve still been using ChatGPT and copy/paste", which made me laugh - I don't use AI at all, so who was he looking down on?

> Shoe shopping is pretty complex, more so than trialing an AI model in my opinion.

Oh c'mon, now you're just being disingenuous, trying to make an argument for argument's sake.

No, shoe shopping is not more complicated than trialing a LLM. For all of those questions about shoes you are posing, either a) a purchaser won't care and won't need to ask them, or b) they already know they have specific requirements and will know what to ask.

With an LLM, a newbie doesn't even know what they're getting into, let alone what to ask or where to start.

> By comparison I can try 10 different AI services without even needing to stand up for a break

I can't. I have no idea how to do that. It sounds like you've been following the space for a while, and you're letting your knowledge blind you to the idea that many (most?) people don't have your experience.

And in 52 weeks we've gone 3.5->4.1 with this training improvement, meanwhile the 52 weeks prior to that were Claude -> Claude 3. The absolute jumps per version delta also used to be larger.

I.e. it seems we don't get much more than new training run levels of improvement anymore. Which is better than nothing, but a shame compared to the early scaling.

It's a bit worse than a branding problem honestly, since there's legitimate overlap between products, because ultimately they're different expressions of the same underlying LLMs.

I'm not sure if you ever got a good rundown, but the tl;dr is that the 3 products ("Desktop", Code, and API) all expose the same underlying models, but are given different prompts, tools, and context management techniques that make them behave fairly differently and affect how you interact with them.

- The API is the bare model itself. It has some coding ability because that's inherent to the model - you can ask it to generate code and copy and paste it for example. You normally wouldn't use this except that if you're using some Copilot-type IDE integration where the IDE is doing the work of talking to the model for you and integrating it into your developer experience. In that case you provide API key and the IDE does the heavy lifting.

- The desktop app is actually a half-decent coder. It's capable of producing specific artifacts, distinguishing between multiple "files" it's writing for you, and revisiting previously-written code. "Oh, actually rewrite this in Go." is for example a thing it can totally do. I find it useful for diagnosing issues interactively.

- "Claude Code" is a CLI-only wrapper around the model. Think of it like Anthropic's first-party IDE integration, except there's not an IDE, just the CLI. In this case the integration gives the tool broad powers to actually navigate your filesystem, read specific files, write to specific files, run shell commands like builds and tests, etc. These are all functions that an IDE integration would also give you, but this is done in a Claude-y way.

My personal take is: try Claude Code, since as long as you're halfway comfortable with a CLI it's pretty usable. If you really want a direct IDE integration you can go with the IDE+API key route, though keep in mind that you might end up paying more (Claude Code is all-you-can-eat-with-rate-limits, where API keys will... just keep going).

You also learned about your shoe needs over the course of a lifetime. A caregiver gave you your first pair and you were expected to toddle around at most with them. You outgrew and replaced shoes as a child, were placed into new scenarios requiring different footwear as you grew up, learning and forming opinions about what's appropriate functionally, socially, economically as you went. You learned what stores were good for your needs, what brands were reputable, what styles and fits appealed to you. It took you more than a decade at minimum to achieve that.

If you allow yourself to be a novice and a learner with AI and LLMs and don't expect to start out as a "shoe expert" where you never even think about this in your life and it's not even an annoyance, you'll find that it's the exact same journey.

It sounds like you're generally unfamiliar with using AI to help you at all? Or maybe you're also being disingenuous? It's insanely easy to figure this stuff out, I literally know a dozen people who are not even engineers, have no programming experience, who use these tools. Here's what Claude (the free version at claude.ai) said in response to me saying "i have no idea how to use AI coding assistants, can you succinctly explain to me what i need to do? like, what do i download, run, etc in order to try different models and services, what are the best tools and what do they do?":

Here's a quick guide to get you started with AI coding assistants:

## Quick Start Options (Easiest)

*1. Web-based (Nothing to Download)* - *Claude.ai* - You're here! I can help with code, debug, explain concepts - *ChatGPT* - Similar capabilities, different model - *GitHub Copilot Chat* - Web interface if you have GitHub account

*2. IDE Extensions (Most Popular)* - *Cursor* - Full VS Code replacement with AI built-in. Download from cursor.com, works out of the box - *GitHub Copilot* - Install as VS Code/JetBrains extension ($10/month), autocompletes as you type - *Continue* - Free, open-source VS Code extension, lets you use multiple models

*3. Command Line* - *Claude Code* - Anthropic's terminal tool for autonomous coding tasks. Install via `npm install -g @anthropic-ai/claude-code` - *Aider* - Open-source CLI tool that edits files directly

## What They Do

- *Autocomplete tools* (Copilot, Cursor) - Suggest code as you type, finish functions - *Chat tools* (Claude, ChatGPT) - Explain, debug, design systems, write full programs - *Autonomous tools* (Claude Code, Aider) - Actually edit your files, make changes across codebases

## My Recommendation to Start

1. Try *Cursor* first - download it, paste in some code, and ask it questions. It's the most beginner-friendly 2. Or just start here in Claude - paste your code and I can help debug, explain, or write new features 3. Once comfortable, try GitHub Copilot for in-line suggestions while coding

The key is just picking one and trying it - you don't need to understand everything upfront!

And in all the years that LLMs have been available I've yet to find a subscription plan confusing.

Just play with the 'free tier' on whatever website does the AI thing and figure it out.

Maybe there's a need to try ten different ones but I just stuck with one and can now convince it to do what I want it to do pretty successfully.

Claude Code running in a terminal can connect to your IDE so you can review its proposed changes there. I’ve found this to be a nice drop in way to try it out without having to change your core workflow and tools too much. Check out the /ide command for details.

Also when it comes to API integrations, I find some better than others. Copilot has been pretty crummy for me but Zed's Agent Mode seems to be almost as good as Claude Code. I agree with the general take that Claude Code is a good default place to start.

To me that's the silly argument. How many different tools have you ever used? New build system? New linter? How did you know if you wanted to run those on the command line or in your IDE?

And it seems the story you shared sort of proves the point: the web interface worked fine for you and you didn't need to question it until someone was needlessly rude about it.

Wow. After 50 replies to what I thought wasn't such a weird question, your rundown is the most enlightening. Thank you very much.

Is it really a bigger jump to go from plausible to frequently useful, than from frequently useful to indispensable?

Why is there supposed to be no step between frequently useful and indispensable? Quickly going from nothing to frequently useful (which involved many rapid hops between) was certainly surprising, and that's precisely the lost momentum.

FWIW it's probably because a lot of us have been following along and trying these things from the start so the nuances seem more obvious but also I feel that some folks feel your question is a bit "stupid", like "why are you suddenly interested in the frontier of these models? where were you for the last 2 years?"

And to some extent it is like the PC race. Imagine going to work and writing software for whatever devices your company writes software for in whatever toolchain your company uses. Then 2-3 years after the PC race began heating up, asking "Hey I only really write code for whatever devices my employer gives me access to. Now I want to buy one of these new PCs but I don't really understand why I'd choose an Intel over a Motorolla chipset or why I'd prioritize more ROM or more RAM, and I keep hearing about this thing called RISC that's way better than CISC and some of these chips claim to have different addressing modes that are better?"

Mostly sonnet because of the usage limits.

Makes sense. Seeing some posts about how the system can slow down or decrease in quality at certain times of day.

> How many different tools have you ever used? New build system? New linter? How did you know if you wanted to run those on the command line or in your IDE?

In what way is this analogous? Running scripts is vastly different than AI codemod. I could easily answer how when and why a build system would be plugged in, and linting and formatting are long-established pathways.

On the flipside there are barely even established practices, let alone best ones, for using AI. The point being offered is that AI companies offer shockingly little guidance on how to use their apparently amazing tool.

I personally have never used AI to author code, so I don't really know how the story I provided proves anything to you. I like it to answer questions about why something isn't working to help give me some leads, and it is good at telling you how to use a new framework quickly, but that's a pretty different practice than it authoring code. Seems like you're kinda dodging the question too.