Fwiw I have a Claude pro plan and have no interest in using other offerings so I'm not sure if they're super simple (one model, one interface, one pricing plan)?
It's almost as if companies sell more than one product.
Why is this the top comment on so many threads about tech products?
Or maybe that's me, but still whether its through the likes of those vibe coding apps like lovable bolt etc.
at the end of the day, Most people are using the same tool which is claude since its mostly superior in coding (questionable now with oss models, but I still use it through kiro).
People expect this stuff to be simple when in reality its not and there is some frustation I suppose.
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.
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.
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.
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.
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!
Maybe the problem is I don't take shoes seriously enough? Something to work on...
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.
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!
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.)
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?
You're comparing well understood products that are wildly different to products with code names. Even someone who has never wore a t-shirt will see it on a mannequin and know where it goes.
I'm sorry but I cannot tell what the difference is between sonnet and opus. Unless one is for music...
So in this case you read the docs. Which is, in your analogy, you going to the Nike store and reading up on if a tshirt goes on your upper or lower body.
It's more like going to the Nike store and asking about the difference between the Vaporfly 3 and the Pegasus 41. I know they're all shoes and therefore go on my feet, but I don't know what the difference is unless one is better for riding horses?
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.
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+.
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).
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?"
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.
At least with those you can buy whatever you think is coolest. Which Claude model and interface should the average programmer use?
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?
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.
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.
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
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.
I absolutely loathe this timeline we're stuck in.
This is a well-known and documented phenomenon - the paradox of choice.
I've been working in machine learning and AI for nearly 20 years and the number of options out there is overwhelming.
I've found many of the tools out there do some things I want, but not others, so even finding the model or platform that does exactly what I want or does it the best is a time-consuming process.