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Comment by bottlepalm | Hacker News Reader

Coding with AI it feels like if you're not using the best model then you're possibly missing out - creating less capable, maintainable, just plain 'good' code. Why waste time using anything less than the best and cleaning up the mess later on. This is why I feel like local models and Chinese models aren't taking off (and Gemini/Grok) - they work, but they're plain just not as good as OpenAI/Anthropic. If you have the money then it doesn't make sense to code with anything else.

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There are diminishing returns, especially for more mundane tasks. Fable is nice, and I bet Sol is also nice. But there really isn't much of a difference right now when using something beyond Opus or presumably Terra for most things. They're most useful when doing greenfield, highly complex/novel tasks. When Open Source catches up, it will be more widely adopted.

Yea, but by the time open source catches up, the frontier will be that much more capable and you won't want to waste time babysitting less capable models.

Another dimension for the fronteir to move in is speed. Codex has /fast which is great, but yea the bottleneck right now in many cases is just the time it takes these tools to complete tasks. I'm running many sessions in parallel just because I'm waiting for tasks to finish. I'm constantly round robin'ing them, and kicking them off on the next 20 minute task. If these models were faster I wouldn't need to context switch as much.

I’ve been using mostly deepseek v4, kimi k2.6, and gpt 5.3-codex

I sometimes chuck a few tokens to gpt 5.5 and opus 4.8 and they can sometimes solve a problem one of the other models couldn’t, but they’re not like 10x better or anything in my experience. More like 1.2x better

Does Fable write better code or just can solve more problems?

Better code hands down. Actually GPT 5.5 is a good example of a model that's generally better at solving problems than Opus 4.8, but the code it generates is worse - over engineered, shortcuts, etc.. Fable does both, better code and solves problems, but it's also very expensive. Currently I use Opus mostly, Codex for code reviews because it is pedantic, and Fable for tough problems and high level design.

The way I'd describe it, Fable is the first model that's good enough.

If all I had was Fable for the next couple of years then I'd be totally fine with that. I have never felt that about any version of Opus.

That’s the way I feel about GPT 5.2

That depends entirely on how you're using AI. If you're getting it to do all the hard thinking, then sure using the best model is probably always going to be better. But it's also going to be expensive.

Using cheaper models and using your skills and expertise from the pre-AI era can get you working just as fast. You've gotta be more specific about the work you need doing. It's less "vibes" based, but they're still effective.

Also, Chinese models absolutely are taking off. I used Claude and GPT at work, and then I tried using some Chinese models for personal projects. I am 100% convinced they're like 90% as good for 10% of the cost. But you've basically gotta be a good developer first and know what you want and know when it's giving you shit.

Honestly I'm on $200 a month for Claude Max and $100 a month for Codex, and it's nothing compared to the productivity gains if you're programming professionally. 10 bucks a day, I spend more for lunch. Time is money and I'm not going to waste time with a lesser model if I don't have to.

Yeah if you're a professional engineer it's a no brainer to buy these subs, even multiple subs, and you could replace another employee's salary especially if you're a solo founder working on your own product.

OpenCode + GLM has become a daily driver for me. With the z.ai subscription it’s super cheap too.

I’d describe it as something between Sonnet and Opus.

Is it cheaper than Codex for example? The problem with paying per token via API is it's not subsidized like subscriptions are, maybe Z AI one is though. But GLM doesn't have vision which is a deal breaker for many frontend or full stack tasks.

Or maybe you are still iterating on a plan or spec file with Qwen 3.6 27B, while I implemented three features with Fable and QA tested them in the testing environment.

Of course, if you think that this approach is as fast and effective as "vibe coding" as in outsourcing more thinking to the AI, it is not surprising you would conclude the cheaper models were nearly as useful.

I don't know if you are right or not, a lot depends on the constraints of the project and team.

Some of the newer arguably now viable use cases, such as porting a large codebase to Rust, are certainly not going to be as fast with a more manual approach.

70s thru 90s computing and even into the early 2000s every new bit of computer meant new capabilities.

Eventually it plateaued and now you can do a decent chunk of your computing on something from 2012.

People keep saying scaling will top out, for example. But scaling keeps stubbornly refusing. New techniques keep coming along too. It's really still exploding into existence and every new generation brings new capability. Eventually it'll clear a ceiling for your key use cases and you'll stop worrying about new models.

It always pays to look back at history and see if you can pattern match.

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