There's diminishing returns bigly when you increase parameter count.

The sweet spot isn't in the "hundreds of billions" range, it's much lower than that.

Anyways your perception of a model's "quality" is determined by careful post-training.

More parameters improves general knowledge a lot, but you have to quantize more in order to fit in a given amount of memory, which if taken to extremes leads to erratic behavior. For casual chat use even Q2 models can be compelling, agentic use requires more regularization thus less quantized parameters and lowering the total amount to compensate.

Interesting. I see papers where researchers will finetune models in the 7 to 12b range and even beat or be competitive with frontier models. I wish I knew how this was possible, or had more intuition on such things. If anyone has paper recommendations, I’d appreciate it.

They're using a revolutionary new method called "training on the test set".

So, curve fitting the training data? So, we should expect out of sample accuracy to be crap?

Yeah, that's usually what tends to happen with those tiny models that are amazing in benchmarks.

It doesn’t. I’m not sure it outperforms chatgpt 3

You are not being serrious, are you? even 1.5 years old Mistral and Meta models outperform ChatGPT 3.

3 not 3.5? I think I would even prefer the qwen3.5 0.8b over GPT 3.

The short answer is that there are more things that matter than parameter count, and we are probably nowhere near the most efficient way to make these models. Also: the big AI labs have shown a few times that internally they have way more capable models

Considering the full fat Qwen3.5-plus is good, but barely Sonnet 4 good in my testing (but incredibly cheap!) I doubt the quantised versions are somehow as good if not better in practice.

I think it depends on work pattern.

Many do not give Sonnet or even Opus full reign where it really pushes ahead of over models.

If you're asking for tightly constrained single functions at a time it really doesn't make a huge difference.

I.e. the more vibe you do the better you need the model especially over long running and large contexts. Claude is heading and shoulders above everyone else in that setting.

>I.e. the more vibe you do the better you need the model especially over long running and large contexts

For sure, but the coolest thing about qwen3.5-plus is the 1mil context length on a $3 coding plan, super neat. But the model isn't really powerful enough to take real advantage of it I've found. Still super neat though!

When you say Sonnet 4, do you mean literally 4, or 4.6?

It's not as capable as Sonnet 4.6 in my usage over the past couple days, through a few different coding harnesses (including my own for-play one[0], that's been quite fun).

[0] https://github.com/girvo/girvent/

What is the benefit of writing your own harness? I am asking because I need to get better at using AI for programming. I have used Cursor, Gemini CLI, Antigravity quite a bit and have had a lot of difficulties getting them do what I want. They just tend to "know better."

I’m not an expert but I started with smaller tasks to get a feel for how to phrase things, what I need to include. It’s more manageable to manually fix things it screwed up than giving it full reign.

You may want to look at the AGENTS.md file too so you can include your stock style things if it’s repeatedly screwing up in the same way.

I think it's the same instinct as making your own Game Engine. You start off either because you want to learn how they work or because you think your game is special and needs its own engine. Usually, it's a combination of both.

Purely as an exercise to see how they operate, and understand them better. Then additionally because I was curious how much better one could make something like qwen3.5-plus with its 1 mil context window despite its weaker base behaviour, if I was to give it something very focused on what I want from it

The Pi framework is probably right up your alley btw! Very extensible

AFAIK post-training and distillation techniques advanced a lot in the past couple of years. SOTA big models get new frontier and within 6 months it trickles down to open models with 10x less parameters.

And mind the source pre-training data was not made/written for training LLMs, it's just random stuff from Internet, books, etc. So there's a LOT of completely useless an contradictory information. Better training texts are way better and you can just generate & curate from those huge frontier LLMs. This was shown in the TinyStories paper where GPT-4 generated children's stories could make models 3 orders of magnitude smaller achieve quite a lot.

This is why the big US labs complain China is "stealing" their work by distilling their models. Chinese labs save many billions in training with just a bunch of accounts. (I'm just stating what they say, not giving my opinion).