Gemini is not at all unusable. It is quite usable for the tasks it excels at - to the point that it is the top pick for many tasks and I spend more money there than elsewhere. On the other hand it responds quite differently from the other major models - so that claude and gpt on one hand are similar and gemini requires a different approach. In my opinion people who think gemini is worthless have not learned how to prompt it correctly. Again, it's intuitive and watching concrete response difference due to small input changes, but if I had to summarize it shows its google books / google scholar roots.

I have started experimenting with qwen more than deepseek, but I have not had good results yet. Given the good press I presume I will learn how to interact with it for better results.

Curious if others have similar experiences in comparing models usefully, or if most don't bother with this, or do something else? I mainly use models for highly focused specialty tasks, so this fine tuning makes the difference between usable and unusable. I don't yet have the luxury of defining my preferred workflow and finding the tool for the task. Everything just breaks almost immediately if I try to shoehorn into my preferred flow.

They are cheaper! All signals point to them staying cheaper because they are built more sustainably. Also, some of the latest entries can run on 1 GPU! Literally available at your desktop where there can be no service interruptions. Not even network latency. People are one and few shotting little games for 0 dollars because they bought a GPU to play video games this year. To me that's an unbeatable value. Once the tooling catches up and a few more model releases, it could change everything completely.

Of course, when I tried it on something else it rewrote every line in the file for no good reason, applied changes directly when I told it just to plan, etc.

So maybe it has one strength.

Ha! I find that Gemini is quite useful - if only because I am forced to use it (on my personal projects) because it's the only one that has unlimited interaction for "free"

It has its limitations, yes, but so does Claude (which I am leaning on too heavily at work at the moment)

I wouldn't use Gemini 3 Flash or GPT 5.4 mini for anything except the most trivial work, although both are useful for basic exploratory work.

So I'm using a heavy model for the bulk of the work and the cost of that so far outweighs the light model that the light model cost is effectively irrelevant.

What I want is more fully open models where everything is shared. Data, training algorithms, weights. That way we can figure out if we should trust it.

It's way too important a piece of information not to have it visible.

The context would be really nice to have, but reading the comments myself, it often just isn't very clear what exactly users are building or which programming language they are using.

I think analyzing more comments is promising. If you get enough data, you can generalize across use cases and get more meaningful ratings. The obvious lever is including more posts, although it might hit diminishing returns. I'll play around with it.

For the context, I want to try giving Gemini a "scratch pad", where it can note down strengths and weaknesses per model that it finds in the comments. Something like "some users say that model x is good for writing tests". Then on each run, I let it update the scratch pad and publish the results as more of a qualitative analysis.

For the wording, I'd like to keep a certain amount of click bait, sorry ;)

And it's probably a good idea to create a list of model release dates, so older comments can't accidentally map to models that weren't released yet.

The technical abilities and usage are derived from the commenters usage reflections.