Show HN: State of the Art of Coding Models, According to Hacker News Commenters

Hello HN,

I was away from my computer for two weeks, and after coming back and reading the latest discussions on HN about coding assistants (models, harnesses), I felt very out of the loop. My normal process would have been to keep reading and figure out the latest and greatest from people's comments, but I wanted to try and automate this process.

Basically the goal is to get a quick overview over which coding models are popular on HN. A next iteration could also scan for harnesses that people use, or info on self-hosting or hardware setups.

I wrote a short intro on the page about the pipeline that collects and analyzes the data, but feel free to ask for more details or check the Google Sheet for more info.

https://hnup.date/hn-sota

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jdw64 4 hours ago | parent | next [-]

Interpreting these metrics is quite interesting.

One thing for sure is that while Claude is currently taking the #1 spot in mentions, it carries a lot of negative sentiment due to API pricing policies and frequent server downtime. On the other hand, the runner-up, GPT-5.5, actually seems to have more positive feedback.

Personally, my experience with Codex wasn't as good as with Claude Code (Codex freezes on Windows more often than you'd expect), so this is a bit surprising. That said, the more defensive GPT is definitely better in terms of sheer code-writing capability. However, GPT actually has quite a few issues with text corruption when generating in Korean or Chinese—something English-speaking users probably don't notice. In terms of model capabilities, when given the same agent.md (CLAUDE.md) file, I think GPT is better at writing code, while Claude is better at writing text during code reviews.

Looking at the bottom right, Qwen and DeepSeek are open-source, so they are largely mentioned in the context of guarding against vendor lock-in, which drives positive sentiment. Considering that Hacker News occasionally shows negative sentiment toward China, the fact that they are viewed this positively—unlike US models—shows that being open-source is a massive advantage in itself.

Anyway, one thing for sure is that Gemini is pretty much unusable.

	▲	2ndorderthought an hour ago \| parent \| next [-]
		I like your analysis but I think the open models are genuinely well received not only because of vendor lock in or being open source. 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.
	▲	dgacmu 44 minutes ago \| parent \| prev \| next [-]
		I had a surprisingly positive experience with Gemini optimizing some mathy MPS code. It did far better than claude. 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.
	▲	awesome_dude an hour ago \| parent \| prev [-]
		> Anyway, one thing for sure is that Gemini is pretty much unusable 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)

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2ndorderthought an hour ago | parent | prev | next [-]

Interesting to see the positive sentiment around kimi2.6 qwen3.6 and deepseek relative to the negative. I hope the trend of people appreciating open models continue. They aren't namesakes yet, but it's a higher percentage then I thought it would be. Especially on HN where we are all talking about businesses.

I am upset because now anthropic, openai, meta, etc will continue their smear campaigns here. But I am also happy because it will make HN less useful when they do.

Everything is a give and take I guess. Excited to see where the equilibrium sits

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SilverElfin 13 minutes ago | parent [-]

Is it just “smear campaigns”? Don’t get me wrong - I don’t want big tech or big AI monopolies and appreciate the open weight models. But it’s also true that Chinese companies are basically stealing through distillation and also that they censor to align to CCP rules. They’re problematic in a different way.

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.

	▲	2ndorderthought a minute ago \| parent [-]
		They are all stealing from each other just like how they all stole from us. Grok admitted to distilling from openai for instance. I think it's also unfair to say their success is solely due to stealing data. They are contributing a lot of advances to the literature about what they are doing. It's murky sure. But there are smear campaigns about how people can't trust China too. There's some truth to that too but we can't trust the US either so local models are an interesting way for China to offer us some level of sovereignty.

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chillfox 37 minutes ago | parent | prev | next [-]

Surely "Claude Opus 4.7" and "Claude Opus Latest" should be the same, right?

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idivett an hour ago | parent | prev | next [-]

Thanks for doing the hard work. I've bookmarked this, hoping it'll come handy when new models are released. If you're taking feature requests, I've a few. - Show combined measurements of model makes. Like All claude models vs open ai, Deepseek so on. - Another toggle to remove the neutral section?

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gobdovan an hour ago | parent | prev | next [-]

Before harnesses, I'd fix the methodology/claims. A saner methodology would be to see comments that compare two models, say 'gpt5.5>opus4.7' and infer context ('ctx:frontend', for example). For your current methodology, 'opus 4.6 was very smart, opus4.7 is a disappointing upgrade to 4.6' would make normal aspect-based sentiment analysis consider 4.6 is smarter than 4.6. But considering you have <300 mentions total, probably you'd be better off scrapping some other websites as well. I'd also take out completely the SotA claim and downgrade the mentions to measuring something like visibility rather than performance.

	▲	yunusabd 30 minutes ago \| parent [-]
		That's fair, my immediate concern would be that there would be very few comments comparing any two models, so the data would be very anecdotal. 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 ;)

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Jabbles 4 hours ago | parent | prev | next [-]

Please fix your graph so the names of the models are readable

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marcuskaz 3 hours ago | parent | next [-]

Also, the stacked graph only allows you to quickly see total mentions, really hard to compare negative or positive sentiment across models at a glance.

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yunusabd 3 hours ago | parent [-]

Yep, a toggle to scale all columns to the same height could solve this. I'll look into it when I do the custom graph.

Edit: Done

	▲	marcuskaz an hour ago \| parent [-]
		Much better, nice update!

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yunusabd an hour ago | parent | prev | next [-]

Thanks for the comment, should be fixed now.

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smeej 3 hours ago | parent | prev [-]

Came here to offer this feedback. If I can't see the name of the model, nothing else in the chart really matters to me. I even tried going to the Google Sheet.

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

	▲	yunusabd 2 hours ago \| parent [-]
		Thanks, I replaced it with a custom graph, should be easier to read now.

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brooksc 2 hours ago | parent | prev | next [-]

It'd be interesting to also graph this over time to see how sentiment changes from when a model is released to today.

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Hari2028 31 minutes ago | parent | prev | next [-]

How noisy is the sentiment classification? Feels like that could skew results a lot

	▲	yunusabd 3 minutes ago \| parent [-]
		From the comments that I've checked manually it's pretty good. You can go to the "User Ratings" tab in the Google Sheets and check some comments to get an idea.

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pbgcp2026 2 hours ago | parent | prev | next [-]

So, it's a webpage with 3 paragraphs and a simple chart. It has: 1) terrible color scheme – fine, I switch to reader mode 2) shitloads of JS - fine, NoScript works, page breaks 3) Fancy "design" with simple graph but unreadable X axis labels - fine, I can use screen zoom for that ... to see 3x "Claude O..." LOL are we playing guess-me-over game? 4) ... "LxxxLxxx - Learn languages with YouTube!"

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yakkomajuri 4 hours ago | parent | prev | next [-]

"Prompts an LLM" -> which LLM?

I saw you're using Gemini for the sentiment rating (which I guess you picked because it's not often mentioned and thus "neutral"? lol)

But would be interesting to get more details overall

	▲	yunusabd 3 hours ago \| parent [-]
		It's actually ChatGPT at the moment for the first filtering step, for no other reason than having a code snippet ready that I could point Cursor at (I know, so 2025). The Gemini call is using batch processing, so it's handled differently.

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ranger_danger 3 hours ago | parent | prev [-]

Just FYI this article seems to define "start of the art" as "popular", as measured by "total mentions and user sentiment", without any bearing on the technical abilities or actual usage of the model.

	▲	yunusabd 2 hours ago \| parent \| next [-]
		Calling it sota might be a bit provocative, but what actually is the "state of the art"? We have benchmarks, but those are getting increasingly gamed and don't necessarily reflect the actual performance of a model, see Opus 4.7. So I think it's useful to have real world data from actual users as an additional data point.
	▲	mellosouls 3 hours ago \| parent \| prev [-]
		That's pretty much exactly what the title says. The technical abilities and usage are derived from the commenters usage reflections.