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.

maybe cache this thing my guy you're just doing a bunch of reads

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constructive suggestions

- you have a pretty cheap process here, and HN exposes historical posts by date. perhaps worth running this back the last 2 years to reconstruct a history of sentiment?

- introduce alternative sorts around the net positive/negative sentiments and absolute positive sentiments, similar to State of JS (https://stateofjs.com) - you'll see the gpt outperformance more

- matching of Opus 4.7 and Opus Latest seems sus?

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

It's actually pretty difficult to find a good comparison model because there isn't one. Again, a 14/28 cent in/out model, ignoring cache, it scores just below GPT 5.4 Mini-xhigh (75/450) and Gemini 3 Flash (50/300) in intelligence. It's similar to Gemma 4 31B in some metrics (13/38) including cost, so it's not completely unheard of, but it's pretty notable that virtually everything else in the same region in most benchmarks are going to cost at least 5 times more (much, much more in very output-heavy contexts)

1.) Opus 4.7 via the API is great. Unlike 4.6, I have found the model to degrade far less beyond 120k, even 600k can be relied upon. Task Inference, Task Evaluation, Task Adherence, tool calling, all do very well on my evals. I did however for the first time in a while end my Claude Max subscription because, after their post-mortem [0] I for the first time saw true, reproducible, incredibly frustrating regressions in model output when using Claude Code.

Yes, this was after their post in the last week of April 26 and yes, I have been fortunate enough to never have been affected by regressions up to this point. The model via API with other harnesses provides consistent, useful and high quality output, but the recent changes have become an avalanche of "this requires more than two changes so we should table this for later" and "it seems the subagent finding was wrong and this is not actionable" with a healthy mix of suggestions that clearly are there to safe tokens, but go against clear instructions. I understand that they are compute constraint but as someone who until recently has never maxed out their weekly and nearly never their 5 hour limits on the Max 5x plan, these changes are not just frustrating (and make reasonable users think the model was nerfed rather than the harness) but also cost more as I now have to prompt four times and thousands of tokens more for a task that previously the same harness with the same model did far more efficiently. I regularly check the numbers and yes, by trying to be more efficient, they made what I am costing them far higher, going beyond what I pay for the subscription. Ironically, and I must emphasise this, I did not have regressions before, which suggests some major luck in A/B testing at least.

2.) GPT-5.5 is amazing, a true jump I have not seen since GPT-5 and far more than even GPT-5.4 is approaching the way Anthropic models have handled task inference, which also has lead to far reduced reasoning needs. I very much like it, with the exception of the reduced context window and degradation in compaction. GPT-5.4 did compaction so consistently well, that the 272k standard window before the price increase was of no concern and going beyond it was reliably possible. With GPT-5.5, the cost per token is doubled and compaction is far less reliable, leading to loss of task adherence and preventing task completion in certain cases. I am aware GPT-5.5 is a new pretrain (though how new given frontend is still abhorrently poor and has been since Horizon Alpha which I maintain was worse than GPT-4.1) and am hopeful they can integrate some of the solutions they were leveraging for GPT-5.4 compaction, but until then, it remains a model great for very challenging and complex blockers, but not a GPT-5.4 drop-in replacement.

3.) Kimi K2.6 is great for the API price, efficient, fast and does very well on all my metrics. I very much like it, far more so than Deepseek V4 Pro, any Qwen, Z.AI or Meta model and I truly am impressed. Composer 2 has shown how you can take the base even further given the right data and if I had to pay exclusively API pricing without any subscriptions, I think I'd have no problem leaning on K2.6 for most needs. It is what I'd love to see from Mistral or Apple and shows that one can't just succeed in a few narrow areas (Z.AI with tool calling, Deepseek with world knowledge, Mistral with being European, etc.) but provide a balanced product across all areas as an open weight company. I just wish they'd expose Agent Swarms via the API, there are a few experiments I'd like to try.

[0] https://www.anthropic.com/engineering/april-23-postmortem

Subjectively, it seemed like DeepSeek V4 Pro had the highest hype/performance ratio (meaning high hype for lower performance). Whereas MiMo V2.5 Pro didn't get much attention despite being the top dog in the open weights world, not even an honorable mention in your chart :( ...

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

Now it seems like it's come circle from the other direction, too. We always had fandom elements in computing nerd culture. Editor wars. Language wars. Framework wars. Now that software tooling has become nearly human-like, mercurial, unpredictable, inconsistent in performance and experience from week to week, software developers have turned into sports scouts and ESPN talking heads, going so far as to make continually updating live power rankings the way commentators try to predict in season which team is looking most like they'll win the championship that year. You're in the position talent evaluators were in roughly the late 90s, relying mostly on eye test and rough proxy measures of raw potential. Simon Willison applies the pelican test the way draft combines put athletes through shuttle drills and test vertical leap to try and predict how well they'll do in real gameplay.

It leaves me wondering when we'll have the Bill James style analytics breakthrough in software talent evaluation or if such a thing is even possible. At least with athletes, practice can make them better and injury and age can make them worse, but you can't just silently swap out an entirely different mind and body under the same name and face. You guys are trying to assess the performance of constantly moving targets that can and do change capabilities and characteristics on a daily basis.

I've been experimenting with the 26B-A4B model with some surprisingly good results (both in inference speed and code quality — 15 tok/s, flying along!), vs my last few experiments with Devstral 24B. Not sure whether I can fit that 35B Qwen model everybody's so keen on, on my 32GB unified RAM.

However I think I may be in the minority of HN commenters exploring models for local inference.

kimi...?