Comment by onlyrealcuzzo
1 day ago
Does anyone troll these releases and cherry pick random metrics other companies would cherry pick to show how amazing their models are?
There's like 8 million benchmarks. Every release, every model randomly picks 5-10 where they win in everything except 1, to make it look like they aren't randomly cherry picking benchmarks they probably benchmaxxed for.
Ultimately I think the only way you can trust benchmarks is if you build them yourself and keep them secret from the AI labs.
There are different levels of "cheating" on benchmarks. The worst would be just literally putting them in the loss function during RL, I assume the major labs are not cheating at that level. And I am sure they are making a genuine effort to keep the benchmark content out of the training data.
But, ultimately it seems implausible that they completely abstain from benchmarking their model until they are about to release it. Even if they did do that, the benchmark is still ultimately a part of the outermost feedback loop. So these models are all, to _some_ degree, benchmark-solving machines.
I think all we can really do is live with the model for a while and develop a subjective feeling about its quality. This shouldn't be surprising, nobody believes that coding interviews work, we all know that you just have to work with someone to figure out if they're a good programmer. As AIs become more human like it's natural they should get harder to evaluate.
This is a bit awkward, it puts us in quite a weak position as consumers.
Maybe to some extent you can get a meaningful signal from sentiments on HN etc, but:
- There must be some amount of manipulation going on of this
- Even if it was fully organic, it's highly likely that your experience will differ materially from the median online nerd, because AIs are bizarre things that respond in unpredictable ways to intangible things.
https://arena.ai/leaderboard - I’ve found this company is a pretty good ranker - not sure their exact methodology but during day to day programming with Claude / gpt models I’ve felt qualitatively what they report
Also check mine[0], basically random private tests/questions and an ok-ish methodology, testing mostly for general intelligence than coding-specific tasks.
I built it for myself, to test which models to use via OpenRouter for my n8n agents. Currently actually still using gpt-5.3-codex for many things, as its pricing is really good in production (due to how their token caching works).
Gemini models still have the best intelligence (when asked any questions, most likely to get it right), but in production they still have many failure modes[1].
[0]: https://news.ycombinator.com/item?id=48230368
Every model release you'll post this, and every time I'll be there to point out how it's completely useless (for reasons you've shared are intentional)
It does things like place the old Gemini 3 Flash above the more capable 3.5 Flash and Opus 4.5 - Opus 4.8 and gpt-5.5
At least, until hopefully one day HN has a rule about accounts that derive 99.9999% of their engagement with the site from shilling a personal project.
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Have you seen https://deepswe.datacurve.ai/blog? This is the closest to a vibe check i’ve felt even with the open models.
This actually looks like a really good test.
There are many benchmarks all for specific use cases but with them the difference seems to be in extreme points (93% vs 92%)
I think that, that tracks but still, it was refreshing to see a benchmark which I can help make better opinions about.
Surprised about Mimo v2.5, within artificial-analysis and other benchmarks, the difference between Mimo and deepseek seems very partial and a lot of focus/(hype?) is on Deepseek
But mimo seems like an interesting model and they are having some crazy discounts too.
Deepseek is valuable for the research community because of how open they are but absolutely crazy to think how Xiaomi basically pulled up in creating Mimo given that they didn't have anything till quite recently.
Either way, an interesting benchmark, also a plus point for giving golang some decent representation equal to python/typescript.
I think that there are sets of things which resemble something like normal benchmarks where open source models can be absolutely fine and for a very small fraction or more technical things, the benchmark that you linked starts to be better projected so it depends upon the scale of complexity but its good to see how models compete given enough complexity. definitely fascinating.
I would be interested to see more models compete on this test. The current range is still a bit limited as compared to other benchmarks but OSS models like Kimi/mimo seem to only be 3-4 (at max 6 months) behind closed source.
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No way is Muse Spark generally better than offerings from Google and OpenAI. I actually find arena to be amongst the most useless indicators
I think their "code" ranking is biased towards visual aesthetics more than raw coding as the voters are just asked which generated website they prefer.
I'm finding it a little hard to believe that GPT 5.5 is in 11th place for webdev, outranked by models like Kimi, Qwen, and Z.ai. I'm not saying it's not true (I have noticed GPT being less smart in recent weeks), but this is very different from my expectation.
On paper it's one of the best because it's meant to be blind comparison of your own prompts. However if you are someone who geeks hard on one or a few models, you learn their "personality" and can recognize them in a blind test.
If you don't know their methodology, or anything about it why do you think its a good ranker?
It's interesting they only included 6 metrics this time. Opus 4.7 had 12, and 4.6 had 13.
Of the metircs they reported for 4.7, for 4.8 they excluded BrowseComp, CharXiv Reasoning, CyberGym, GPQA Diamond, MCP Atlas, MMMLU, SWE-bench Verified. The last 4 were almost always mentioned in previous Opus releases.
Their Cybergym score is reportedly awful because of the cybersecurity nerfing. https://x.com/i/status/2060046843023630841
Gonna assume it's because they barely budged or moved downward and most of their reported benchmark results are probably within sampling errors...
They will release a system card, and you can then confirm or disconfirm your assumptions.
I would take all benchmarks with a grain of salt. I don't really use them. What's it supposed to tell me? "5% smarter", what does that mean? My experience will differ. Just try it!
I doubt Anthropic internally sets as a goal to improve this or that benchmark - it's just a way to visualize progress. They probably have much more complex metrics internally.
On this note, is there a benchmark aggregator to compile all benchmarks in a single large grid?
I find this site useful https://artificialanalysis.ai/leaderboards/models
At least they show competitors in any benchmark, compared to OpenAI which likes to pretend that there isn't any competitor.