https://artificialanalysis.ai/models/gemini-3-8-flash shows an intelligence score of 59, the same as Opus 5 medium!
Wow - for a flash model this seems to benchmark powerfully. Remains to be seen what it is like to use.
With a score of 59, Gemini 3.8 Flash is in eighth place, falling behind even Grok 4.6, Kimi k3, and GLM 5.3.
Are you implying Google or Artificial Analysis are reporting false numbers? What's your source?
Model size also can not be inferred by tokens/sec for a multitude of reasons, but to showcase two examples, Opus 5 and Sonnet 5, as well as Gemini 3.1 Pro Preview and 3.1 Flash have each very comparable output speeds when using the same deployment as a basis for comparison, despite it being very likely that within their generation, the former are larger than the latter. Feel the need to mention this, as I unfortunately stumble upon so many poorly reasoned, speculative hype post trying to infer model size via utterly unreliable metrics, not based in actual data.
It’s like comments below arguing about the reasoning levels not normalized to some metric (like cost, output token amount or duration) but just the labels or high, max, medium, etc. Those mean almost nothing even when comparing models based on the same pretrain (just compare GPT-5.4 to GPT-5.2), they mean less than nothing comparing different labs releases.
https://arxiv.org/html/2604.24827v1
The short of it is by using hard facts knowledge that is difficult to compress, and then quizzing models on these facts and calibrating against a bunch of open models, you can kind of feel out the size of closed models.
That’s a massively wide, inaccurate and at best barely informative range, demonstrating that even the most well thought out method will yield little usable information.
Additionally, I got some private evaluation taking a similar approach towards gauging models in topics I’ve found either over or underfitted by labs. If we just used that to rank models (not get a potential size range but just a rough order) Thinking Machines Inkling would need to be lager than Fable 5.
>Considering how much memory a server is using, when evaluating models that you'll never have access to in order to host yourself, doesn't really make sense.
This entire sentence makes no sense given what is being discussed.
intelligence/model size is not a useful metric for a black box user.
intelligence/cost and intelligence/speed is a useful metric for a black box user.
Yes, it's cool, but as a black box user, the amount of memory a model is using on a server that I do not own has exactly zero practical use to me.
Cheers!
Nothing here is false, you are simply confused. You either didn't read what they wrote in its entirety or decided to reinterpret what they did write.
All you had to do is go hover your mouse over "Models" in the top bar, hover over Claude Opus 5 and and click on medium: https://imgur.com/mlRCrt1
When you do that you arrive on this page: https://artificialanalysis.ai/models/claude-opus-5-medium
The gemini flash page for reference: https://artificialanalysis.ai/models/gemini-3-8-flash
You have to be an incredibly dishonest person to see a 59 on both pages and say "the initial reported numbers were false and this was simply pointed out. You're changing the subject".
Just wow. Someone actually said this.
Further, opus 5 medium outputs 4x fewer tokens to achieve the same result, negating a lot of the speed difference.
These folks must laugh themselves to sleep. This whole industry hoodwinked the masses. It’s impressive.
Not sure on consumer/product use though
It's almost across the board better than Terra at less than half the price. 3.9 is likely to approach Sol at the 1/10th the price.
Hopefully OpenAI releases Astra first, and it's not only better than Sol but significantly cheaper, too.
Then I tell Opus to read the audit report and implement what it agrees with.
Flash is really good at this, and it is blazing fast in Antigravity CLI. Easily 10x faster than Opus.
Can't wait to try 3.8 Flash. If it's good enough, maybe I'll switch Flash to primary and make Opus the auditor.
In india, my telco gives me google ai pro for free. And agy with flash goes a long way.
It looks more like Google execs losing their mind and pressuring researchers to put DeepSWE directly into the training set.
It's clearly been "dealt with" already. When it launched we had interesting gaps and definitely differences. Now every new release is "crushing it".
The result is the same as the previous Gemini 3.6/3.7 Flash days: Claude could always note much more problems in Codex's plan and implementation than Gemini could - the ratio is like 10:1.
I occasionally switch the roles between Codex and Claude, and result is the same, Codex could always catch much more problems in Claude's plan and implementation, than Gemini could.
So I am guessing in a relatedly complex codebase, Gemini is much less effective in acting as a guardrail (or a senior engineer/team lead) than the other SOTA models.
It would be really useful to observe this in a benchmark vs. the more common “go implement this, or fix this bug” type benchmarks that seem to be prevalent.
A couple of months ago (before opus-5 and gpt-5.6 sol), The ratio of problems caught by codex/claude vs gemini was more like 2:1 to 3:1. But now it seems codex and claude have made huge leaps and gemini is more or less staying put.
Another observation, Gemini's review on code is more critical now, but its review on design plans is still quite agreeable - it tends to approve Codex's design plan immediately, while Claude could often pick out a bunch of problems in the design plan in the first round of reviews.
...on Medium reasoning. Claude Opus 5 (high) is the default in e.g. Claude Code and scores 61. Still very impressive.
So, yes, maybe it's still not - but this would be the only time it would be highly suspicious / obvious benchmaxxing / obviously bad benchmarks.
anthropic really needs something to address the cheaper end of the market before they get left behind. Sonnet 5 sucks, and Haiku hasn't been updated in a year. meanwhile we've got gemini flash, luna, and GLM5.3 all delivering 90% of the performance for a small fraction of the cost. paying $25/mTok is going to start looking pretty silly soon.
I find GLM5.3 so much better than Sonnet it is not even funny.
Sonnet behaves like a cheap model while being very expensive.
For example, it's not even close to Opus 5 on Terminal-bench 4.0, 19.1% vs. 51.8%.