Their newest model wasn’t really SOTA. And honestly fable 5 was the most human like model I’d ever tried. It was an incredible jump.

And recently lots of Claude users at r/ClaudeAI are noticing Opus 4.8 has really increased in capability. Not new things but maybe redirected compute. It just feels like one of the best models ever, maybe because the compute that was previously assigned to Fable has been redirected? It feels incredible.

Thank God. I'd rather companies ship something when engineers say it's actually ready rather than when the suits want something to show on stage to pump their egos and career exposure but turn out to be a massive disappointment covered in fluff.

Although it does feel very embarrassing for Google who invented transformers and has more money than both Anthropic and OpenAI combined, to fall behind them at the LLM race.

More seriously, Grok has serious problem with bias. Since its an activist model its judgment cannot be trusted.

Example: ask 50 different models if Elon Musk should be elected as the next US president. 49 will tell you this cannot happen since he was born South African. One will tell you this is an excellent idea.

https://artificialanalysis.ai/articles/glm-5-2-is-the-new-le...

The idea of "falling behind" when you can leapfrog each other every six months leads me to believe it has to be more than just "falling behind" for one cycle. It's a culture, process, red tape, focus, or mandate problem of some sort. Something not as easily correctable preparing for next launch.

Define "better". I guess it depends on what you're using it for. I use it almost daily as an alternative to google search and it's great for that, but I think it's absolute garbage for coding and reasoning.

For questions related to coding, solving Arch Linux and WINE Lutris issues, helping me with MXLinux issues, and wifi issues on an old rooted huawei tablet running LineageOS, it was consistently wrong, constantly giving out confident but outdated or misinformation, or hallucinating stuff while gaslighting me. Every time I would point out it was wrong, it would re-check and keep apologizing and then repeat giving me wrong answers, and then apologising again and so on. It doesn't matter what prompts or jailbreaks you give it to get 3.5 Flash to chew longer on complex problems for better reasoning and accuracy, it just defaults to being lazy and giving you the quick and easy answer from its weights, which can be totally wrong. Same for asking it to write me a cover letter based on my resume and the job description I wanted to apply to. It massively sucked at that too and made up a bunch of unusable fake sounding BS.

Basic free tier ChatGPT 5.5 would blow it out of the water on all of those tasks. Hell, even Grok free is better at that, it gave me a one-shot Arduino code that blew Gemini 3.5 flash away.

3.5 Flash seems tuned to just eyeballing basic answers to general purpose questions that resemble Google searches like "give me a recipe" or "give me a workout plan", or "what's the difference between Arch and Fedora based distros", not to solving complex issues that require cognition and accuracy. That's what the 3.1 Pro is better for according to Gemini. Oh and it is also gaslights you by starting the answers with first telling you how amazing things from your question are, which is insanely annoying but I guess Google's A/B testing found out the majority of Average Joe midwits love it when "the AI" reinforces their choices and decisions like a fake friend.

I think Google just doesn't care about being the SOTA for coding, reasoning and accuracy, since they're in the ads and search business for everyone, not in the agentic coding business for pro-sumers, so if the answers are some hallucinations that sound "good enough" to its clueless search user base, but is at least dirt cheap to run on their datacenter hardware, then it's already more than enough for them and they can all it a day.

Meanwhile OpenAI and Anthropic don't have search and ads monopolies, so they need to perform well at certain task for people and businesses to give them their hard earned money for them to survive. For them, nailing stuff like coding and writing accuracy is an existential threat, not a hobby sideproject like it is for Google.

Google seems more interested in fast models that can quickly turn responses, which kind of fits with a company that needs to serve AI on a mass scale.

Fast answers, using their search as grounding, that can parse keywords and spit out a few ads is where Gemini Flash is going to head. That, and the agentic actions stuff they showed off at I/O with Google shopping, ordering food, etc. Speed is important there.

https://ads.openai.com/

https://openai.com/index/new-ways-to-buy-chatgpt-ads/

It out-performed every model that wasnt a max/ultrafrontier of some sort, except for the one that the article was extolling the virtues of, including grok high. you could make a good argument that deepseek is a better value, but gemini flash is when bundled is already pretty accessible.

nowhere did i claim that flash was better than fable or 5.5xhigh.

I don't care about someone else's charts, i care about my own lived experiences. Benchmarks can be gamed to get to the top of charts.

Read my comment again please. I think I was pretty clear with detailed examples on where Gemini sucks and where it's good at.

>nowhere did i claim that flash was better than fable or 5.5xhigh.

And nowhere did I claim that. I said even basic GPT and Grok are better than Gemini at reasoning tasks. Again, read my comment again.

Bruh, do you know what a personal opinion is? You don't need to care about it, and I never said I don't care about other people's opinions, I just said that chart which is based on non peer reviewed information, doesn't match my experience so without further peer reviewed proof to back it up, I don't care about it as my expire shows otherwise.

Would you believe any graph that tells you the sky is brown when you go outside and see that it isn't?

I've definitely noticed it, at least for doing backend C#/dotnet. Its insanely good, I haven't had to babysit much at all this week.

Is Gemini good at writing code? I am sure it is. But where is their Codex? And no, antigravity isn't it.

Is Gemini good at making visualizations? I am sure it is. But where are artifact or visualise skill in gemini.google.com similar to what's available on claude.ai?

What is an average user going to do raw model capability if the product surface isn't expressive enough?

Antigravity CLI is quite decent, it's a huge step up from Gemini CLI (like, for example, it actually fucking works) and has some genuine advantages over Claude Code. Does Codex have something over both of them? I haven't tried it.

But the model just fucking sucks. Before I switched to Claude for personal stuff a few weeks ago, I was like "damn model capabilities are really slowing down" but no, it's just Gemini that's slowing down.

Will have to see if 3.5 Pro is any good when that comes out. But it feels like they would be attempting to catch up to Opus, not to Fable.

FWIW issue is never really about the code it writes it's about general intelligence. Gemini hallucinates like it's 2024, fails to follow instructions, and goes down wildly wrong debugging paths. Opus just gets the job done, first time, every time. With Gemini it feels like "I _am_ glad this intern is working for me but I'm tired of babysitting him" and with Claude it's like "this new PhD guy can replace me soon".

- the /artifact thing is quite useful (don't think CC has it?)

- the /tasks is a bit better than CC's equivalent

- there are a few built-in skills that I haven't found CC equivalents for in the built in set (but the fact that I haven't sought out 3rd party versions shows you they aren't very important).

And more generally it does a better job of making the agent available. When Claude is debugging something complex and running a bunch of experiments it's often unavailable for like 20 minutes at a time, you only have /btw. Whereas AGY tends to more aggressively use timers and background jobs.

But now I wrote that out, I realised it's probably just as much of a system prompt thing as a harness design thing. Coz Claude _can_ operate that way too.

Anyway, like I said none of these come anywhere near balancing out the model quality gap.

So maximal safety at all costs is in itself a cost. They can spend billions on AI but that spend is down the toilet if the user bounces because the AI's persona is a relentless politically correct scold.

I went from spending 20-60 dollars a day in api fees down to like 5 dollars a day cause I have had to limit my use to things I know it performs reliably on.

When personal finance is not the bottleneck anymore, the new criteria becomes "vision" and "stacked talent".

Where did I hear this before? Very few people feel that a specific amount is ever enough.

A friend made mid-8 figures after exit and became a highschool math teacher.

In general: if money was everything, wouldn't the top faculty in every top school be quitting and joining FAANGs ? Who would want a professor's job, making a mid-level SWE amount?

Google appears to be falling behind in the AGI race. The leadership (MBAs) do what clueless leadership always does: they start cracking the whips, bring the knives out. People like Jumper, Shazeer, Dean, etc. are not built for fighting political battles; they're built for solving tough problems! What MBAs don't understand is that researchers at this level put a tremendous amount of pressure on themselves; and this internal pressure is far beyond what any external entity can apply. So, when MBAs start hassling top researchers with "so ... what have you done lately?" and "what are you working on? Is it important?" and "when are we getting AGI (with a smirk)" type of questions, then it feels really really grating: If I knew a sure path to the goal, I would work 24x7 to get there, dammit!

That was when they realized the deep learning was largely unnecessary, and they could just use their massive compute resources to brute force the problem space.

Proving that we would greatly benefit from using our compute resources for science rather than showing ads, and then we just kept showing ads.

Extreme investor desire for return on capital investment, and quickly

It also feels like Anthropic is the new Google though. They actually try to not be evil, and are actually at the frontier of new tech.

Demis is the CEO of DeepMind, it's completely different.

Jumper.. the AlphaFold team left & made Isomorphic. I was always surprised that Jumper hadn't gone with them.

Seems like everyone here is easily fooled by the Anthropic hype. After the IPO, Anthropic won't be like the daycare it is today.

Their main competitors are the chinese labs which are racing all their prices down close to $0.

If he walks the talk, I really do not understand how either OpenAI or Anthropic is going to justify the twelve-digit valuations they are hoping for. They will just be some people who bought a domain name and rented some GPUs.

The question is, how far ahead will the frontier models be in 6 months? if it's still 6 months, open weights might have a fable equivalent model, and the frontier models will be on upwards towards ... essay, or novel, or bibliography, or whatever the next name is.

Moore's law is dead now, so at some threshold purchasing the GPUs to run the biggest and newest model hurts you more than whatever rent you could've extracted from it.

When we get there, why would you want to run a closed model that you can't control, with restrictions you can't remove, that a company can take from you or silently nerf without telling you?

Not a bad playbook. If you’re important to the company, leave and start your own company. Then play the M&A game and you can clean up nicely.

I've been in NLP since the LSTM days and it's hard for me to look at LLMs and not just think they are incredible. It's truly a different level of expressiveness. So much of capabilities research is pointing to LLMs effectively learning a world model.

RLVR is also proving really effective. It is hard for me to imagine a world in the future where LLMs aren't at human level performance across a wide variety of tasks.

I fully acknowledge that current LLM labs have a financial interest in people believing AGI is very near, but from what I'm reading in the literature and seeing myself experimenting with the SOTA models it doesn't seem totally unreasonable.

What evidence are you seeing that makes you confident that AGI in the soon-ish future is a complete myth?

Unfortunately an LLM is not AGI, and video recording is not text.

Nobody will buy an AI with enough context to develop critiques of their own organizational structure.

> Thanks John for an extraordinary partnership and wonderful collaboration over the past 9 years! What we achieved with AlphaFold changed the world, and showed the field what was possible with AI for science and medicine, lighting the way for how AI can benefit humanity.

Gemini 3.1 flash was actually an amazing model to code with and their 20 dollar AI plans had solid value, but they locked it all behind 429s, needless gatekeeping of clients and poor product differentiation even among internal offerings. Users moved on. To claude for the best product, to OpenAI for the non gatekept API access. It’s hard to bring them back.

Their devs are not incompetent so there must be some extreme dysfunction for that to be possible at the org level, where the IC is either not allowed to fix the bugs or doesn't want to fix them.

Either way that dysfunction is probably not limited to just the Gemini UI team.

Why are we keeping tabs on researchers moving around?

You're talking about them like people talk about the NFL trading players or (football) soccer teams making recordbreaking player transfers.

someone further down wrote

> Anthropic legit builds one the strongest if not the strongest IC team in the history of computational technology.

What a weird thing to say. It's not a team sport where you support 'sides'.

there are also real implications. assuming money is not the only factor in moving to anthropic, it does help guide insight into where innovation might be and where to put your AI spend. a decision which could result in real returns for individuals and companies.

It is if you have equity!

If the next big breakthrough in AI comes from Anthropic, good chance it comes from some genius you’ve never heard of who decided to work there because of [famous researcher].

I‘d argue it’s more meaningful than sports, which makes it more interesting to follow.

Of course the sports aspects are silly, but maybe they‘re just a fun way to keep track. Happens to other popular companies in the tech sector as well.

Nothing but nice things were ever heard about him. In a school of egos, his was approachably humble [†]... reasoning and facts seemed to make him tick. My twin actually had classes with him (small school), but also didn't know him well... but knew he was a swell guy. None of his IT tickets were ever typical "rude rich Vandy kid" – he could solve/delegate most his own problems.

So glad to see Vanderbilt secure yet another humble Laureate (Muhammud Yunis won his Nobel while we all were attending, I believe the previous alumnus-so).

Keep it humble, fellow knowledge-seeker Human John. Howdy from KissamKissam.

[†] most Obviously Brilliant-types "come off" as I_went_to_Harvard arrogant; not John ("awe" of his obvious brilliance?)

But yeah conspiracy theoretical approach would generate lot of discussion on this rather normal thing happening.

Nobody really knows or cares about Mr Jumper there. Congrats on his new role in converting humanity’s achievements into slop.

There's no place in polite company for comments like this, and you could be trolling. But since you also simply might not know:

John Jumper won the Nobel Prize in Chemistry in 2024 for his contributions to AlphaFold and is a Fellow of the Royal Society. Among the more influential scientists of our time.

https://en.wikipedia.org/wiki/John_M._Jumper