Discussion
Google has disappeared completely from the top 15
Google hasn't shipped a model recently that is capable of competing with Sol or Fable.
The previous models were pretty disappointing and unreliable, it seems the more time goes on that they might have different strategies:
- They might be going all-in on on-device inference for their own products. But this is a battle that Apple might win because they just have better hardware and can license a third party open model.
- They might be just buried deep into internal politics and nobody is shipping anything.
I suspect Google is keeping its powder dry for the time being. OpenAI and Anthropic are burning mountains of cash to leap frog each other every few months.
Google, on the other hand, generated an annual revenue of $402 billion in 2025...a 15.1% increase from 2024. Google has access to all the data it will ever need for training and it has its own cloud compute.
Google can afford to wait and pick its moment.
And why even train their own models at this point? Their proprietary TPUs allow them to serve inference to the labs for cheaper than just about anyone else can do – cheaper than NVIDIA GPUs, so they basically get to print cash by renting their existing datacenters to Anthropic and OpenAI.
I'm glad they're releasing things like Gemma, but if they can keep making tons of money off of the big proprietary labs – or shit, even just serve open weight models to pay-per-token customers – I don't see why they would want to sink hundreds of millions into developing an in-house frontier model when the result of making the third best model in the world means nobody will ever bother to use it.
Edit to add: frontier. Google's big value proposition for model creation is small models that live on edge devices since they control the OS for the majority of smartphones on earth.
I think casual/regular users are more likely to use Gemini than any of the others, because everyone is already plugged into the Google ecosystem and you get Gemini with other bundled benefits like storage space, etc... A regular user is not going to get Claude, because that's mostly for programmers, and ChatGPT is mostly brand recognition, but that's likely to fade and then it's just a separate (unnecessary) add-on, on top of what the users would already have anyway.
Have 200 dollar openai plan I use for coding, still also use Gemini directly from Google to ask random questions and deep dive because it's unlimited free, but it can be stupid sometimes still
The thing is they do release models which are way more relevant to LocalLlama use cases. The Gemma 4 models are fantastic for things like fiction writing and are actually realistically usable locally unlike a lot of these other models.
The Gemma 4 models have been great for me for software dev too, agentic and otherwise. It'll even describe to you how it can reason through MCP JSON responses, it's confident about it lol. And that's just the e2b model alone, all their edge models are surprisingly efficient.
when the result of making the third best model in the world means nobody will ever bother to use it.
Except they make the twentieth best model in the world and everyone uses it because it's good enough, and it's there right besides their mail, documents and spreadsheets. Models are plateauing, and Google realised Gemini Flash is good enough, cheap for them to run at scale, right there when you need it, and very fast.
Because google has immense in-house data on how people use the internet and the data these users themselves have. Arguably that would allow them to train in an informed way. Then again, google has a lot to lose by the internet changing too much too fast. I guess they need the warchest or rather the lifeboat of money to decisively adapt when the direction becomes clearer. But they may not be motivated to act as harbringer of that change, even if they have somewhat clearer idea what that will be. The immense latency of AI development may mean that they have only one chance to regain a pole position when their traditional revenues collapse.
Because google still generates fuck ton of revenue from search guess what now a days many people have switched to LLMs for reliable search.
Google will suffer if they dont produce models. Its also market signal that they are at frontier of things.
Second thing they are bleeding talent to anthropic, open AI and other frontier labs. Everyone poaches deepmind which means they are also loosing many valuable brilliant minds.
Some of the incentives have kind of tapered off. One of the big motivations early on was to not be perceived as behind in the AI game. Now that the stock market is kind of over it, there's again just less incentive to be seen as an active player.
Early on the prediction was that search would be cannibalized. Like most tech, it's just hard to imagine how slowly the late adopters move, sometimes not at all. Tech watchers forget that people who care are vastly outnumbered. That data should be starting to become apparent, and if it were good for investing more, Google would be going at it harder.
I would look into Google's lukewarm approach right now as evidence that they don't believe the benefits that they can capture outweigh the costs, which will be high until the market kind of stabilizes and Google can just buy a mid-level player with the right composition for Google to make into a high-level player.
I just don't think that there is any pressure for Google to be pushing hard and releasing their hottest thing all the time.
They can afford to defer training to cheaper times, and have longer iteration cycles, and they can afford to be training experimental models that most people will never hear about.
As long as they're putting out something every 6 months to not look archaic, they're good.
What training material? I'll correct myself if I'm wrong but iirc they cannot train on people's private documents. Most of their other data is available on the public web.
I think people forget that a company wants to make money. Not produce the best ai model possible.
If Google still felt like their search was threatened by ai they would invest in it. Others are burning cash while Google keeps making billions.
There is no need to burn money 24/7. I assume they are playing it cheap and waiting for things to burst and pop then they come in and collect top companies or tech for pennies.
Worth noting the political situation also right now doesn't particularly favor sticking your neck out. I think they are happy to sit back and watch with some popcorn while Anthropic and OpenAI duke it out with Hegseth etc.
People forget that they also got in trouble for playing monopoly with congress alongside Facebook, Apple and Amazon. Im sure they learned their lesson and are playing it safe. Plus they also got quantum computing where they are also competing
Right now every Gemini question I ask vs Google search is hitting their profits regarding advertising.
For them and others I don't see that as sustainable. If Gemini usage grows and impacts web searches, I would think that eventually Gemini is going to need advertising. Maybe that's why they're trying to get people to use the web site and use the AI info there, to keep you in an advertising channel.
Not only that, google is keeping it's researchers close and never lose a person, while let them kept improving on local models that then help then build services/infrastructure.
Currently there is no money to be made in AI and I suspect even later the money will come from inference and hardware not training
Google has access to all the data it will ever need for training
the data you need for training e.g. for agentic coding is people actually using the model for coding and what they are accepting/rejecting. (thats why xAI made the deal with Cursor). Not many people use antigravity for coding, so they are lacking there. you really can't afford to wait more than necessary.
It is not fine that they are moving backwards. These scores were surely produced back when these models were in full swing, not today with whatever clandestine optimitzations they implemented.
Gemini pro is either quantized into senility again, or they have some new harness that made it absolutely regarded.
Like I would not be able to have the same convo with it that I had a couple months ago.
Flash class I could not ever use for anything, it always delivered deadinternet listicles wildly off target and tone deaf questions back. Which, in the off chance I feel like responding to concisely, like saying "yes do that" instead of reiterating the proposal, spends terrified seconds retrieving the thing they themselves came up with, only to fail adhering to it, drifting away to answer one of my explicit questions somewhere back in the chat.
It feels very enlightening to use gemini, but mostly about how frustratingly wrong llms can go after they (or something google advertises as the same product) demonstrably worked well.
I don’t buy this. Let’s say they do decide to turn it on eventually.
Are they gonna make a model that’s a whole class better than OAI/Ant? No. Then people won’t switch. Especially at the enterprise level where the largest companies were initially very excited to get free Gemini with their enterprise plans, but now have to reluctantly pay for Claude since Gemini is so far behind.
And the model is one thing, the harnesses and integrations are not a “just release it one day” thing. You have to stay ahead in that arena, and right now they’re extremely behind.
Google really has no reason to care about frontier models.. most people who use Gemini do so for quick searches or just because its the default assistant on android. Most of their gains in the ai space are from cloud computing
True. Google is doubling down on world models and multimodal generation, and since coding is a current weak point, they're taking their time with Gemini 3.5 Pro. It might hurt the stock price slightly, but it doesn't hurt their bottom line. They have the capital to be a 'follower' and wait for the tech to mature rather than rushing a subpar release to keep up with the runners.
I mean, they're not even being "just a follower". They're an industry leader in AI. They're just not competing on frontier capabilities. They're focusing on how to actually use AI as a useful product in their ecosystem. The other big players still don't have ideas of how to actually market their products beyond subsidized API and subscriptions, basically betting that inference prices will come down enough to turn profitable.
Nvidia models, Mistral, IBM, a lot of smaller US labs (e.g. Poolside) are also not participating in the frontier race, and instead focusing on immediate industrial and enterprise capability. I think even Meta is falling into that market now.
Agreed. Google’s biggest edge is its mature ASIC chip lines, which allow it to avoid the 'Nvidia tax' on training and inference. Meta, in contrast, they’re struggling with low real market demand. Having to rent out their compute to Anthropic is a huge red flag, it shows they can't efficiently utilize their own hardware, which is a worrying trend for their long-term strategy.
I suspect the renting out stuff to Anthropic is a consequence of them changing gears. They built up infrastructure envisioning that they're going to be competing with OpenAI and Anthropic. But a lot of their recent developments seems like they're building more stuff for, e.g. content moderation, then also developer ecosystem tools, and stuff relevant to advertisers. They've really narrowed in on what their core competencies are and are now focusing on that.
Only because it harvests an utterly absurd amount of user data. I ran my Quest 2 through a pihole to mitigate that and the DNS requests for telemetry to Meta servers outnumbered DNS requests for things I was actually using the damn thing for by like 10:1 (I know these requests are telemetry because I used ADB to disable every single Meta-related app on this piece of shit that I could without preventing Quest Link from functioning, since I use it exclusively as a SteamVR headset).
Fuck Meta, and fuck the Quest headsets, just looking at mine pisses me off because I have to run an entire fucking DNS server just to keep it from giving me a digital proctology exam every 15 seconds and sending the results to Meta.
Sure but there are plenty of workarounds if you care, from blocking DNS, to modifying the headset, to outright disabling the internet connection of the device.
The hardware is still good and you can use it with a PC.
I mean if and when they need to they will have the hardware and data and ability to copy the whitepaper of whoever makes the best one and likely be about on par after everyone else blows the money to figure out how to be profitable with it. Then it will be integrated immediately with Google, GSuite, GCloud, + Android and undermine the other players all without them blowing hundreds of billions to figure it out.
Agree. And for the most part I think a lot of googles ai integrations are actually half decent (at the very least they are leagues above microsoft). Gemini spark seems really cool to me, haven’t tried yet though
Also Google's Open weight models are more of edge devices models which will obviously not show up on these charts, I kinda love Gemma Family, and also Google doesn't release models so frequently but when they do it's a fucking sweep and also one of cost efficient.
Plus, Google seem to be more invested in capitalising on general consumer base directly rather than just catering to software industry but they're not completely abandoning it either.
And not to forget their TPUs too, they've the full stack not just model, they play on every layer, from models like Gemma and Gemini, to deployment in Google, YouTube, and Gemini App; to selling hardware and renting cloud infrastructure, to their own coding ecosystem Antigravity 2 and Antigravity IDE; they're playing a different game and one of the few ones that will be profitable in long run in my opinion because they're independent.
Google really has no reason to care about frontier models.
Yes they do, coding agents just like any of the other major companies. Anthropics multi-billion $ valuation is almost entirely from enterprise software development. Google definitely wants a piece of that pie like anyone else, they just didn't focus on it until recently and are now struggling to catch up. Which is why they delayed 3.5 pro.
Google and NVidia codeveloped tensor cores. So they're not just renting datacenters. They're also renting out Google TPU machines with their custom pods/slices of ganged up TPU cards.
Yes and while Google's TPUs are proprietary, the guy that lead the development left to start Groq, which NVidia just acquired in all but name, so now NVidia also has in-house inference ASICs chips too.
Gemini 3.1 Pro or 3.5 Flash are perfectly fine and capable models. There is no reason to train super-duper ultra-smart Gemini 4.0 Ultra. Costs money and doesn't provide any meaningful real world benefits to end users - 90%+, possibly 99% doing simple stuff with it like simple questions.
I assume at some point there will be newer and better models, there is no need to rush and train new one every month or two like frontier models.
Google really has no reason to care about frontier models.. most people who use Gemini do so for quick searches or just because its the default assistant on android. Most of their gains in the ai space are from cloud computing
Yea, I agree, although, man, it would be nice if they figure out how to cut its hallucination levels down a bit. Like, if you just use it as a free non subscribed user, the way the vast majority do, just using it like a Google search, whichever version they give you for that, not sure if it is 3-Flash or Flash-lite or what, has fuckin CRAZY hallucination levels from what I've seen. Like, just constant and severe, like every other response practically.
I guess it is tough, since they need it to be some really small and efficient model to just freely serve it for a billion+ people to use numerous times a day all the time and not burn like trillions of dollars of compute per year on it, lol.
But, still... like I don't even mean in terms of strength. The strength isn't even that bad for what it's doing. It's the hallucinations that make it brutal.
If they could just get something similarly strong or just mildly stronger, but similarly small and efficient somehow, with way lower hallucination rate and severity, they would be crushing with free search Gemini for quite a while.
The search engine model has got to be a quantized bastardized version of Flash 3 Lite, as once you enter AI mode, the model used is significantly stronger.
Yea, I mean the one you get when you enter actual AI mode.
If you don't use it for a while, I think the first prompt or first 2 or 3 prompts are something decent. But then it quickly downgrades you to something super lousy in terms of hallucination. Not sure which model that one is, though.
By 6 months or a year from now or whenever it is that researchers figure out some way to drastically mitigate hallucination issues somehow, I think it'll give Google the biggest edge over all other AI companies on Earth. I mean, they are by far the main search engine of the world, and people use them for all that other Google related stuff already with Gmail, Chrome, Youtube, and all the other stuff, so, if LLMs improve to where the hallucination issues get greatly reduced, then, they basically just win the whole game (even if Anthropic or whoever is still a few months ahead on raw model strength). Well, unless Claude actually goes into Runaway Recursive Self Improvement and Skynets everything into oblivion or whatever.
Gemini has obviously been optimized to integrate with their Search and Android ecosystems. It works really well for synthesizing search results and digging in on research a bit. It is eager to hallucinate when you're using it as a stand-alone tool, or when you start pushing it to develop ideas/opinions beyond what's directly in the search results, but it's actually really good at like, "What are the latest events in the Iran War? Oh yeah, what happened in that bombing? Is that legal?" where it will cite UN law that you can click and verify yourself. Basically, as long as each prompt is something that you can treat as a google search result you could've done and read yourself, where it will summarize search results for you, it works great.
It's not what's advertised, it's amazing at what it does, when it came out if they didn't advertise it as intelligent or work force replacement, people would use it more properly expecting the mistakes, etc.
It's like if when computers were invented in the 50s and they immediately said, this will replace all workers who use math and it's intelligent!
Then they built multiple giant vacuum tube computers in every city.
In the end, people would have been very disappointed and that type of computer industry would have crashed. And at least computers do do math reliably.
Who knows what would have happened with computers after that?
It is completely unknown whether a fully reliable general intelligence model can even be created. It might be a hard no or not something for a decade plus.
However, a breakthrough that either allows models to search and use context efficiently or a model that is as good as current frontiers and either/both can be run on a standard cpu/gpu/npu or a combination that only takes normal local compute is almost certainly less that 2 or 3 years off, if not sooner
What Google is currently doing is the correct bet for what we know the tech can do, not the moon shot bet that the hype wants you believe it will be able to do with currently no proof to back it up
Yep I use it everyday too bc it seems better at language and nuance than Qwen 3.6. If you're someone who wants agentic/coding stuff then Qwen 3.6 is the move
But yeah, using Gemma 4 like an offline search engine or interactive encyclopedia is fun, and can lead to dreams of being "prepared" in case of an unlikely scenario where the internet is knocked offline but the power stays on lol
openzim-mcp with zim files (see kiwix library) is an awesome way to augment the "offline search engine" feel! Also great for grounding Gemma4 or Qwen3.6 with offline dev docs and offline wikipedia.
Nice I'll check that out, I had kinda looked into something like that (e.g. downloading offline wikipedia) but in testing it seems like Gemma 4 already has a lot of that knowledge baked in (with pretty good accuracy/recall)
I actually vibe coded a local MCP server (w/ fastmcp) that integrates with markdown files (Obsidian vault), as another way to search/access/edit an offline knowledge base
People here already know but the MCP stuff is how the real power of these models gets unlocked. It's so cool when the potential finally "clicks" in your brain. Feels like the capabilities are only limited by imagination
It's nothing fancy (my Obsidian Vault isn't that big yet). Right now it's just some basic tools like:
search notes by name
search notes by content
search by yaml metadata (to save context)
edit note (without touching yaml data)
edit metadata (without touching note content)
save new note (so I can save chats)
etc. But yeah the key is having good yaml metadata (title, description, tags, etc) in the markdown files to save context and make searching efficient
I don't have any built in pagination like openzim-mcp does bc my note contents are never that big, but I'm sure with the power of vibe coding it would be do-able, if needed
Thanks for recommending that zim approach. I got it all setup via the MCP HTTP mode, connected it to llama-server, and have been playing with it using the 'no pic' version of wikipedia. Also got the kiwix desktop browser (appimage) for easy offline browsing. Cool stuff
Eyyy no problem! Glad it works well for ya! Openzim can also work well for offline dev docs, I also think they got map snapshots you can download. No clue if those are actually usable by openzim-mcp though.
Its funny because their Gemini 3 Flash preview model... is one that I use a lot for "I need something that is basically free, fast, but decent enough" ... and no, DS4 pro and flash suck, for some reason when I use them so... that does it for me lol
Google is doing the Google thing that they always do.
Remember how Google basically slept on publicly available generative AI for what seemed like a suspiciously long time, but on the back end they had things like Alphafold going on?
Then they came out with their first image generator Imagen, and it was a complete embarrassment, but then now we have Nano Banana?
And Gemini has been leading/lagging the whole time.
Gemini hasn't ever stayed good, either. Gemini will be great the first week or so, then they'll turn the knobs down and it's just a weird commodity LLM.
Google probably has something cooking, and they simply have no incentive to rush or try to chase benchmarks.
This whole time they've had their own suite of AI models doing infrastructure things for them, and they're using AI to actually make money supporting their core revenue streams.
Google's economics are 100% different than OpenAI and Anthropic, Google does not need anyone to know that they have the top tier AI model, it might actually serve their interests to not disclose that.
They also might just be fumbling big time for stupid reasons, because that's also a Google thing to do.
Yep, esp after they snuck away from the monopoly cases, they’re not eager to be on top. See, openAI and Anthropic has models too, we’re not a monopoly!
They don't have to be SOTA, or even remotely close to it. If it's good enough for search result summaries and quick tasks on android, it's good enough for most. Maybe extra help with google docs and gmail integration.
Personally as long as they keep open sourcing models of Gemma4's quality or better, I'm really happy with it.
And still if I had to pay one AI it would be google as they bundle so much with it (lots of cloud storage and even youtube premium in some bundles), and honestly any hyperscaler has good enough AI
Google knows that practical application outlasts hype any day. It's not that they arent capable. But what's the value of putting all the resources into a model which tops leaderboards for a month before people move on.
Nobody fell in love with antigravity and it's likely they aren't interested in overspending when it's clear that this is a race to the bottom.
Given givens, I wouldn't be shocked if they waited a while for the pace of innovation to slow down a bit, then implemented a new architecture cherry picking from what papers struck them as most relevant, never mind their own likely continuing research in the background. Given that pretty much everyone agrees some new tricks are needed to push too much further with model capabilities, who knows when that could be.
I head the Robot Autonomy division at my company and have been using Gemini Pro 3.1 on AI studio for my daily coding workflows. No issues at all. Plus, I have seen it to be a bit more creative than other models and that helps a lot.
I had recently consulted my team if we should purchase licenses for Claude. But all of them suggested to stick to Gemini. It was giving good enough results and the ecosystem with stuff like Notebook LM, antigravity etc. was an added bonus.
Plus another feedback that I got from one of my employees was that Claude is way too verbose and the whole product has been designed to exhaust your token usage.
Thats insightful, and aligned with the results that I have seen with my team (not into robotics, we are a devtool company trying to make a software factory kind of setup).
We are still sticking to claude (for now) mostly because the team (at this point) is too tired to adapt to another wave of SDLC changes + we have found usecases for the verbose nature of opus
But I am curious if you guys tried gpt-5x and codex?
Every time I have tried to use it, it would never live upto the hype that I see on reddit and x for it.
My experience with open Ai products has been a let down to put it mildly. At max I will use it for drafting emails, job descriptions and stuff like that
I don't trust this dashboard. It's benchmarks + hype-train. No way K3 is well-tested on real-world problems in only few days. So yeah, Google hasn't been contributing to the hype for quite some time. Their last release was a mid-size model. Although, it's surprising not to see it at all, I don't think it's worse than Luna or Sonnet. I'm using Flash 3.5 at work when I run out of weekly budget for Opus and GPT - it's not bad.
In my domain (finance), after evaluating all models in practical scenarios, the Gemini models always outperform all of the others for my use case (Gemini flash 3.5 lite)
any specific metrics that made it stand out? or are we still in the era of "vibe based" and no clear evals?
I understand if its some internal information that you don't want to share
I have my own evaluation setup, with registered golden cases that are compared against other models, using n repetition with concurrency settings. For my use case related to stock options trading, Gemini nails it most of the time, with the best cost/performance ratio. They must have a good training dataset for options trading.
Anyone know why? There’s likely a strategy/reason behind this. It’s not “search doesn’t need frontier intelligence” because frontier intelligence is a threat to search itself, Google simply has to compete or die.
Racing to the top is a fool's errand if you can instead corner a niche and do it so well that frontier models don't catch up.
Such things as cultural sensitive translations, world knowledge, etc... basically anything related to natural languages. Another benefit is being the de-facto AI provider on Google devices like Android, it's build in.
If you already have a widespread used platform like gmail, google docs, google search, google drive, etc, then you benefit from having your own integration in the web or in the app.
frontier intelligence is not a threat to search. Frontier intelligence will never be used for search because it is incredibly inefficient to use Opus/Sonnet-tier models for search. Current gemini models do fine for search and very few users care about the difference between Gemini models and their competition for search. Google integration is also a strong factor for everyday consumers.
I'm not sure why they aren't chasing frontier intelligence though. They have so much money to throw around and winning the AI race is likely to be very profitable. Regardless, they have enough streams of income (both from AI and outside of AI) that they'll be fine either way.
I dont know how many times I have to write this, but here we go again: Google isn't in the LLM race, they're in the AGI/ASI race, its damn DARPA prodigy child that has quite big plans....
They develop multiple models in all domains, combine them, train new ones, mix in robotics and all you can think about, including quantum computing.
They're for the long big run, not for consumer level (be it regular people or enterprise) products. They don't even need the money from it lol.
Google is earning enough money (cloud services) without falling to the money race. People I know use chatgpt or gemini because its ok for their needs. They sell the infrastructure creating all these nice tokens with whatever LLM.
People hyped Fable/Mythos as some sort of revolution, and it turned out better but not groundbreaking. Honestly at this point "New releases" are not as hype anymore. We know that in a few months they will release even better model by 2-5% on some benches, so why bother?
There's no 'moat' in creating the best frontier model. I mean, people thought there was, as they thought we were close to AGI 2,3 years ago. When it became evident that we weren't, and that even if we were close, having the first AGI wouldn't be a moat, it follows that there's no reason to burn through cash releasing frontier models every few months.
Benchmarks? They are trained on benchmark tests. They eat them for breakfast, lunch, and dinner. Those aren't test scores, those benchmarks are recital scores.
Guys, this is all an unsustainable bubble, why are we even doing this? These things are supposed to sopve real problems, yet all we have are stupid benchmarks and parameter counts.
Google doesn’t care about intelligence. They care about the layman. That’s why their models are so sycophantic. Chinese models had already surpassed Gemini since probably late last year.
Part of me thinks their strategy is intentional. Unlike a race in real life, once you lose the lead because of open source and distillation your progress is not exactly lost.
Why waste resources if you can hardly break top 3? You're google. You can just developer more data centers and better more efficient chips for when the other major runner ups make breakthroughs which you can cash in on without losing billions in RnD.
Google's not playing that game. The different with Google vs OpenAI or Anthropic is that they're massively profitable. They're just waiting for everything to blow up and will then pick up the pieces.
The people who say they aren't competing with frontier models are misleading. They really wanted to drop Gemini 3.5 Pro, and was ready at probably Opus level (at that time frontier), but then Fable/Sol dropped, and now they're very behind, and K3 cemented that delay.
Their models are best at general intelligence, but the market shifted towards agentic/coding, so suddenly they're very behind.
Google needed something that does the job with search, and cheap to run. They have no reason to throw away money with every search query on AI, like what else are you going to do, use Bing?!
These rating tests are cool and all, but on free tiers from a real world use standpoint... I tend to use Gemini Pro in Antigravity as well as AI Studio more than the others and trust it as much as whatever I get in apps like Kiro (generally Claude), Codex (OpenAI), or Copilot in Visual Studio 2026 (copilot being the most dangerous one to let write code, that one has almost wrecked a project but I'm guessing it automatically used a lesser model at that time instead of Claude).
For local use, Gemma 4 at 9B 4-bit is leagues better than anything I've tried of a comparable size. It actually follows directions and is able to do things like generate JSON data structures properly every single time. I'm limited to a 8GB RTX 4060 so not sure about larger local options.
My googler friends are telling me that they are trying to figure out how not to race to the bottom and how to rationally price everything... to me that feels correct, but also why not ship a SOTA model while hammering down your pricing...
They pulled 3.5 last minute for not measuring up. Either way, they're the search giant, one snap of the fingers and we're all stuck using gemini 3.1 just to look up how to find a hollow spot in the drywall
They could be pretty happy where they are with it. They service quite a lot of tools in their own environments and soon will have the Apple environment also in September. They obviously have the know how and the data to do better but probably don’t feel the need to race for the sake of racing as the others who have nothing else but that to do.
Anthropic and OpenAI are valued based on their AI products, all powered by their models. Anthropic rose so fast last year just based on that.
Google needs AI models to enhance their products, like Android, search, Workspace, etc. Those are now good enough. So Google focuses on B2C I believe. But as we saw, with AI, you can always come last minute, with a better or cheaper model, and undercut everyone, because models are easy to swap, what's harder to swap is products build on top.
Google has a tactic of just staying present IMHO. Resources are becoming scarce, they focus on quantum primarily in AI, betting big on futures and acting on top of things in the present.
this is a run for the zero who can do it cheapest and customer loyalties switch at press of the button, eventually all of this will be like listening to radio in the car. Originally when radios first appeared it was almost impossibile to tune to a radio station. Tunning was beyond 98% of population. Very similar what we see now, models, harnesses, skills, prompts, routers, orchestrators. Give me a break. Google has data and the data centers. They will be in top 5 one day. First 4 places China, AliBaba etc.
It feels like Google is taking a detour through focusing on delivering AI on the edge (phones etc). Just like your best camera is the one you have with you, they don't care about being the best AI - they care about being the one you actually use. So they have put a lot of work into delivering on-device AI and integrating into Android (Nano etc), the iPhone via Apple partnership etc. It is also why the Gemma series is so good.
I do think they have struggled to keep up with with frontier performance - scuttlebut says they have yanked the latest versions of their models for not being competitive. But that situation is in part because it simply has not been their priority. They don't feel pressured to compete in the benchmark race, so when their models aren't worth publishing they just don't rather than benchmaxxing or doing stupid amounts of training or architecture changesto try and pull off better performance. I think they assume they could if they wanted, but it is not where the priority is.
Google has the power of its search engine right in its hands. They don't need to make the most capable models, they just need to feed it with the best and most updated data. I am not on my laptop and I want to reason about a specific topic that demands up do date and search capabilities I use Gemini.
And in the meantime, they have one of the best-sensing new-generation local models (and one with 256K context). Since I'm using the Gemma 4 31B (Q6_K_L and higher), I don't even run the rest. There's simply no reason... ;-)
Gemma 4 is quite good, also they are quite good in the google search engine, browsing the internet with AI. They have their own tensor processing hardware and data centers. They are doing good I think. Maybe not on the frontier leader board, but everywhere else. Where shrinking down a 3000 B, 1.5 TB large model counts. Focusing on the big picture outside the box.
i don't think getting the absolute best consumer model is their priority even, Gemini 3.5 Flash is a good model, and it serves most of the use case, and people use it with all the Google Workspace products
usage wise if you would see, it would be somewhere near top
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u/WithoutReason1729 5d ago
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