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That and/or the business case isn’t as clear when serving enormous models? You’re constantly stuck in a red queen’s race where your profitability window is increasingly measured in weeks because the Chinese are right behind you.

For small models (which are probably distilled from their big ones) you can serve them economically all the time and not hemorrhage money.

That's the only explanation that makes sense. If it was frontier but cost or compute were limiting factors, they'd release it at an obscene price for the bragging rights. Google doesn't care that much about alignment, and I don't think it's likely to be significantly different than 3.5 anyway. The only reason it would need to be soft-canceled is if it's terrible, and has to end up in a ditch like Llama 4 to avoid shareholder panic.

Yes agreed - I wrote this up a while back https://martinalderson.com/posts/whats-going-on-with-gemini/

My view then was they are optimising the models for inference ability on their own hardware AND use cases, which is often speed and time to first token.

They've somehow seemed to end up with terrible compute shortages, which again is surprising given how good Google is at infra deployments AND have their own hardware. From rumors out there they are turning down enterprise deals for Gemini because they don't have the compute.

The problem is they're falling further and further behind on frontier class on coding especially, and since I wrote that article it's got even worse with open weights models undercutting them on price AND intelligence.

There was some recent reporting that a July release of the Pro model got pushed back for exactly that reason. Its performance was not good compared to the OpenAI/Anthropic big models. They are having a lot of problems with posttrain.

I personally doubt that.

It would be a shame if they cannot beat Kimi K3 or Qwen3.8 Max, both of which are claimed to be Fable-like. If that is true, it will be [or would be] the first time a major American lab falls behind a Chinese competitor.

This is the feeling i get too. Cant produce quality, but can produce something that is super fast...so take the wins where they are.

> focusing on what they can get wins in like speed instead

Speed as a differentiator has always been Google's thing. They (used to?) show the microseconds it took to query & rank web-scale search results. Chrome, notoriously, focused on speed at the expense of resource use. The very many efforts to efficiently speed up Android & its runtime since its inception, and so on...

> their big model underperforms chatgpt 5.6

Possible but TFA claims:

  We have started our most ambitious pre-training run yet, for Gemini 4 ...

Didn't they already acknowledge this?

Paywalled article, but the headline is basically all you need: https://www.bloomberg.com/news/articles/2026-07-16/google-ge...

or they just don't want compete in coding space ???

they have search,youtube,android,office suite like gmail,maps,spreadsheet etc

coding is the least of their problem/priority

I think it's a safe bet that Google seems more interested in making a model that improves Google rather than making a model that improves workers.

Fast, light weight, ok intelligence. Perfect for serving 20B+ prompts per day mostly surrounding banal human things.

OAI and Anthropic's cloud spend can cover the revenue gap, as Google is already capturing a large chunk of those guy's revenue.

Not to mention internal use cases, such as prediction-related tasks like serving ads.

AI overview is just a summarization of the top 2-3 results. Of course at Google scale that will still need a ton of compute, but the requirement for generating an overview is many orders of magnitude lower than asking the same question in Gemini.

The AI mode on Google search is pretty impressive. Helped me figure out what a bunch of stuff I was seeing out the window was while traveling.

Microsoft also seems to be working in this space. They recently released this:

https://huggingface.co/microsoft/bitnet-embedding-0.6b

It’s a small multilingual embedding model designed for things like search, RAG, and semantic similarity. It supports a fairly large context window and is designed to run efficiently on a CPU in a GPU starved world.

The interesting part is that it builds on BitNet, using ternary weights of -1, 0, and 1 instead of the usual floating-point weights. That should make indexing and searching large amounts of text much cheaper without giving up too much accuracy.

In terms of open models, Gemma 4 beats the pants off everything else to the point that paying for APIs becomes hard to justify. Qwen has the meme-share for coding, but it feels much less well rounded. I have no doubt that Google have both the infrastructure and the expertise to curb stomp everyone else, should they resolve in earnest to do so.

Lest we forget, "Attention is All You Need" came from Google.

It's rumored that Gemini 3.5 flash has a >50% margin, and I'd imagine 3.6 flash is even higher.

I do not think OpenAI or Anthropic are actively chasing margins - though, Anthropic is supposed to be profitable on some form of non-GAAP accounting...

I suspect Google isn't really interested in seeing how far it can get dragged into a race of selling dollars for $0.25, and is more interested to see if it can stay in the race selling $0.50 for a dollar - when everyone else is losing or barely breaking even.

That "TPU advantage" might be slowing Google down (though likely not as much as their internal bureaucracy).

Porting CUDA-based research, debugging, and overall experimentation speed is likely slower.

The GPU is still king for training.

They basically don't exist in the currently most profitable LLM market (coding).

Yes, subs like codex are heavily subsidized. But API billing has massive margins and that's what enterprises pay.

Maybe, but they said they have “started” the Gemini 4 pretrain. So not having done any significant pretrain in a year or so seems odd to me.

It's hard to tell the difference because they nerfed Pro to oblivion, it used to be much, much better model (even for non-coding/chat)

I've found that having good source material (markdown, dependency source, search results for agents) for the models to draw on significantly improves information accuracy. Definitely worth investing in this side of "harness engineering", don't rely on facts burned into weights

Besides Apple apparently making Siri AI model agnostic, the choice to go with Google was almost certainly for practical reasons. Google is a low-risk established player that already has a long work history with Apple. Google also isn't in an existential battle to establish themselves, Gemini still amounts to just another project at Google. There is tangible non-zero risk that either OAI or Anthropic will be gone in 5 years, or will be forced to leave Apple high and dry to save themselves. There is almost no risk Google will be in either such position. And worst case scenario, Google has incredibly deep pockets should Apple pursue a "refund."

What Apple wants out of Google is Siri that runs at 8gb ram and isn’t a horrible embarrassment that feels like a primitive markov chain. Given how good Gemma 4 is, Google can squeeze some serious performance in small models. Whether they can make bleeding edge models is irrelevant to Apple.

As someone on the Apple beta.. the model is almost completely irrelevant to the experience. Apple has gone and done Apple things by nerfing the experience so completely that almost any model in the past year would be fine. I still reach for ChatGPT/Claude/Grok constantly instead of the AI toy that Apple calls the new Siri.

Apple isn't counting on their model to be a frontier coding and cowork model. Gemini is perfectly fine for the tasks that new Siri is supposed to be doing.

The bar was quite low with Siri that adding any model would make a huge difference.

How does your comparison work? It places Gemini 3.6 Flash Medium above GPT 5.6 Sol High and Fable 5 Medium, which makes me skeptical because that... would be making headlines that I'm not seeing right now.

You should really provide more on your methodology because as it stands, it really doesn't pass the sniff test. GPT-5.6 Sol on Low beats Fable Medium by 10% and Gemini-3.6 Flash then beats them both? Fable is number 20?

This does not match any lived experience or developer experience.

It'd be helpful to know _what_ you're testing and break that out by dimension. You mention randomly selected questions. How does that work?

With n=22 and binary pass/fail, the 95% confidence interval on a pass rate spans roughly (+-)15-20 percentage points. There's just not enough data ironically, for this leaderboard to mean anything. Ranks #5 through #25 are statistically indistinguishable

>verbose

GLM defaults to max effort btw

https://docs.together.ai/docs/glm-5.2-quickstart#reasoning-e...

Not really. Gemini 3.6 Flash actually cost $0.01 more per task, compared to GLM 5.2.

https://artificialanalysis.ai/models/gemini-3-6-flash

according to AA it's not better than GLM 5.2 and that's surprising to me

Yeah that’s the joke :p

It's literally the meme of the MS org chart pointing guns at each other.

The GCP team wants their slice, the other team wants some otjer slice, and so on. Everyone wants some crap for their promotion package.

It's no wonder Meta has shit the bed even worse.

It's also why Google still releases actually decent, useful models despite the product being such a hilarious mess. A lot of the time Gemini models have actually been better as production LLMs as part of LLM-based production applications than OpenAI and Anthropic models when it comes to the complete cost:quality:latency:adherence picture. And they still are. We have products in prod that use Gemini because they're better than any other model at the specific task. But we wouldn't dare use it for anything coding related, or even just as productivity tool to rely on, because as a consumer product it's a joke.

As someone who's been using Workspace as a personal email account for over a decade this has been such a struggle forever. Just lots of odd limitations to feature sets all over the place.

When they swapped Google Assistant for Gemini as the default voice provider in Android Auto it was so annoying. My wife's non-work space account can get Gemini to do the normal things like play music and what not, but my Workspace one can't do much of anything at all. I can talk about nearly any random topic with it, but getting it to change the playlist, nah, can't help you there.

It's no surprise to me to see them fumble actually supporting a lot of the consumer features of Gemini into Workspace.

Apparently you cannot turn off using your data as training data with gemini. This is in line with Google's general privacy policies and it's seeming need to create a stasi file on every human.

What does "leading from the front" get them?

There is absolutely no loyalty when it comes to coding. Nothing could be more common than people threating to jump ship whenever another frontier or open source model comes.

Google is clearly able to keep growing their free and consumer and small business use cases. Unlike corporate coding, we actually have evidence that solo and small businesses can actually see productivity gains.

Anthropic and OpenAI need to stay dancing like mad, because it's their source revenue which underpins their investments.

Why does Google need to shove something at the top at the same desperate cadence? Other than "recursive self improvement leads to AGI" it seems perfectly fine if they push out something dramatically better every year and half.

They simply don't have the leadership to lead. And it starts from the top.

Google promotes people based on shipping features, not making good software. So this breakage means someone is shipping features.

You should be happy for them.

I don't think it's that early tbh, agentic coding has ~90% adoption in the US.

Claude Code has largely won individual developer mindshare and has been on top ever since it came out. The benchmarks change, but almost nobody opts to use anything other than Claude IME when I ask them. Enterprise is more competitive since they care about costs and other things, but developers leaning towards Claude puts a thumb on the scales there.

The product doesn't have much lock in, so it is possible to dislodge Claude, and Anthropic could (and some may argue is likely to) just shoot themselves in the foot again and again and again, but Google has never been particularly good at enterprise sales, and they have never actually been at the frontier of intelligence.

I think Google's incentives have mostly about building models for their products, which makes them focus more on the cheap end, and while they need that, it feels like the Innovator's Dilemma is biting them here.

I own a lot of Google stock from working there in the past and have been quite happy about their trajectory up until the last 6 months, but I am getting pretty antsy about their AI story these days.

Early days or not, Google fucking deleted my IDE and wiped my settings. It took them days to roll out a fix, by which point I had migrated off Antigravity.

I don't think that's true if the reason a company has left the vendor for given model by making it hard to buy. Enterprise IT is enough of a pain in the butt that people will forego the new shiny to avoid the old painful unless it's genuinely better. As you say though, the best frontier model flips regularly, so companies won't go through the hassle of deploying a model if it's proved horrible to do in the past. They'll just skip that model because their current one is fine.

I guess? Maybe I'm alone here but I don't feel like Fable is particularly more useful than Sonnet most of the time. I feel like the LLMs are good enough for the majority of uses and the hyper expensive premium ones are way into diminishing returns. At this point with Kimi being as good as it is, if they jack up the price any more I'll just go open source.

Model quality is only one aspect, the bigger problem is making it work in a fully integrated enterprise platform, and Google has always been lacking when it came to the latter.

At this rate, if Google has a flagship model, you're better off plugging it into a competitor's tooling than hope Google figures out how to use it.

This is hotly debated and completely unclear. Let's say Anthropics Opus models cost the same to serve as GLM 5.2. GLM 5.2 is 4.4$/MTok while Opus is 5.6 times more expensive. Assume that GLM 5.2 is served at essentially zero margin. Then Anthropic has >80% margin on API pricing. So even if an average person with a subscription pays only 20% of the API price of their usage, Anthropic makes money on subscriptions.

And the real numbers could be better for Anthropic. It's feasible Opus models are actually cheaper to serve than GLM 5.2 because Anthropic have optimized the hell out of inference.

I think the subscriptions pay them back in spades because the same dev who maxes out their subscription on their personal account transfers that exact behaviour over to their enterprise work and - guess what - it's all billed per token there. This is a large reason why corporates are reeling from the cost right now, I think.

Makes sense, subsidizing tokens doesn’t seem like a great strategy for a public company.

Possibly, but aren't the tech giants positioned to win a war of attrition? Then again, they're more likely to sit on that cash and wait for the opportune time to buy a frontier lab.

they're subsidized if you max them out, i'd imagine most users are paying $20 for maybe $2-5 of tokens.

anthropic probably has more customers that use more of their sub, but for open ai where a lot of their subs are consumers through chatgpt.com, they have a lot of free money to work with there

The subsidizing thing is repeated over and over without proof. Personally, I doubt they're actually subsidized.

> google being their usual terrible selves at anything consumer-product related

And yet millions of people around the world are using their stuff, very often using only their stuff.

I am going to ask a very direct question and only because I am curious.

Why do you care? Why would you spend your own money to a multi trillion dollar company so that they win their own "war" against another multi trillion dollar company?

Please don't get me wrong, I know the question can seem a bit negative, I am really just curious.

Life gets simpler and better as soon as you stop giving money to Google.

It’s so silly that individual people still use their shit. Corporations, I understand - they always choose the most mediocre stacks and tools by default. But why people choose to bring the mediocrity of Google into their lives is beyond me.

More likely an acquisition.

I have friends and family who use Gemini, but entirely because their Pixel phones came with a year of it for free. No other reason, and they will most likely never pay for it.

Yup, it is often mind-boggling how people here are detached from reality. Millions of people around the world rely on Google services, Gemini in all probability is the most used model consumer-wise, Google receives unimaginable stream of data on even smallest habits common people have etc.

But hey, Gemini makes shittier job than Fable at producing my shitty "app" no one cares about. It is so over for Google!

That's kinda like asking why someone might complain about Chrome's memory usage in a thread about a new version of V8.

From the posted link: "3.6 Flash: Our workhorse model that delivers better coding, knowledge work, and multimodal performance."

What is Google's recently released AI coding product?

"Your new models are great, but uh... How exactly am I supposed to use them?"

That is, given a lot of users' contexts, the way they can and the way they want to use these models are increasingly disjoint.

You don't see what Google phasing out an AI product subscription has to do with a Google AI product release?

You and a few other people, but enough people still appreciate the bit that I'm going to keep doing it.

They're easy enough to skip - click the little "-" icon and you'll collapse the entire sub-thread.

At this point, it's kind of a hackernews thing. Simon posts them as a single comment in the relevant thread. It's okay for this place to have a little bit of a sense of community, and you can just ignore the comment.

But how else am I supposed to know when we've reached AGI, until I see an absolutely flawless pelican?

All of the pelicans so far have had really weird flaws / quirks so I am always a little interested to see how well these models perform at this task, since I've seen all the past pelicans and have some anchoring.

Seeing a truly flawless pelican would tell me that the model has true visual reasoning capabilities as well as good taste.

Its a nice benchmark. Like hearing the ice cream truck on a summer day.

I feel the same way. It was fun at first but has gotten tiresome. Does anyone actually use these models to generate SVGs?

I think you're underweighting the Pelican test.

Not only does it give you a super easy-to-grok understanding of the model quality just by looking at the image, but when you compare tokens and costs (both input and output), you really get a good, simple COST x QUALITY evaluation across models.

Simon explains it well: https://simonwillison.net/2026/Jul/16/kimi-k3/#what-can-we-l...

Simon, you should put up a summary table page that you update after every release.

All the models do this well. It's a test that tell us nothing at this point.

My 2 cents: you don’t have to look at the pelican if you don’t want to.

Here is a different opinion. I’m always looking forward to see the pelican whenever a new model is released. It’s plain simple to understand, memorable, subtle enough in terms of details, and my favorite part is that you have been doing them consistently long enough for it to be useful for comparing almost everything with anything.

Hard disagree. I love a little bit of whimsy (which I feel the world is lacking more and more everyday) from Simon everytime a new model is announced.

Disagree. They're a nice tradition, but besides that, they're a useful way of eyeballing improvements. I realise labs are likely to be training for Pelicans - but if they're all training for them, the differences in the results are as indicative as they were before labs trained for them.

The 3.6 Flash pelican is just about the best I've seen.

Yeah. It's something I can do myself in a couple seconds if I want, also on more varied SVG scenes. If this is going to be a benchmark people turn to I'd like to see more effort put into it than just a one-sentence prompt.

Perhaps freshen it up and extend the test by feeding the model the rendered output so it can iterate once. Assuming a multi-modal model.

I love them. Keep 'em coming.

I'm happy for Simon to post what he wants, when he wants. He's earned it.

Vibe code an extension that autocollapses any post mentioning pelicans and by simonw?

Do something instead of complain

Your comment reads very pedantic with a hint of jealousy. The pelican and xbox controllers are great ways to see how well it can follow direction dealing with svg a difficult format for LLMs to use and testing their spatial vision awareness.

I find Simon's work informative and entertaining; the last thing he can be accused of is low effort. The Pelicans are just a bit of fun icing on top.

I'll split the difference. When it's a blog post there's usually an interesting observation or two, but if it's totally automated? Maybe just do the ones with a post.

Yeah and it's surely in the training data by now. Long past time to stop.

It's just how he is. Prior to LLMs he was cramming a datasette link into every thread. Downvote and move on.

I like seeing the pelicans, it's a tradition.

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A new model arrives. The pelican, with uncanny commercial instinct, is never far behind.

Sponsored blogs and paid newsletters are after all, notoriously poor at subsisting on silence :)

Could also be that they are pricing it at levels where they actually make money. Without seeing the behind the scenes compute cost on all of these its hard to really judge.

That being said with any open model we of course do know the total cost (or estimate)

In my tests, 3.6 Flash is NOT more token efficient, so it actually ends up costing more than 3.5 Flash, even with the output price reduction.

EDIT: It less less verbose in final output though, but it reasons more.

I assume the optimization comes when you have long-running tasks with many tool calls, and by reasoning more, it reduces the number of tool calls needed.

It seems that they're trying to push up-market, or at least they were.

Given the extremely competitive releases of GLM 5.2 and DeepSeek V4 (both pro and flash), I don't think there'll be appetite for it.

Pricing often reflects what the vendors (expects) the customer is willing to pay. It seems that Google is still trying to find their niche in the market.

What is a “knowledge cutoff” ?

—Ah, got it, it knows more about recent times.

That reasoning didn't stop them from releasing this batch of models, admittedly that may be less face to lose away from the flagship position.

> It’s multimodal though.

Sure.

And how does that make your day better? I know it does not improve my work in any way shape or form.

I'll take a better coding model that's not multi-modal any time.

If I need an LLM to do images or sound, I'd rather use a dedicated one instead of a jack-of-all-trades-master-of-none model.

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It's based on the Artificial Analysis "Intelligence Index vs. Cost per Intelligence Index Task" here:

https://artificialanalysis.ai/#intelligence-comparison-tabs

Differences in token "density" are accounted for by pricing per task

could you explain how to optimize prompt caching or point to a doc about it?

its not always that simple. dropping in a new model is trivial, but highly specific workflows may rely on specific _invisible_ aspects of a model. when that model gets deprecated, the workflow needs to be rebuilt/re-tuned to work with a different model.

google's inability or unwillingness to provide stable timelines for model deprecation makes it risky to build complex workflows using their models

You would be surprised how much of a difference the model makes for certain niche tasks.

For my use case, `gemini-3.1-flash-lite` is ~20% higher accuracy than the next best model of comparable cost (considering both proprietary and open-weight alternatives)

"Intelligence" being what, math? Coding? Unfortunately there's a billion use cases for LLMs whose performance is not at all captured by the popular benchmarks they're all trying to maxx.

There are plenty of 3rd party providers hosting deepseek models, if you don't want to use the 1st party API. 3rd party providers are generally slightly more expensive, but still quite cheap compared to other models of similar vintage and size.

sadly it's not multimodal

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Shame. I'm on the $20/mo Gemini Pro plan because the 5tb of cloud storage and the youtube premium lite were good enough perks, and my coding complexity needs were light enough for me to overlook Claude or Codex. But Antigravity is working better than Jules and it's basically giving me a taste of what I'm missing and it's harder to justify not trying out the competitors.

> Could it be that they have to serve their models to billions of users?

And how is that different from their competitors exactly?

How so?

Their shop is linked to in the bio. They don't seem to have a privacy policy up on the site. When in the checkout form it links to the generic Shopify privacy policy. No hints to the fact that uploaded images are processed by third parties, as far as I can tell. That should be corrected for sure.

Yes this is extremely unresponsible if so. Fingerprints are legally protected biometric data in most juristictions.

AA coding index has been updated to use DeepSWE, Terminal-Bench v2, and SWE-Atlas-QnA.

Whats a better option for AA Coding Index?

Antigravity is now split into 2 Apps:

'Antigravity' which is an agent-first editor layout where you don't see your code and just prompt it.

'Antigravity IDE' which uses the Windsurf/VS Code editor, which is still what I primarily use in my day-to-day.

I hope they never retire the IDE, I don't think I can get used to prompting an AI Agent without being able to see my code to help workout what needs to be done.

(Rate limits * capability of the model) / cost

Knowledge Graph + automated transcriptions of almost every YouTube video = giant untapped moat of data

gemma 4 thinks joe biden is president

Free plan, the default tier without requiring a subscription. If you use through the Gemini App or gemini.google without paying anything, the model used is 3.6 Flash.

Rankings for text are here https://arena.ai/leaderboard/text

For comparison of Free Tiers: - Gemini serves 3.6-flash (rank 12) - ChatGPT serves 5.5-Instant (rank 23) - Claude serves Sonnet 5 (rank 27) - Meta AI serves muse-spark-1.1 (rank 5)

While Meta AI serves the better ranked model, it doesn't end up working that well for other things. For example, if I ask "help me buy a new raincoat", it ends up suggesting a Cambodian website, whereas Google is well integrated with Google shopping. It doesn't have the same integration with GApps outside of Gmail/Calendar. A few other email connectors are available.

Claude has one of the best interfaces with connectors, skills, and plugins galore, but the model and limits are restrictive on the free tier.

Gemini, as far as I know, I've never hit a rate limit on Flash.

I believe Gemini is going to gain market share through the free tier funnel while serving models as cost-effectively as possible. People are going to use Gemini because they use GApps and Google.

ChatGPT and Anthropic are going to be competing for the API/Business users, but for everyone else they are going have to become Google before Google becomes them.

Sure, but there's no sota alternative from Google. That's it, and its beaten by GLM 5.2 on every measure except somewhat speed.

I find that quite staggering. GLM is open weights

You're right, should've gotten some LLM to summarize it instead of skimming.

>boy

Seriously?

3.6 Gangsta Pro

Did you ask me a question and then answered it yourself in the very next sentence?

Anyway, given that both Gemini and OpenAI have 3 sizes of models, one would think Google compares their medium size to OpenAIs.

No I'm not, I expressed it...

I prefaced it with that to simply say I knew it'd be an unpopular opinion.

Thanks, I'll give that a try.

I mean like an ai agent doing some sort of HR work, not a coding agent. Very few businesses are trusting an autonomous agent.

I agree, I see how Gemini itself with a usable harness can be excellent. But in agy with forced eager compaction (~125k with 3.1-pro, ~200k with flash) a kind of laziness and forgetting shows through that leads to an endless sequence of stopgap instructions, even with rigorous GEMINI.md and isolated task delegation and a good task tracking system. Agy is basically useless for more complex problems as far as I am concerned, at least when used somewhat autonomously as one could expect from claude. For strictly mechanical one-shot tasks it might be fine. I've spent way too much time working around these limitations instead of just continuing to use claude. Hoping that things would have improved with flash 3.6 I feel that it's actually worse in following instructions, and always acts even when just asked a question. If just agy offered a better experience and got rid of the terrible forced automatic eager compaction.

PS: That opus-4.6 via agy works so much better points in another direction though!

That contradicts this:

https://www.wsj.com/tech/ai/chinas-xi-touts-open-source-ai-a...

So who even knows.

They don't need to be open weight in the long term; once there's an open-weight Fable-level model with 1M context it'll be pretty much good enough for all coding tasks, no need for new models.