[foundry27]

I started doing some experimentation with this new Deep Think agent, and after five prompts I reached my daily usage limit. For $250 USD/mo that’s what you’ll be getting folks.

It’s just bizarrely uncompetitive with o3-pro and Grok 4 Heavy. Anecdotally (from my experience) this was the one feature that enthusiasts in the AI community were interested in to justify the exorbitant price of Google’s Ultra subscription. I find it astonishing that the same company providing free usage of their top models to everybody via AI Studio is nickel-and-diming their actual customers like that.

Performance-wise. So far, I couldn’t even tell. I provided it with a challenging organizational problem that my business was facing, with the relevant context, and it proposed a lucid and well-thought-out solution that was consistent with our internal discussions on the matter. But o3 came to an equally effective conclusion for a fraction of the cost, even if it was less “cohesive” of a report. I guess I’ll have to wait until tomorrow to learn more.

[mnmatin]

They might not have been ready/optimized for production, but still wanted to release it before Aug 2 EU AI Act, this way they have 2 years for compliance. So the strategy with aggressively rate-limit for few users make sense.

[novok]

wheee, great way to lock in incumbents even more or lock out the EU from startups

[dataviz1000]

Several years ago I thought a good litmus test for mastery of coding is not finding a solution using internet search nor getting well written questions about esoteric coding problems answered on StackOverflow. For a while, I would post a question and answer my own question after I solved the problem for posterity (or AI bots). I always loved getting the "I've been working on this for 3 days and you saved my life" comments.

I've been working on a challenging problem all this week and all the AI copilot models are worthless helping me. Mastery in coding is being alone when nobody else nor AI copilots can help you and you have dig deep into generalization, synthesis, and creativity.

(I thought to myself, at least it will be a little while longer before I'm replaced with AI coding agents.)

[epolanski]

Your post misses the fact that 99% of programming is repetitive plumbing and that the overwhelming majority of developers, even ivy league graduates, suck at coding and problem solving.

Thus, AI is a great productivity tool if you know how to use it for the overwhelming majority of problems out there. And it's a boost even for those that are not even good at the craft as well.

This whole narrative of "okay but it can't replace me in this or that situation" is honestly between an obvious touche (why would you think AI would replace rather than empower those who know their craft) and stale luddism.

[benreesman]

They're remarkably useless on stuff they've seen but not had up-weighted in the training set. Even the best ones (Opus 4 running hot, Qwen and K2 will surprise you fairly often) are a net liability in some obscure thing.

Probably the starkest example of this is build system stuff: it's really obvious which ones have seen a bunch of `nixpkgs`, and even the best ones seem to really struggle with Bazel and sometimes CMake!

The absolute prestige high-end ones running flat out burning 100+ dollars a day and it's a lift on pre-SEO Google/SO I think... but it's not like a blowout vs. a working search index. Back when all the source, all the docs, and all the troubleshooting for any topic on the whole Internet were all above the fold on Google? It was kinda like this: type a question in the magic box and working-ish code pops out. Same at a glory-days FAANG with the internal mega-grep.

I think there's a whole cohort or two who think that "type in the magic box and code comes out" is new. It's not new, we just didn't have it for 5-10 years.

[burnte]

I have similar issues with support form companies that heavily push AI and self-serve models and make human support hard. I'm very accomplished and highly capable. If I feel the need to turn to support, the chances the solution is in a KB is very slim, same with AI. It'll be a very specific situation with a very specific need.

[Melatonic]

This has been my thought for a long time - unless there is some breakthrough in AI algo I feel like we are going to hit a "creativity wall" for coding (and some other tasks).

[zyngaro]

Curious to know what are those challenging programming problems are. Can you share some examples?

[LeafItAlone]

> It’s just bizarrely uncompetitive with o3-pro and Grok 4 Heavy.

In my experience Grok 4 and 4 Heavy have been crap. Who cares how many requests you get with it when the response is terrible. Worst LLM money I’ve spent this year and I’ve spent a lot.

[danenania]

It's interesting how multi-dimensional LLM capabilities have proven to be.

OpenAI reasoning models (o1-pro, o3, o3-pro) have been the strongest, in my experience, at harder problems, like finding race conditions in intricate concurrency code, yet they still lag behind even the initial sonnet 3.5 release for writing basic usable code.

The OpenAI models are kind of like CS grads who can solve complex math problems but can't write a decent React component without yadda-yadda-ing half of it, while the Anthropic models will crank out many files of decent, reasonably usable code while frequently missing subtleties and forgetting the bigger picture.

[qingcharles]

It's just wildly inconsistent to me. Some times it'll produce a work of genius. Other times, total garbage.

[Closi]

It's not particularly interesting if Deep Mind comes to the same (correct) conclusion on a single problem as o3 but costs more. You could ask gpt 2.5 and gpt4 what 1+1= and would get the same response with gpt 4 costing more, but this doesn't tell us much about model capability or value.

It would be more interesting to know if it can handle problems that o3 can't do, or if it is 'correct' more often than o3 pro on these sort of problems.

i.e. if o3 is correct 90% of the time, but deep mind is correct 91% of the time on challenging organisational problems, it will be worth paying $250 for an extra 1% certainty (assuming the problem is high-value / high-risk enough).

[lucianbr]

> It would be more interesting to know if it can handle problems that o3 can't do

Suppose it can't. How will you know? All the datapoints will be "not particularly interesting".

[thimabi]

> I find it astonishing that the same company providing free usage of their top models to everybody via AI Studio is nickel-and-diming their actual customers like that.

I agree that’s not a good posture, but it is entirely unsurprising.

Google is probably not profiting from AI Ultra customers either, and grabbing all that sweet usage data from the free tier of AI Studio is what matters most to improve their models.

Giving free access to the best models allows Google to capture market share among the most demanding users, which are precisely the ones that will be charged more in the future. In a certain sense, it’s a great way for Google to use its huge idle server capacity nowadays.

[hirako2000]

I'm burning well over 10 millions tokens a day on free tier. 99% of the input is freely availzble data, the rest is useless. I never provided any feedback. Sure there is some telemetry, they can have it.

I doubt I'm an isolated case. This Gemini gig will cost Google a lot, they pushed it on all android phones around the globe. I can't wait to see what happens when they have to admit that not many people will pay over 20 bucks for "Ai", and I would pay well over 20 bucks just to see the face of the c suite next year when one dares to explain in simple terms there is absolutely no way to recoup the DC investment and that powering the whole thing will cost the company 10 times that.

[827a]

Similar complaints are happening all over reddit with the Claude Code $200/mo plan and Cursor. The companies with deep VC funding have been subsidizing usage for a year now, but we're starting to see that bleed off.

I think the primary concern of this industry right now is how, relative to the current latest generation models, we simultaneously need intelligence to increase, cost to decrease, effective context windows to increase, and token bandwidths to increase. All four of these things are real bottlenecks to unlocking the "next level" of these tools for software engineering usage.

Google isn't going to make billions on solving advanced math exams.

[Fade_Dance]

Agreed, and big context windows are key to mass adoption in wider use cases beyond chatbots (random ex: in knowledge management apps, being able to parse the entire note library/section and hook it into global AI search), but those use cases are decidedly not areas where $200 per month subscriptions can work.

I'll hazard to say that cost and context windows are the two key metrics to bridge that chasm with acceptable results.... As for software engineering though, that cohort will be demanding on all front for the foreseeable future, especially because there's a bit of a competitive element. Nobody wants to be the vibecoder using sub-par tools compared to everyone else showing off their GitHub results and making sexy blog posts about it on HN.

[petesergeant]

> Similar complaints are happening all over reddit with the Claude Code $200/mo

I would imagine 95% of people never get anywhere near to hitting their CC usage. The people who are getting rate-limited have ten windows open, are auto-accepting edits, and YOLO'ing any kind of coherent code quality in their codebase.

[amelius]

It could be that your problem was too simple to justify the use of Deep Think.

But yes, Google should have figured that out and used a less expensive mode of reasoning.

[danenania]

Model routing is deceptively hard though. It has halting problem characteristics: often only the smartest model is smart enough to accurately determine a task's difficulty. And if you need the smartest model to reliably classify the prompt, it's cheaper to just let it handle the prompt directly.

This is why model pickers persist despite no one liking them.

[dweekly]

"I'm sorry but that wasn't a very interesting question you just asked. I'll spare you the credit and have a cheaper model answer that for you for free. Come back when you have something actually challenging."

[raincole]

Interestingly Gemini CLI has a very generous free quota. Is Google's strategy just overpricing some stuff and subsidizing the underpriced stuff?

[sunaookami]

It doesn't, it's not "1000 Gemini Pro" requests for free, Google misled everyone. It's 1000 Gemini requests, Flash included. You get like 5-7 Gemini Pro requests before you get limited.

[pembrook]

This is the fundamental pricing strategy of all modern software in fact.

Underpriced for consumers, overpriced for businesses.

[ebiester]

I've found the free version swaps away from pro incredibly fast. But our company has gemini but can't even get that - we were being asked to do everything by API key.

[svantana]

I suspect that the main goal here was to grab the top spot in a bunch of benchmarks, and being counted as an "available" model.

[llm_nerd]

They're using it as a major inducement to upgrade to AI Ultra. I mean, the image and video stuff is neat, but adds no value for the vast majority of AI subscribers, so right now this is the most notable benefit of paying 12x more.

FWIW, Google seems to be having some severe issues with oddball, perhaps malfunctioning quota systems. I'm regularly finding extraordinarily little use of gemini-cli is hitting the purported 1000 request limit, when in reality I've done less than 10.

[ifwinterco]

I'm not in the AI sceptic camp (LLMs can be useful for some tasks, and I use them often), but this is the big issue at the moment.

In order for agentic AI to replace (for example) a software engineer, we need a big step up in capability, around an order of magnitude. These chain of thought models do get a bit closer to that, although in my opinion we're still a way away.

However, at the same time we need about an order of magnitude decrease in price. These models are expensive even at the current price tokens are sold at which seems to be below the actual cost. And these massive CoT models are taking us in completely the wrong direction in terms of cost

[profsummergig]

The part I cannot understand is why for many AI offerings, I cannot make out what each pricing tier does with a quick glance.

What happened to the simplicity of Steve Jobs' 2x2 (consumer vs.pro, laptop vs. desktop)?

[starfallg]

The rate limits are not because of compute performance or the lack of. It's to stop people from training their own models on the very cutting edge.

[golfer]

What was the experimentation? Can you share with us so we can see how "bizarrely uncompetitive" it is?

[iamronaldo]

Bizarrely uncompetitive is referencing the 5 uses per day not the performance itself

[riskassessment]

I'd be interested in tests involving tasks with large amounts of context. Parallel thinking could conceivably useful for a variety of specific problem types. Having more context than any specific chain of thought can reasonably attend to might be one of them.

[ankitml]

I have ultra. Will not be renewing it. Useless, at least have global limits and let people decide how they want to use it. If I have tokens left, why can't I use it for code assist?

[andsoitis]

it turns out that AI at this level is very expensive to run (capex, energy). my bet is that AI itself won't figure out how to overcome these constraints and reach escape velocity.

[int_19h]

Perhaps this will be the incentive to finally get fusion working. Big tech megacorps are flush with cash and could fund this research many times over at current rates. E.g. NIF is several billion dollars; Google alone has almost $100B in the bank.

[crowcroft]

Mainframes are the only viable way to build computers. Micro processors will never figure out how to get small and fast enough for personal computers to reach escape velocity.

[petesergeant]

> it turns out that AI at this level is very expensive to run (capex, energy)

If it's CapEx it's -- by definition -- not a cost to run. Energy costs will trend to zero.

[twobitshifter]

our minds are incredibly energy efficient, that leads me to believe it is possible to figure out, but it might be a human rather than an AI that gives us something more akin to a biological solution.

[ramoz]

Uncompetitive how, what task and Eval?

Gemini is consistently the only model that can reason over long context in dynamic domains for me. Deep Think just did that reviewing an insane amount of Claude Code logs - for a meta analysis task of the underlying implementation. Laughable to think Grok could do that.