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Gemini 2.5 Deep Think

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Re: Gemini 2.5 Deep Think

#121

Post by Fade_Dance »

ccozan wrote: post #95
dweekly wrote: post #78

Earlier quoted context omitted.

"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."

Actually why not? Recognizing problem complexity as a fist step is really crucial for such expensive "experts". Humans do the same. And a question to the knowledgeable: does a simple/stupid question cost more in terms of resources then a complex problem? in terms of power consumption.

Do they not do this in some general way behind the scenes? Surprising to me if not.

Re: Gemini 2.5 Deep Think

#122

Post by holowoodman »

asaddhamani wrote: post #19

I find it interesting, how OpenAI came out with a $200 plan, Anthropic did $100 and $200, then Gemini ups it to $250, and now Grok is at $300. OpenAI is the only one that says "practically unlimited" and I have never hit any limit on my ChatGPT Pro plan. I hit limits on Claude Max (both plans) several times. Why are these companies not upfront about what the limits are?

Because they want to have their cake and eat it too.

A fair pricing model would be token-based, so that a user can see for each query how much they cost, and only pay for what they actually used. But AI companies want a steady stream of income, and they want users to pay as much as possible, while using as little as possible. Therefore they ask for a monthly or even yearly price with an unknown number of tokens included, such that you will always pay more then with token-based payments.

Re: Gemini 2.5 Deep Think

#123

Post by Fade_Dance »

827a wrote: post #113
foundry27 wrote: post #34

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…

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 window…

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.

Re: Gemini 2.5 Deep Think

#124

Post by Closi »

foundry27 wrote: post #34

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…

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).

Re: Gemini 2.5 Deep Think

#126

Post by irthomasthomas »

SubiculumCode wrote: post #92
irthomasthomas wrote: post #40

You can spin up a version of this at home using simonw's LLM cli with the llm-consortium plugin. Bonus 1: Use any combination of models. Mix n match models from any lab. Bonus 2: Serve your custom consortium on a local API from a single command using the llm-model-gateway plugin and use it in your apps and coding assistants. https://x.com/karpathy/status/1870692546969735361 > uv tool install llm llm install llm-conso…

1. Why do you say this is a version of Gemini deep think? It seems like there could be multiple ways to build a multiagent model to explore a space. 2. The covariance between models leads to correlated errors, lowering the individual effectiveness of each contributing model. It would seem to me that you'd want to find a set of model architectures/prompt_congigs that minimizes covariance while maintaining individual a…

I didn't mean to suggest it's a clone of Deep Think, which is proprietary. I meant that it's a version of parallel reasoning. Got the idea from Karpathy's tweet in December and built it. Then DeepMind published the "Evolving Deeper LLM Thinking" paper in January with similar concepts. Great minds, I guess? https://arxiv.org/html/2501.09891v1

2. The correlated errors thing is real, though I'd argue it's not always a dealbreaker. Sometimes you want similar models for consistency, sometimes you want diversity for coverage. The plugin lets you do either - mix Claude with kimi and Qwen if you want, or run 5 instances of the same model. The "right" approach probably depends on your use case.

Re: Gemini 2.5 Deep Think

#127

Post by redox99 »

danenania wrote: post #114
martinald wrote: post #111

Earlier quoted context omitted.

Yes but prompt evaluation is far faster than inference as it can be done (mostly) in parallel, so I don't think that's true.

The problem is that input token cost dominates output token cost for the majority of tasks. Once you've given the model your prompt and are reading the first output token for classification, you've already paid most of the cost of just prompting it directly. That said, there could definitely be exceptions for short prompts where output costs dominate input costs. But these aren't usually the interesting use cases.

That's usually not the case for thinking models. And usually hard problems have a very short prompt.

Re: Gemini 2.5 Deep Think

#128

Post by mclau157 »

Asking AI to create 3D scenes like the example in the page seems like asking someone to hammer something with a screwdriver, we would need an AI compatible 3D software that either has easier to use voxels built in so it can create similar to pixel art, or easier math defined curves that can be meshed, either way AI just does not currently have the right tools to generate 3D scenes

Re: Gemini 2.5 Deep Think

#129

Post by energy123 »

danenania wrote: post #114
martinald wrote: post #111

Earlier quoted context omitted.

Yes but prompt evaluation is far faster than inference as it can be done (mostly) in parallel, so I don't think that's true.

The problem is that input token cost dominates output token cost for the majority of tasks. Once you've given the model your prompt and are reading the first output token for classification, you've already paid most of the cost of just prompting it directly. That said, there could definitely be exceptions for short prompts where output costs dominate input costs. But these aren't usually the interesting use cases.

No, you're talking about costs to user, which are oversimplifications of the costs that providers bear. One output token with a million input tokens is incredibly cheap for providers

Re: Gemini 2.5 Deep Think

#130

Post by rdtsc »

nickandbro wrote: post #43

Ladies and Gentlemen, Here's Gemini Deep Think when prompted with: "Create a svg of a pelican riding on a bicycle" https://www.svgviewer.dev/s/5R5iTexQ Beat Simon Willison to it :)

If it's on HN and is a meme at this point, it will end up in the training set.

It's kind of fun to imagine that there is an intern in every AI company furiously trying to get nice looking svg pelicans on bicycles.

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