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Gemini 3.1 Pro

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Re: Gemini 3.1 Pro

#561

Post by h14h »

h14h wrote: post #381
embedding-shape wrote: post #73

Earlier quoted context omitted.

It's an excellent demonstration of the main issue I have with the Gemini family of models, they always go "above and beyond" to do a lot of stuff, even if I explicitly prompt against it. In this case, most of the SVG ends up consisting not just of a bike and a pelican, but clouds, a sun, a hat on the pelican and so much more. Exactly the same thing happens when you code, it's almost impossible to get Gemini to not do…

Would be really interesting to see an "Eager McBeaver" bench around this concept. When doing real work, a model's ability to stay within the bounds of a given task has almost become more important than its raw capabilities now that every frontier model is so dang good. Every one of these models is so great at propelling the ship forward, that I increasingly care more and more about which models are the easiest to ste…

For sure. I imagine it'd be pretty difficult to evaluate the "correct" amount of steer-ability. You'd probably just have to measure a delta in eagerness on a single same task between when given highly-specified prompts, and more open-ended prompts. Probably not dissimilar from how artificialanalysis.ai does their "omniscience index".

Re: Gemini 3.1 Pro

#562

Post by sdeiley »

davidguetta wrote: post #162

Implementation and Sustainability Hardware: Gemini 3 Pro was trained using Google’s Tensor Processing Units (TPUs). TPUs are specically designed to handle the massive computations involved in training LLMs and can speed up training considerably compared to CPUs. TPUs often come with large amounts of high-bandwidth memory, allowing for the handling of large models and batch sizes during training, which can lead to bet…

Googler. We use GPUs, but its a drop in the bucket in the sea of our accelerators. We might sell more GPUs in Cloud than we use internally.

These are not data driven observations just vibes

Re: Gemini 3.1 Pro

#563

Post by incognito124 »

logicchains wrote: post #530
mijoharas wrote: post #205

Gemini 3 is still in preview (limited rate limits) and 2.5 is deprecated (still live but won't be for long).[0] Are Google planning to put any of their models into production any time soon? Also somewhat funny that some models are deprecated without a suggested alternative(gemini-2.5-flash-lite). Do they suggest people switch to Claude? [0] https://ai.google.dev/gemini-api/docs/deprecations

It's the Tensorflow strategy; deprecate the old version while the new version still doesn't support all the old one's functionality.

welcome to Google, where the only kinds of systems are the deprecated ones, and the not-yet-production-ready ones

Re: Gemini 3.1 Pro

#564

Post by miohtama »

renegade-otter wrote: post #521
karmasimida wrote: post #362

Earlier quoted context omitted.

Gemini just doesn’t do even mildly well in agentic stuff and I don’t know why. OpenAI has mostly caught up with Claude in agentic stuff, but Google needs to be there and be there quickly

It's like anything Google - they do the cool part and then lose interest with the last 10%. Writing code is easy, building products that print money is hard.

One does not need products if you have monopoly on search

Re: Gemini 3.1 Pro

#565

Post by swftarrow »

karmasimida wrote: post #362
spankalee wrote: post #199

I hope this works better than 3.0 Pro I'm a former Googler and know some people near the team, so I mildly root for them to at least do well, but Gemini is consistently the most frustrating model I've used for development. It's stunningly good at reasoning, design, and generating the raw code, but it just falls over a lot when actually trying to get things done, especially compared to Claude Opus. Within VS Code Copi…

Gemini just doesn’t do even mildly well in agentic stuff and I don’t know why. OpenAI has mostly caught up with Claude in agentic stuff, but Google needs to be there and be there quickly

I suspect a large part of Google's lag is due to being overly focused on integrating Gemini with their existing product and app lines.

Re: Gemini 3.1 Pro

#566

Post by zapnuk »

Gemini 3 was:

1. unreliable in GH copilot. Lots of 500 and 4XX errors. Unusable in the first 2 months

2. not available in vertex ai (europe). We have requirements regarding data residency. Funny enough anthropic is on point with releasing their models to vertex ai. We already use opus and sonnet 4.6.

I hope google gets their stuff together and understands that not everyone wants/can use their global endpoint. We'd like to try their models.

Re: Gemini 3.1 Pro

#568

Post by mikestorrent »

small_model wrote: post #478
TZubiri wrote: post #468

Earlier quoted context omitted.

No version control?

I was being facetious, I mean one day models might skip the middle man of code and compilation and take your specs and produce an ultra efficent binary.

Musk was saying that recently but I don't see it being efficient or worthwhile to do this. I could be proven brutally wrong, but code is language; executables aren't. There's also no real reason to bother with this when we have quick-compiling languages.

More realistically, I could see particular languages and frameworks proving out to be more well-designed and apt for AI code creation; for instance, I was always too lazy to use a strongly-typed language, preferring Ruby for the joy of writing in it (obsessing about types is for a particular kind of nerd that I've never wanted to be). But now with AI, everything's better with strong types in the loop, since reasoning about everything is arguably easier and the compiler provides stronger guarantees about what's happening. Similarly, we could see other linguistic constructs come to the forefront because of what they allow when the cost of implementation drops to zero.

Re: Gemini 3.1 Pro

#569

Post by siliconc0w »

Google has a hugely valuable dataset of changes from decades of changes from top tier software engineers but it's so proprietary they can't use it to train their external models.

Re: Gemini 3.1 Pro

#570

Post by miohtama »

amluto wrote: post #532
minimaxir wrote: post #23

Price is unchanged from Gemini 3 Pro: $2/M input, $12/M output. https://ai.google.dev/gemini-api/docs/pricing Knowledge cutoff is unchanged at Jan 2025. Gemini 3.1 Pro supports "medium" thinking where Gemini 3 did not: https://ai.google.dev/gemini-api/docs/gemini-3 Compare to Opus 4.6's $5/M input, $25/M output. If Gemini 3.1 Pro does indeed have similar performance, the price difference is notable.

Now compare the monthly plans for business users who want the CLI agent but who don’t want the models trained on their data. OpenAI: no big deal — sign up, pick your number of seats, and you’re all set. Anthropic: also no big deal but there’s an obnoxious minimum purchase. Google: first you have to try to figure out what the product is called. Then you need to figure out how to set the correct IAM rules. Then you hav…

I can confirm the products bit, I tried to use Gemini to help with G Suite admin.
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