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Gemini 3.1 Pro

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Re: Gemini 3.1 Pro

#301

Post by aberoham »

dekhn wrote: post #145
quacky_batak wrote: post #54

I’m keen to know how and where are you using Gemini. Anthropic is clearly targeted to developers and OpenAI is general go to AI model. Who are the target demographic for Gemini models? ik that they are good and Flash is super impressive. but i’m curious

I am a professional software developer who has been programming for 40 years (C, C++, Python, assembly, any number of other languages). I work in ML (infrastructure, not research) and spent a decade working at Google. In short, I consider Gemini to be a highly capable intern (grad student level) who is smarter and more tenacious than me, but also needs significant guidance to reach a useful goal. I used Gemini to com…

Wow, you have to try claude code with Opus-4.6..

Re: Gemini 3.1 Pro

#302

Post by thornewolf »

quacky_batak wrote: post #54

I’m keen to know how and where are you using Gemini. Anthropic is clearly targeted to developers and OpenAI is general go to AI model. Who are the target demographic for Gemini models? ik that they are good and Flash is super impressive. but i’m curious

I have swapped to using gemini over chatgpt for casual conversation and question answering. there are some lacking features in the app but i get faster and more intelligent responses.

Re: Gemini 3.1 Pro

#303

Post by jakejohnson »

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

I haven't seen any deprecation notices for 2.5 yet, just for 2. I'd expect (and hope) the deprecation timeline for 2.5 is longer since 3.0 is still in preview. Maybe they just default to 1 year here?

> Note: The shutdown dates listed in the table indicate the /earliest/ possible dates on which a model might be retired. We will communicate the exact shutdown date to users with advance notice to ensure a smooth transition to a replacement model.

Re: Gemini 3.1 Pro

#304

Post by toraway »

eknkc wrote: post #151
mixel wrote: post #82

Google seems to really pull ahead in this AI race. For me personally they offer the best deal and although the software is not quiet there compared to openai or anthropic (in regards to 1. web GUI, 2. agent-cli). I hope they can fix that in the future and I think once Gemini 4 or whatever launches we will see a huge leap again

I hope they fail. I honestly do not wish Google to have the best model out there and be forced to use their incomprehensible subscription / billing / project management whatever shit ever again. I don’t know what their stuff cost. I don’t know why would I use vertex or ai studio. What is included in my subscription what is billed per use. I pray that whatever they build fails and burns.

For a personal plan to use premium Gemini AI features or for agentic development with Gemini CLI/Antigravity the billing is no more or less complicated then Claude Code or Codex CLI.

You pay for the $20/mo Google AI Pro plan with a credit card via the normal personal billing flow like you would for a Google One plan without any involvement of Google Cloud billing or AI Studio. Authorize in the client with your account and you're good to go.

(With the bundled drive storage on AI Pro I'm just paying a few bucks more than I was before so for me it's my least expensive AI subscription excluding the Z.ai ultra cheap plan).

Or, just like with Anthropic or OpenAI, it's a separate process for billing/credits for an API key targeted at a developer audience. Which I don't need or use for Gemini CLI or Antigravity at all, it's a one step "click link to authorize with your Google Account" and done.

You could decide to use an API key for usage based billing instead (just like you could with Claude Code) but that's entirely unnecessary with a subscription.

Sure, for the API anything involving a hyperscalar cloud is going to have a higher complexity floor with legacy cruft here and there, but for individual subscriptions that's irrelevant and it's pretty much as straightforward of a click and pay flow you'd find anywhere else.

Re: Gemini 3.1 Pro

#305

Post by trilogic »

Humanity last exam 44%, Scicode 59, and that 80, and this 78 but not 100% ever.

Would be nice to see that this models, Plus, Pro, Super, God mode can do 1 Bench 100%. I am missing smth here?

Re: Gemini 3.1 Pro

#306

Post by xrd »

These models are so powerful.

It's totally possible to build entire software products in the fraction of the time it took before.

But, reading the comments here, the behaviors from one version to another point version (not major version mind you) seem very divergent.

It feels like we are now able to manage incredibly smart engineers for a month at the price of a good sushi dinner.

But it also feels like you have to be diligent about adopting new models (even same family and just point version updates) because they operate totally differently regardless of your prompt and agent files.

Imagine managing a team of software developers where every month it was an entirely new team with radically different personalities, career experiences and guiding principles. It would be chaos.

I suspect that older models will be deprecated quickly and unexpectedly, or, worse yet, will be swapped out with subtle different behavioral characteristics without notice. It'll be quicksand.

Re: Gemini 3.1 Pro

#307

Post by suddenlybananas »

AlphaAndOmega0 wrote: post #297
suddenlybananas wrote: post #277

Earlier quoted context omitted.

They probably had time to toss that example in the training soup.

Previous models from competitors usually got that correct, and the reasoning versions almost always did. This kind of reflexive criticism isn't helpful, it's closer to a fully generalized counter-argument against LLM progress, whereas it's obvious to anyone that models today can do things they couldn't do six months ago, let alone 2 years back.

I'm not denying any progress, I'm saying that reasoning failures that are simple which have gone viral are exactly the kind of thing that they will toss in the training data. Why wouldn't they? There's real reputational risks in not fixing it and no costs in fixing it.

Re: Gemini 3.1 Pro

#308

Post by foruhar »

throwaw12 wrote: post #226

Can we switch from Claude Code to Google yet? Benchmarks are saying: just try But real world could be different

My sense is that the Gemini models are very capable but the Gemini CLI experience is subpar compared to Claude Code and Codex. I'm guess that it's the harness but since it can get confused, fall into doom loops, and generally lose the plot in a way that the model does not in Gemini Studio or the Gemini app.

I think a bunch of these harnesses are open source so it surprises me that there can be such a gulf between them.

Re: Gemini 3.1 Pro

#309

Post by upmind »

In my experience, while Gemini does really well in benchmarks I find it much worse when I actually use the model. It's too verbose / doesn't follow instructions very well. Let's see if that changes with this model.

Re: Gemini 3.1 Pro

#310

Post by brokencode »

plaidfuji wrote: post #203
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.

Sounds like the update is mostly system prompt + changes to orchestration / tool use around the core model, if the knowledge cutoff is unchanged

This keeps getting repeated for all kinds of model releases, but isn’t necessarily true. It’s possible to make all kinds of changes without updating the pretraining data set. You can’t judge a model’s newness based on what it knows about.
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