Gemini 3.1 Pro Preview

Does the arc-agi-2 score more than doubling in a .1 release indicate benchmark-maxing? Though i dont know what arc-agi-2 actually tests

The touted SVG improvements make me excited for animated pelicans.

Remember when ARC 1 was basically solved, and then ARC 2 (which is even easier for humans) came out, and all of the sudden the same models that were doing well on ARC 1 couldn’t even get 5% on ARC 2? Not convinced these benchmark improvements aren’t data leakage.

I don't think there's much recursive improvement yet.

I'd say it's a combination of

A) Before, new model releases were mostly a new base model trained from scratch, with more parameters and more tokens. This takes many Months. Now that RL is used so heavily, you can make infinitely many tweaks to the RL setup, and in just a month get a better model using the same base model.

B) There's more compute online

C) Competition is more fierce.

this is mostly because RLVR is driving all of the recent gains, and you can continue improving the model by running it longer (+ adding new tasks / verifiers)

so we'll keep seeing more frequent flag planting checkpoint releases to not allow anyone to be able to claim SOTA for too long

not much to do with self improvement as such. openai has increased its pace, others are pretty much consistent. Google last year had three versions of gemini-2.5-pro each within a month of each other. Anthropic released claude 3 in march 24, sonnet 3.5 in june 24, 3.5 new in oct 24, and then 3.7 in feb 25, where they went to 4 series in May 25. then followed by opus 4.1 in august, sonnet 4.5 in oct, opus 4.5 in nov, 4.6 in feb, sonnet 4.6 in feb itself. Yes, they released both within weeks of each other, but originally they only released it together. This staggered release is what creates the impression of fast releases. its as much a function of training as a function of available compute, and they have ramped up in that regard.

With the advent of MoEs, efficiency gains became possible. However, MoEs still operate far from the balance and stability of dense models. My view is that most progress comes from router tuning based on good and bad outcomes, with only marginal gains in real intelligence

and anyone notice that the pace has broken xAI and they were just dropped behind? The frontier improvement release loop is now ant -> openai -> google

That's what scaling compute depth to respond to the competition look like, lighting those dollars on fire.

This is what competition looks like.

Only using my historical experience and not Gemini 3.1 Pro, I think we see benchmark chasing then a grand release of a model that gets press attention...

Then a few days later, the model/settings are degraded to save money. Then this gets repeated until the last day before the release of the new model.

If we are benchmaxing this works well because its only being tested early on during the life cycle. By middle of the cycle, people are testing other models. By the end, people are not testing them, and if they did it would barely shake the last months of data.

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Output limit has consistently been 64k tokens (including 2.5 pro).

People did find Gemini very talkative so it might be a response to that.

> Even when the model is explicitly instructed to pause due to insufficient tokens

Is there actually a chance it has the introspection to do anything with this request?

> Even when the model is explicitly instructed to pause due to insufficient tokens rather than generating an incomplete response

AI models can't do this. At least not with just an instruction, maybe if you're writing some kind of custom 'agentic' setup.

>I wish Google also updated Flash-lite to 3.0+

I hope every day that they have made gains on their diffusion model. As a sub agent it would be insane, as it's compute light and cranks 1000+ tk/s

> I've been playing with the 3.1 Deep Think version of this

How?

Seconded. Gemini used to be trash and I used Claude and Codex a lot but gemini-3-flash-preview punches above it's weight, it's decent and I rarely if ever run into any token limit either.

What models are you running locally? Just curious.

I am mostly restricted to 7-9B. I still like ancient early llama because its pretty unrestricted without having to use an abliteration.

I like to ask claude how to prompt smaller models for the given task. With one prompt it was able to make a low quantized model call multiple functions via json.

Not Simon's but here is one: https://news.ycombinator.com/item?id=47075709

Please no, let's not.

I've found Google (at least in AI Studio) are the only provider NOT to nerf their models after a few weeks

Maybe that's the only API-visible change, saying nothing about the actual capabilities of the model?

I disagree. Incrementing the minor number makes so much more sense than “gemini-3-pro-preview-1902” or something.

> increasing the number for such a minor change is not a move in the right direction

A .1 model number increase seems reasonable for more than doubling ARC-AGI 2 score and increasing so many other benchmarks.

What would you have named it?

According to the blog post, it should be also great at drawing pelicans riding a bicycle.

> but without assessing whether the models are actually improving in practical use-cases

Which cases? Not trying to sound bad but you didn't even provide of cases you are using Claude\Codex\Gemini for.

I'm glad someone else is finally saying this, I've been mentioning this left and right and sometimes I feel like I'm going crazy that not more people are noticing it.

Gemini can go off the rails SUPER easily. It just devolves into a gigantic mess at the smallest sign of trouble.

For the past few weeks, I've also been using XML-like tags in my prompts more often. Sometimes preferring to share previous conversations with `<user>` and `<assistant>` tags. Opus/Sonnet handles this just fine, but Gemini has a mental breakdown. It'll just start talking to itself.

Even in totally out-of-the-ordinary sessions, it goes crazy. After a while, it'll start saying it's going to do something, and then it pretends like it's done that thing, all in the same turn. A turn that never ends. Eventually it just starts spouting repetitive nonsense.

And you would think this is just because the bigger the context grows, the worse models tend to get. But no! This can happen well below even the 200.000 token mark.

I exclusively use Gemini for Chat nowadays, and it's been great mostly. It's fast, it's good, and the app works reliably now. On top of that I got it for free with my Pixel phone.

For development I tend to use Antigravity with Sonnet 4.5, or Gemini Flash if it's about a GUI change in React. The layout and design of Gemini has been superior to Claude models in my opinion, at least at the time. Flash also works significantly faster.

And all of it is essentially free for now. I can even select Opus 4.6 in Antigravity, but I did not yet give it a try.

Honestly doesn't feel like Google is targeting the agentic coding crowd so much as they are the knowledge worker / researcher / search-engine-replacement market?

Agree Gemini as a model is fairly incompetent inside their own CLI tool as well as in opencode. But I find it useful as a research and document analysis tool.

(Shrug) Ask it to write one!