edit: biggest benchmark changes from 3 pro:
arc-agi-2 score went from 31.1% -> 77.1%
apex-agents score went from 18.4% -> 33.5%
Even when the model is explicitly instructed to pause due to insufficient tokens rather than generating an incomplete response, it still truncates the source text too aggressively, losing vital context and meaning in the restructuring process.
I hope the 3.1 release includes a much larger output limit.
It's only February...
Apart from that, the usual predictable gains in coding. Still is a great sweet-spot for performance, speed and cost. Need to hack Claude Code to use their agentic logic+prompts but use Gemini models.
I wish Google also updated Flash-lite to 3.0+, would like to use that for the Explore subagent (which Claude Code uses Haiku for). These subagents seem to be Claude Code's strength over Gemini CLI, which still has them only in experimental mode and doesn't have read-only ones like Explore.
Either way early user tests look promising.
Off topic, but I like to run small models on my own hardware, and some small models are now very good for tool use and with agentic libraries - it just takes a little more work to get good results.
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.
Is there actually a chance it has the introspection to do anything with this request?
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 get the impression that Google is focusing on benchmarks but without assessing whether the models are actually improving in practical use-cases.
I.e. they are benchmaxing
Gemini is "in theory" smart, but in practice is much, much worse than Claude and Codex.
How?
I'm not even sure what "pausing" means in this context and why it would help when there are insufficient tokens. They should just stop when you reach the limit, default or manually specified, but it's typically a cutoff.
You can see what happens by setting output token limit much lower
Here's a similar result with Qwen Qwen3.5-397B-A17B: https://chat.qwen.ai/s/530becb7-e16b-41ee-8621-af83994599ce?...
so we'll keep seeing more frequent flag planting checkpoint releases to not allow anyone to be able to claim SOTA for too long
and I'm sure others I've missed...
A couple of western models have dropped around the same time too but I don't think the "strides on benchmarks" are that impressive when you consider how much tokens are being spent to make those "improvements". E.g. Gemini 3.1 Pro's ARC-AGI-2 score went from 33.6% to 77.1% buuut their "cost per task" also increased by 4.2x. It seems to be the same story for most of these benchmark improvements and similar for Claude model improvements.
I'm not convinced there's been any substantial jump in capabilities. More likely these companies have scaled their datacenters to allow for more token usage
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?
The model thought for over 5 minutes to produce this. It's not quite photorealistic (some parts are definitely "off"), but this is definitely a significant leap in complexity.
Here's a similar result with Qwen Qwen3.5-397B-A17B: https://chat.qwen.ai/s/530becb7-e16b-41ee-8621-af83994599ce?...
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
Also people use "saturated" too liberally. The top left corner 1 cent per task is saturated IMO. Since there are billions of people who would perfer to solve arc 1 tasks at 52 cents per task. Arc 2 a human would make thousands of dollars a day with 99.99% accuracy
> Create an SVG animation of a Beaver sitting next to a recordplayer and a create of records, his eyes follows the mouse curser.
I am mostly restricted to 7-9B. I still like ancient early llama because its pretty unrestricted without having to use an abliteration.
It does say 3.1 in the Pro dropdown box in the message sending component.
Could be useful for planning too, given its tendency to think big picture first. Even if it's just an additional subagent to double-check with an "off the top off your head" or "don't think, share first thought" type of question. More generally would like to see how sequencing autoregressive thinking with diffusion over multiple steps might help with better overall thinking.
Basically, what does the word "Preview" mean, if newer releases happen before a Preview model is stable? In prior Google models, Preview meant that there'd still be updates and improvements to said model prior to full deployment, something we saw with 2.5. Now, there is no meaning or reason for this designation to exist if they forgo a 3.0 still in Preview for model improvements.
However, I heavily use Gemini in my daily work and I think it has its own place. Ultimately, I don't see the point of choosing the one "best" model for everything, but I'd rather use what's best for any given task.
Which cases? Not trying to sound bad but you didn't even provide of cases you are using Claude\Codex\Gemini for.
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.
It's a sort of arbitrary pattern matching thing that can't be trained on in the sense that the MMLU can be, but you can definitely generate billions of examples of this kind of task and train on it, and it will not make the model better on any other task. So in that sense, it absolutely can be.
I think it's been harder to solve because it's a visual puzzle, and we know how well today's vision encoders actually work https://arxiv.org/html/2407.06581v1
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.
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.
GMail was in "beta" for 5 years.
Happy to learn more about this if anyone has more information.
The models are all close enough on the benchmarks and I think people are attributing too much difference in the agentic space to the model itself. I strongly believe the difference is in all the other stuff, which is why Antropic is far ahead of the competition. They have done great work with Claude Code, Cowork, and their knowledge share through docs & blog, bar none on this last point imo.
But scaling pre-training is still worth it if you can afford it.
Wonder how GP feels about the minor bumps for other model providers?