I've heard almost nothing about Gemini in my circles, are they still in the race to build AGI?
Last time Google had a "everybody agrees" SOTA model was Gemini 3 in November 2025, it beat GPT-5.1, it really was better and held it for a month.
Currently Google's best model doesn't match open models, in any category (performance, price or speed), in fact the last 4 open weight champions all beat Google's best model.
Here's a plot of the evolution: https://www.reddit.com/r/LocalLLaMA/comments/1v20g29/kimik3_...
I have no opinion on whether this is true, but "It's been 6 months since Google had the best model in the world" seems a rather weak criticism!
It's anyways been clear for a long time that people are finding value at all sorts of different model sizes and price points, and that Pareto frontier and cost-to-complete task are more important than who benchamaxxed who.
If a model as strong as Gemini 3.6 Flash(!) had been released a year ago, then everyone would be falling over themselves calling it AGI - it is extremely capable, and free usage in the chat app is essentially unlimited.
At least OpenAI and Anthropic can still tell their customers their best model is better than some Finnish student can setup in the customers' basement, even if the difference is pretty small now. But they are better. Google is not.
Surely that's a major change in the situation. "America" is losing the advantage (although even the Chinese models are losing, see next paragraph) but Google is a big step behind 6 other players and 3 open models, so let's just say, Google is effectively behind everyone else that matters. Worse: one of the models that beats Google's best model was trained on Chinese ASICs (that aren't even 2nm. And very few I might add, couple thousand. Google's next model ... has a LOT to prove)
Even Chinese models are losing their advantage. By which I mean that if you check how far Qwen 3.6 27B is behind trillion-parameter models, it's less than a year, down from easily 3 years. If that continues to go down, even Chinese trillion parameter models will become hard to justify (and Qwen 3.8 is being cooked up as we speak, I mean we don't even know if a new 27B is in the works or not, but everyone's very excited)
Bank 1> What is Google going to do about not having a SOTA model?
Sundar> Everyone uses Gemini 3.6 Flash anyway. We do that internally as well.
Bank 2> What is Google going to do about not having a SOTA model?
Sundar> Flash is great. We've started our most ambitious training run with Gemini 4.
Bank 3> What is Google going to do about not having a SOTA model?
Seriously, like 4 banks in a row, same principle. I'll look up the exact wording in the transcript, but for now it's not available and I'm not listening to the 20 minutes of that repeat again.
As for your question: Google is training a new major version of Gemini, so yes, Google is trying to get a SOTA model. They've just not succeeded for a while.
Now, Google DeepMind is still very much pursuing AGI, and do see an LLM as being a component of that, so that's at least one area where it might make sense for Google to invest in a frontier LLM, but otherwise ???
Why aren't Wall St clamoring for Google to get back into robotics to compete with Tesla, or maybe to get into the rocket business to compete in the "data centers in space" business, or maybe get into the dry cake mix business to compete with Sara Lee ? ...