[scarmig]

Cheapness has a quality all its own.

Gemini is substantially cheaper to run (in consumer prices, and likely internally as well) than OpenAI's models. You might wonder, what's the value in this, if the model isn't leading? But cheaper inference could potentially be a killer edge when you can scale test-time compute for reasoning. Scaling test-time compute is, after all, what makes o1 so powerful. And this new Gemini doesn't expose that capability at all to the user, so it's comparing apples and oranges anyway.

DeepMind researchers have never been primarily about LLMs, but RL. If DM's (and OAI's) theory is correct--that you can use test-time compute to generate better results, and train on that--this is potentially a substantial edge for Google.

[zaptrem]

Google still has an unbelievable training infrastructure advantage. The second they can figure out how to convert that directly to model performance without worrying about data (as the o1 blog post seemed to imply OAI had) they’ll be kings.

[llelouch]

This is why Sam Altman keeps releasing things a few days before Deepmind. He is worried Google will overtake them more so than other companies.

[Dr4kn]

In Home Assistant you can use LLMs to control your Home with your voice. Gemini performs similar to the GPT models, and with the cost difference there is little reason to choose OpenAi

[a_wild_dandan]

Using either frontier model for basic edge device problems is wasteful. Use something cheap. We're asking "is there a profitable niche between the best & runner-up models?" I believe so.