It’s a good model sir. Whilst it doesn’t beat every other model on everything, it’s definitely pushed the pareto frontier a step further out.
It hallucinates pretty badly. ChatGPT 5 did too when it was released, hopefully they can fix this in future patches and it’s not inherent to the model.
To those who were hoping/expecting to have hit a wall. Clearly hasn’t happened yet (although neither have we proved that LLMs can take us all the way to AGI).
Costs are slightly higher than 2.5-pro, much higher than gpt 5.1, and none of googles models have seen any price reduction in the last couple of years. This suggests that it’s not quickly getting cheaper to run a given model, and that pushing the pareto frontier forward is costing ever more in inference. (However we are learning how to get more intelligence out of a fixed size with newer small models).
I would say Google currently has the best image models and best LLM, but that doesn’t prove they’re in the lead. I expect openai and anthropic to drop new models in the next few months, and Google won’t release a new one for another 6 months at best. It’s lead is not strong enough to last that long.
However we can firmly say that Google is capable of creating SOTA models that give openai and anthropic a run for their money, something many were doubting just a year ago.
Google has some tremendous structural advantages:
independent training and inference stack with TPUs, JAX, etc. It is possible they can do ML at a scale and price point noone else can achieve.
trivial distribution. If Google comes up with a good integration they have dozens of products where they can instantly push it out to hundreds of millions of people (monetising is a different question).
deep pockets. No immediate need to generate a profit, or beg investors for money.
lots of engineers. This doesn’t help with the cure model, but does help with integrations and RLHF.
Now that they’ve proven they can execute, they should likely be considered frontrunners for the AI race.
On the other hand ChatGPT has much greater brand recognition, and LLM usage is sticky. Things aren’t looking great for anthropic though with neither deep pockets or high usage.
In terms of existential risk: this is likely to make the race more desperate, which is unlikely to lead to good things.
Quick thoughts on Gemini 3 pro:
It’s a good model sir. Whilst it doesn’t beat every other model on everything, it’s definitely pushed the pareto frontier a step further out.
It hallucinates pretty badly. ChatGPT 5 did too when it was released, hopefully they can fix this in future patches and it’s not inherent to the model.
To those who were hoping/expecting to have hit a wall. Clearly hasn’t happened yet (although neither have we proved that LLMs can take us all the way to AGI).
Costs are slightly higher than 2.5-pro, much higher than gpt 5.1, and none of googles models have seen any price reduction in the last couple of years. This suggests that it’s not quickly getting cheaper to run a given model, and that pushing the pareto frontier forward is costing ever more in inference. (However we are learning how to get more intelligence out of a fixed size with newer small models).
I would say Google currently has the best image models and best LLM, but that doesn’t prove they’re in the lead. I expect openai and anthropic to drop new models in the next few months, and Google won’t release a new one for another 6 months at best. It’s lead is not strong enough to last that long.
However we can firmly say that Google is capable of creating SOTA models that give openai and anthropic a run for their money, something many were doubting just a year ago.
Google has some tremendous structural advantages:
independent training and inference stack with TPUs, JAX, etc. It is possible they can do ML at a scale and price point noone else can achieve.
trivial distribution. If Google comes up with a good integration they have dozens of products where they can instantly push it out to hundreds of millions of people (monetising is a different question).
deep pockets. No immediate need to generate a profit, or beg investors for money.
lots of engineers. This doesn’t help with the cure model, but does help with integrations and RLHF.
Now that they’ve proven they can execute, they should likely be considered frontrunners for the AI race.
On the other hand ChatGPT has much greater brand recognition, and LLM usage is sticky. Things aren’t looking great for anthropic though with neither deep pockets or high usage.
In terms of existential risk: this is likely to make the race more desperate, which is unlikely to lead to good things.