Comment by aliston
6 days ago
I'm having trouble just keeping track of all these different types of models.
Is "Gemini 3 Deep Think" even technically a model? From what I've gathered, it is built on top of Gemini 3 Pro, and appears to be adding specific thinking capabilities, more akin to adding subagents than a truly new foundational model like Opus 4.6.
Also, I don't understand the comments about Google being behind in agentic workflows. I know that the typical use of, say, Claude Code feels agentic, but also a lot of folks are using separate agent harnesses like OpenClaw anyway. You could just as easily plug Gemini 3 Pro into OpenClaw as you can Opus, right?
Can someone help me understand these distinctions? Very confused, especially regarding the agent terminology. Much appreciated!
The term “model” is one of those super overloaded terms. Depending on the conversation it can mean:
- a product (most accurate here imo)
- a specific set of weights in a neural net
- a general architecture or family of architectures (BERT models)
So while you could argue this is a “model” in the broadest sense of the term, it’s probably more descriptive to call it a product. Similarly we call LLMs “language” models even if they can do a lot more than that, for example draw images.
I'm pretty sure only the second is properly called a model, and "BERT models" are simply models with the BERT architecture.
If someone says something is a BERT “model” I’m not going to assume they are serving the original BERT weights (definition 2).
I probably won’t even assume it’s the OG BERT. It could be ModernBERT or RoBERTa or one of any number of other variants, and simply saying it’s a BERT model is usually the right level of detail for the conversation.
It depends on time. 5 years ago it was quite well defined that it’s the last one, maybe the second one in some context. Especially when distinction was important, it was always the last one. In our case it was. We trained models to have weights. We even stored models and weights separately, because models change slower than weights. You could choose a model and a set of weights, and run them. You could change weights any time.
Then marketing, and huge amount of capital came.
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> Also, I don't understand the comments about Google being behind in agentic workflows.
It has to do with how the model is RL'd. It's not that Gemini can't be used with various agentic harnesses, like open code or open claw or theoretically even claude code. It's just that the model is trained less effectively to work with those harnesses, so it produces worse results.
There are hints this is a preview to Gemini 3.1.
I have no proof, but these deep thinking modes feel to me like an orchestrator agent + sub agents, the former being RL‘d to just keep going instead of being conditioned to stop ASAP.