This past week was one of those moments where you just lean back and enjoy the ride. Google dropped Gemini 3 Pro. Anthropic dropped Claude Opus 4.5. Both landed within days of each other. If you work in AI, this is the good stuff.
Gemini 3 Pro
Google went a different direction. Gemini 3 Pro is all about reasoning, multimodal inputs, and that million-token context window.
The benchmark numbers are wild. It hit 91.9% on GPQA Diamond. On ARC-AGI-2, the abstract reasoning benchmark, it scored 31.1% (and up to 45% in Deep Think mode). That is a huge leap over previous models. On LMArena it took the top ELO spot.
If your work is heavy on reasoning, vision, video, or you need to throw massive context at a problem, Gemini 3 Pro is built for that.
Claude Opus 4.5
Anthropic announced Opus 4.5 on November 24, 2025. They are calling it the best model in the world for coding, agents, and computer use. Bold claim.
On their internal engineering benchmarks, Opus 4.5 scored higher than any human candidate ever on their take-home exam. It also delivers higher pass rates on tests while using up to 65% fewer tokens than Sonnet 4.5. That efficiency piece matters if you are building agents that run for hours.
The pitch is clear: if you care about code, automation, and not burning through tokens, Opus 4.5 is the one.
How They Compare
Opus 4.5 is aimed at engineers building agents and writing code. It is optimized for efficiency. Gemini 3 Pro is aimed at everything else: reasoning, multimodal, long context, general purpose.
Both are frontier models. Both are available on major clouds and APIs. The honest answer is you might end up using both depending on the task.
The Real Point
The meta-point is not which model wins. The point is that two frontier models landed in the same week, both pushing hard on different axes. Reasoning, coding, agents, vision, efficiency. The pace of improvement is nuts.
If you are building with AI right now, the table is full. Pick your model, match it to your task, and start experimenting. There has never been a better time.
What excites me about Claude Opus 4.5?
While I was writing this post, a friend texted me to ask what excited me about Claude Opus 4.5. I spent a couple of hours with it today, and I would have to say… tool search.
Tool search lets Claude discover and load tools on-demand instead of pre-loading every definition at the start.
Here is how it works. You provide a catalog of tools to the API with names, descriptions, and input schemas. You mark most of them with defer_loading: true, which means they stay out of the model’s context until needed. Then you include a tool-search tool in the list. When Claude needs a new capability, it searches, finds the right tool, and only then does that tool get loaded into context.
There are two pain points this solves… expensive tokens and picking the right tool.
When you load dozens of tools upfront, the definitions alone eat up thousands of tokens. That is space you could be using for reasoning, tool outputs, and user messages. With tool search, you load only the search tool and maybe three to five relevant definitions. The overhead drops significantly.
With large libraries of tools, the model can struggle to pick the right one, especially when names or parameters are similar. The search step narrows the candidates to tools that actually match the task.