For all of the images I've tried, the base model (e.g. 4o) already has a ~95% accurate idea of where the photo is, and then o3 does so much tool use only to confirm its intuition from the base model and slightly narrow down. For OP's initial image, 4o in fact provides a more accurate initial guess of Carmel-by-the-Sea (d=~100mi < 200mi), and its next guess is also Half Moon Bay, although it did not figure out the exact town of El Granada [0].
The clue is in the CoT - you can briefly see the almost correct location as the very first reasoning step. The model then apparently seems to ignore it and try many other locations, a ton of tool use, etc, always coming back to the initial guess.
For pictures where the base model has no clue, I haven't seen o3 do anything smart, it just spins in circles.
I believe the model has been RL-ed to death in a way that incentivizes correct answers no matter the number of tools used.
[0]: https://chatgpt.com/c/680d011a-9470-8002-97a0-a0d2b067eacf
The clue is in the CoT - you can briefly see the almost correct location as the very first reasoning step. The model then apparently seems to ignore it and try many other locations, a ton of tool use, etc, always coming back to the initial guess.
For pictures where the base model has no clue, I haven't seen o3 do anything smart, it just spins in circles.
I believe the model has been RL-ed to death in a way that incentivizes correct answers no matter the number of tools used.
[0]: https://chatgpt.com/c/680d011a-9470-8002-97a0-a0d2b067eacf