Gemini is a lot more bang for the buck. It's not just cheaper per token, but with the subscription, you also get e.g. a lot more Deep Research calls (IIRC it's something like 20 per day) compared to Anthropic offerings.

Also, Gemini has that huge context window, which depending on the task can be a big boon.

Google deep research writes way too much useless fluff though, like introduction to the industry etc.

I'm completely the opposite. I find Gemini (even 2.5 Pro) much, much better than anything else. But I hate agentic flows, I upload the full context to it in aistudio and then it shines - anything agentic cannot even come close.

I recently wrote a small CLI tool for scanning through legacy codebases. For each file, it does a light parse step to find every external identifier (function call, etc...), reads those into the context, and then asks questions about the main file in question.

It's amazing for trawling through hundreds of thousands of lines of code looking for a complex pattern, a bug, bad style, or whatever that regex could never hope to find.

For example, I recently went through tens of megabytes(!) of stored procedures looking for transaction patterns that would be incompatible with read committed snapshot isolation.

I got an astonishing report out of Gemini Pro 3, it was absolutely spot on. Most other models barfed on this request, they got confused or started complaining about future maintainability issues, stylistic problems or whatever, no matter how carefully I prompted them to focus on the task at hand. (Gemini Pro 2.5 did okay too, but it missed a few issues and had a lot of false positives.)

Fixing RCSI incompatibilities in a large codebase used to be a Herculean task, effectively a no-go for most of my customers, now... eminently possible in a month or less, at the cost of maybe $1K in tokens.

If this is a common task for you, I'd suggest instead using an LLM to translate your search query into CodeQL[1], which is designed to scan for semantic patterns in a codebase.

1. https://codeql.github.com/

+1 - Gemini is consistently great at SQL in my experience. I find GPT 5 is about as good as gemini 2.5 pro (please treat is as praise). Haven't had a chance to put Gemini 3 to a proper sql challenge yet.

Is there any chance you'd be willing to share that tool? :)

It's a mess vibe coding combined with my crude experiments with the new Microsoft Agent Framework. Not something that's worth sharing!

Also, I found that I had to partially rewrite it for each "job", because requirements vary so wildly. For example, one customer had 200K lines of VBA code in an Access database, which is a non-trivial exercise to extract, parse, and cross-reference. Invoking AI turned out to be by far the simplest part of the whole process! It wasn't even worth the hassle of using the MS Agent Framework, I would have been better off with plain HTTPS REST API calls.

I think you're both correct. Gemini is _still_ not that good at agentic tool usage. Gemini 3 has gotten A LOT better, but it still can do some insane stupid stuff like 2.5

Personally my hype is for the price, especially for Flash. Before Sonnet 4.5 was competitive with Gemini 2.5 Pro, the latter was a much better value than Opus 4.1.

with gemini you have to spend 30 minutes deleting hundreds of useless comments littered in the code that just describe what the code itself does

The comments would improve code quality because it's a way for the LLM to use a scratchpad to perform locally specific reasoning before writing the proceeding code block, which would be more difficult for the LLM to just one shot.

You could write a postprocessing script to strip the comments so you don't have to do it manually.

I haven't had a comment generated for 3.0 pro at all unless specified.