Comment by jampa
15 hours ago
I've been using Gemini 3.7 for my personal trip planning app. Across multiple benchmarks, it ranks higher on everything I tried:
- Real world knowledge (when a thing opens and closes, the geographic region, historical facts). It's also the best at taking a cluster of places and working out a visiting order.
- Photo ranking (which photo should be the hero). Gemini can tell whether a photo is of the thing or of the view from it.
- Document parsing (extracting the relevant trip info from PDFs).
If you use LLMs for anything other than coding, I definitely recommend not discounting Gemini like I did just because other models are more popular.
Gemini 3.7 is my workhorse - fast and good enough for most tasks. Occasionally I go to GPT Sol or Claude to improve Gemini's output or for more complex tasks, but more than of my work usage is Gemini 3.7. Quite happy to test 3.8 now.
Same here. I see so many people obsessing over the latest most state of the art bleeding edge models and yelling at Google for not being there, but I feel like the vast majority of people don't actually need those models. Flash has just been super useful and incredibly fast in my experience.
I prefer luna for most development, especially when I am guiding the process. Sometimes terra. I have had terrible results coding with sol. It is way over-tuned on RL to make something that completes the task, no matter what. I end up with way too much code that does a lot of things I didn't ask for.
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How are you able to get lots of usage out of it cost effectively?
Google One plans are quite a good value actually - for a few bucks you get more Gemini plus space in Drive and other extras. Even through API, $3.75 for nearly Sol-level quality isn't that bad. And let's not forget you can use it for free in AI Studio, and in the user app (even free accounts get tons of usage, though it's still 3.6 there), and in Antygravity.
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Ultra AI is like $99/month, and it is hard to exhaust unless you are running a lot of concurrent requests.
I've been benchmarking[1] models for trip planning and world knowledge specifically (to decide on which model to use with my travel app), and the Gemini models consistently come out on top.
[1]: https://tripstitch.app/benchmarks/
One thing in your comment surprised me: "when a thing opens and closes"
Why you would rely on the model's weights to know opening hours, instead of having the model call a web search tool to verify it on the official site?
I believe Gemini Flash is smart enough to know when to ground with web search. Their app has been saying it’s running a web search on almost all of my queries since 3.6. And given that Google … is Google, I trust them with web search grounding more than anyone else.
It can also ground with Google Maps data in addition to web search.
I wasn't trying to be precise originally, I just tried to fit activities into "morning / evening" buckets. I did the whole itinerary with Opus first, but when I gave it to Gemini 3.7 Flash to review, it started correcting it with "this place will close 5PM" or "this place is closed for good".
It was right on every nit, so it was surprising how well the model knows these things. If I ever release this I'll probably need the SERP API or Google Maps SDK (which I've heard is very expensive now), but for a personal trip where I will verify manually, using the LLM is okay for now.
When you called the Gemini API, did you opt in to using search grounding:
I'm curious whether in fact you were getting answers from the model weights (which is what I had assumed) or whether your API calls were resulting in web search tool calls.
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Gemini models - at least via some interfaces - have tool calling API access to various Google integrations. flights.google.com, maps.google.com, etc.
The info isn't in the model weights.
Because of where I live, there are three viable airports for any given flight I might want to take, which historically has made shopping a real pain. But Gemini (and only Gemini) has greatly simplified it. Pramble plus date range plus destination and it very quickly generates potential itineraries with costs, total travel time (driving included), etc.
Maybe the model does some tool calling on its own to figure out the times?
You can opt in on a per-request basis.
In my experience, Gemini 3.7 is excellent for general non-coding tasks. But for coding, especially backend development, I still find models like Opus 5 and GPT-5.6 more reliable.
This sounds great to combine with Wanderlog using an unofficial MCP I made https://github.com/shaikhspeare/wanderlog-mcp
I've swapped over to it in the past two weeks, it's been really good. It does what I ask and doesn't think it knows better than me, which so far has made it the most pleasing experience I've had when slop-coding.
My only wish is it were somewhat cheaper, as it tends to balloon pretty quickly when I'm using it in Opencode. I'm currently trying to offload a lot of work to subagents to stop the context expanding so rapidly. But on the upside, I rarely have to correct it - I've spent far less time arguing with this than with anything else so far.
> Real world knowledge
For awhile now I've found Gemini will use Google search for pretty much any real world knowledge, which is a huge plus IMO. It's basically Google with a much better frontend and no ads/seo nonsense.
> basically Google with a much better frontend and no ads/seo nonsense
so far
I think it already has them but it's much more subtle. Also useful. When I've made certain sorts of queries I've had the distinct impression that it was attempting to very gently steer the conversation with suggestions. But it was brief, still answered usefully, and didn't resist going in the direction I wanted. So a win-win tactic I guess.
For example find a beautiful landscape shot of a place that just so happens to be accessible to tourists and ask it something along the lines of identifying the location. IME it will noticably steer the conversation towards relevant commercial offerings and offer (entirely unprompted) to help plan a trip.
Or ask it about a certain category of product with some requirements and it will initially present (relevant) options that look like paid placement to my eye. But if you ask it's happy to go on to turn up lots of alternatives and enumerate tradeoffs.
Assuming I'm correct the subtlety is on par with product placement in movies. Certainly leagues better than the internet advertising we've suffered to date.
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Fair. But I think there's a good chance it stays that way on paid plans. YouTube Premium is still ad free.
Also them having their own silicon means they don't have to pay the Nvidia tax and can keep costs a lot lower.
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I started trying out 3.7 Flash this week and it is competitive with opus/fable and also FAST. It is getting work done that anthropic models were struggling with and the speed with which it does is quite a bit noticeably faster.
Beginning to think Google is a dark horse in this race and some of Anthropic's "everything feels janky and rushed" karma is going to catch up.
I use Gemini because I feel like Google will win the AI race, and it’s Good Enough
My long term base case, too, but they need to install Demis as CEO and I don’t think either side is ready.
I want to believe this, but every time I try Gemini coding assistance within Colab it's utterly dire. Code gen in a cell is OK, but things fall apart when you try to get into a feedback loop. The system prompt/harness fails to inform the agent about what it can and can't do, or does and doesn't have access to. It will confidently tell you it's done a thing, and then you ask, it admits can't actually do that but will happily try and fail again. Very frustrating, because I really like Colab as a platform for little reproducible experiments that may or may not require CUDA.
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> Beginning to think Google is a dark horse in this race
Google was so hyped up early Gemini 3 era (only some months ago). And now dark horse? The TPU takeover almost crashed nvidia and everyone else.
Hype that burned out pretty quickly, it's hard to speak to the size and significance of old hype, I never felt it.
Every time I personally tried Gemini models up until last week they simply couldn't do the long complex tasks I'd being doing with Anthropic models for many months.
Can G3.7 use Google Maps for distance grounding?
Yep. It has access to much better route planning tools than the other models. The results are really good IME.
Sounds good!
I've been planing around with LLM-based trip planning for a very long time now as it fits my very ad hoc style of traveling very well.
But distances always had been.. lets say.. difficult.
Will test it with my upcoming trip to Greece then!
I tried similar travelling tasks but also added transportation and complex transfers (train, bus, walk, next train...). Worked meh and still a difficult thing to do for a llm.
thanks! this is a very helpful one. I am going to try.
> trip planning app.
this has to be stong suit of ai agents any model
"Claude 3.7"?
I asked Claude to fix the grammar of my comment, and it changed "I am using 3.7 for" to "I've been using Claude 3.7", so they sneaked their own name on it.
incredible. further evidence supporting my personal stance to never ever let an LLM write or edit my writing intended for another human being to read. this is all me, baby
you didn’t even read your comment before you posted it?
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A two paragraph hacker comment? You burned carbon for that?
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I stopped using Gemini a few months ago because it would often just (partially) reply literal nonsense to me.
Think 2023 style ChatGPT. Something like “to open a document on your Mac click File > Open docurrrar” - like it suddenly forgot it had to produce actual words.
Overall I enjoyed its speed and comprehensiveness. But those occurrences of nonsense just made it feel like a great car that once a month just stops in the middle of the highway.