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Comment by jampa | Hacker News Reader

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.

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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.

IME you're supposed to have Sol drive Luna sub-agents to do 90% of the work. Sol should primarily be the verifier and goal setter. Use omp.sh with Task Delegation -> Always to strongly encourage Sol to drive Lunas. Also Luna prefers to be talked to with English in XML.

Try planning with Luna, implementing with Sol with guidelines to not exceed the given scope.

Sounds counter-intuitive at first, but Luna is overall better at sticking with what works. Sol is wicked smart but needs constraints.

I love Luna too. An excellent model and still usually better value per dollar than Gemini if you pay for API tokens. Things may change with 3.8 - we'll know soon.

I setup Luna as main Claude Code driver (so zero anthropic api use) and it nailed crisply a handful of python tasks, gonna continue this way.

Why not use codex or an open source harness?

That is a very valid question. I happen to want to get Claude Code muscle memory under my belt for professional reasons in addition to get side projects advanced, could have settled for Codex else. Also OpenCode with eastern models gets part of the job done. In CC beyond using Luna for the cheap, I am using DeepSeek flash v4 for subagents, that is a further cost shaver. Not sure if in Codex I could do that.

And much better bang for the buck, as well.

When I read all the issues people have with Claude - the aggressive guardrails, the cost, how quickly it burns tokens - it seems almost masochistic to use it. Just seems like herd behavior - people use it because everyone else is using it, and because they believe it’s the “best”, whatever that means. (Benchmarks certainly don’t help define that.)

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.

That's the thing. I am completely lost because there are so many redundant paths to get the same thing and I'm trying to figure out which one is the best deal

Just put Mythos on the task; it’ll work out the best way in a measly few hours.

That's Google at its best :)

Well are you looking for a subscription or pay-as-you-go API usage?

  Subscription? -> Google One plan (http://one.google.com/)

  API? -> AI Studio (https://aistudio.google.com/)

It's not really any different than the choice you'd make with OpenAI/Anthropic depending on how you plan to use it. Except as a hyperscalar, it's also offered first party from Google Cloud (like Claude via Amazon Bedrock or GPT via Microsoft Azure OpenAI Service):

  Google Cloud -> Gemini Enterprise AI Platform (https://cloud.google.com/ai)

But if you're using models via OpenCode or Pi or whatever, the flow chart is basically just "Go To AI Studio" unless you or your employer is already used to Google Cloud, otherwise there's no need to subject yourself to all those enterprise-y IAM dashboards and stuff. You still get free usage from AI Studio when you generate the API key without needing to add billing details so very easy to try.

good summary, thanks. I used to use Google Cloud for consulting and projects (I worked at Google for a while, and there is some nostalgia) so I have Gemini API via Google Cloud, but I am retired now. Your post reminded me that I need to shut that all down and switch to getting an API key using AI Studio.

I have spent two months experimenting with a wide range of US and Chinese models, and I had a lot of fun doing that, but I am in the process of switching to just using local models, using Gemini on an API if I need it, and once or twice a month when I really need help on something difficult, I use something top-tier like Kimi K3.

This is what killed Gemini for me. The model might well be great, but the ecosystem Google has built around them is a confusing maze of not-quite-there products.

Ultra AI is like $99/month, and it is hard to exhaust unless you are running a lot of concurrent requests.

What harness do you use for Gemini? Antigravity?

AGY is best probably, yes. That's what I use. It works with others too, like OpenCode etc.

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/

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.

> 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.

I use Gemini because I feel like Google will win the AI race, and it’s Good Enough

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.

I suspect Gemini doesn’t focus that much on coding, at least compared to frontier labs. It’s not part of Gemini’s core business case.

Gemini’s integration with maps and search is more important for Google.

That’s a good point, and probably why it’s a multimodal model. I wouldn’t be surprised if it’s over index on code written by Googlers.

My long term base case, too, but they need to install Demis as CEO and I don’t think either side is ready.

> 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

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.

This thinking is why I am all in on GOOG shares. As a bonus, that means I’m getting part of Anthropic’s gains as well!

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.

It definitely steers. For example if it suggests travel plans, the booking links it provides give Alphabet a cut.

As you say it was subtle, along the lines of "oh, if you are planning on going to the place you are researching, here are some helpful links to places you can stay". Subtle, in that it didn't get in the way of main result, so I didn't mind overly. Insidious, as I only noticed because I wondered why it was providing those particular links and looked them up. I can't see how you could ad-block them if I did object.

And worrying, because these unblockable sneaky ads are just a first foray coming from a company that prostitutes its own app store searches, by making the first and most obvious result utterly unrelated to to the search topic. Instead it's who paid them the most to be there. That behaviour is why everyone dumped Alta Vista when an alternative came along. Alternative Android app stores can't come soon enough.

They already skim off 15% of purchases which I'm sure makes their Android operation return a profit that makes other industries drool. Debasing their search to ad a tiny bit extra on top must by driven pure greed. Senseless, as I'm sure it will come back to bite them in the end.

There are some subtleties though. For example, with Gemini search grounding (this was a few weeks ago), you cannot give it a domain whitelist, only an excludelist -- with Anthropic's API you can do both. For somewhat niche search tasks like "Find LinkedIn profiles matching this ICP", Anthropic wins there.

That being said, Anthropic is also so insanely expensive for everything I ended up switching that particular part to Exo instead...

Maps grounding is an amazing interface for Google Maps - „I’m at x, need to be at y by 5pm. Find me a route that’s walkable and has a good vegan restaurant and a toy store“

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?

Maybe the model does some tool calling on its own to figure out the times?

You can opt in on a per-request basis.

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:

  tools=[{"type": "google_search"}]

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.

Google AI person here:

Using grounding in Gemini is indeed backed by the same canonical data source for business information (like opening hours) as Google Maps. This stuff is available in its own API for a GCP fee, but we’ve built tooling to connect it to the Gemini agentic ecosystem as well.

So if I understand you correctly, Gemini has direct free access to the Google Maps API in a manner that others (people, LLMs) would need to sign up for API access and pay for?

That is my understanding, which is a nice thing. I use Exa and Brave search APIs separately but the Google bundling is convenient.

Ollama Cloud offers the same thing: they supply a web search tool bundled with cloud API inference services.

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.

The Gemini APIs have options for using Google Search grounding, or in simpler terms: to process a prompt by first using search to augment the context.

Right, I mentioned that here: https://news.ycombinator.com/item?id=49539339

jampa was saying that the Gemini 3.7 (a model) ranks higher on real-world knowledge. The point I was originally making is that I would trust any model by itself to answer real-world knowledge problems. If I want to know opening hours, then probably any of the models could find the answer with a web search tool.

Perhaps the Gemini API makes this easier, as the web search tool is built in.

This sounds great to combine with Wanderlog using an unofficial MCP I made https://github.com/shaikhspeare/wanderlog-mcp

I've just returned home after doing a 100 day "half lap" of Australia in a camper trailer with my family. I made heavy use of gemini to plan much of it. Getting packed up with a general destination in mind and telling gemini "we're leaving X town at 10am and heading to Y, where should we stop for lunch. My wife has coeliac disease, find us somewhere that does good gluten free options" was one of the many things I regularly leant on it for.

Never had a bad suggestion.

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.

I’ve been using Gemini for coding daily since 3.5, mostly on some pretty complex ML engineering projects. It’s great.

What’s an example of not being “reliable”?

If I strip the dependencies out, I'm using it on a 5MLoC C++ codebase, and I found it performs really really well. I am using the Opus 5/Fable in parallel and I couldn't tell the difference. Both models make mistakes here and there.

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.

I usually use OpenCode for all open weight models but for Gemini I use Google’s agy coding harness (or my own).

Venders coupling coding harnesses with their own models is usually a good thing. Poolside.ai has a combined harness with their own models that works well locally, and the DeepSeek harness with their models is very interesting.

Yeah, most people keep bashing my aibenchy.com benchmarks, because Gemini is on top, but that's because questions are not coding only...

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!

> trip planning app.

this has to be stong suit of ai agents any model

thanks! this is a very helpful one. I am going to try.

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.

"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?

Eh that one is on me, if I think too much about my HN comment I end up deleting before posting it. I rely on the 1 min `delay` set in the profile page to fix before it goes live, but for some reason this time it was set to 0.

1. That's a wild conclusion from his comment! Understand that it's not an "either/or".

2. I'd wager the majority of HN commenters don't read their own comment before posting (pre-LLM days).

I can read my own comment twice over and still somehow miss seemingly obvious wording errors.

And if you do a 3rd pass with an LLM, people will accuse of you not reading your own comment...

Once you've written something, it's incredibly easy to overlook minute changes to the text.

See: why authors wait days, weeks, or even months before editing what they've written (or, if you're more interested: cognitive regression, inattentional blindness, and the effects of misdirected saccades).

He didn’t write it.

> I asked Claude to fix the grammar of my comment

I read this to mean he wrote the comment, then asked Claude to fix the grammar (as many ESL speakers do). Sounds to me like he did write it.

Then I think you read it wrong, because you don't make that mistake unless you copy-paste your comment out of a Claude window and into the comment box.

A two paragraph hacker comment? You burned carbon for that?

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.

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