Original images: https://image.non.io/neonRamenDesigns.webp

Gemini 3.7 build: https://html.non.io/neonRamenGemini3.7

Opus 5 build for comparison: https://html.non.io/neonRamen

Opus is still best in class for this, but it's worth noting how well Gemini 3.7 does vs a more comparable LLM price wise, which is Grok 4.6: https://html.non.io/neonRamenGrok4.6 . I thought Gemini would blow Grok out of the water (it generally has in the past), but Grok has really caught up.

It's scheduled to double in price on December 31, 2026, but who would anticipate still using this model five months from now? Especially since 3.6 Flash came out just three weeks ago!

My first effort with default thinking level produced an ambitious pelican, let down by a flawed bicycle: https://tools.simonwillison.net/markdown-svg-renderer#url=ht...

Then I ran it on high, medium and low thinking levels (oddly minimal is no longer an option, which WAS an option for 3.5 and 3.6) and got a pretty excellent pelican for the first two:

https://tools.simonwillison.net/markdown-svg-renderer.html#u...

UPDATE: That was in Safari, but as pointed out in the replies here the pelicans do NOT render well in Firefox or Chrome! Best guess is that's because of this invalid filter in the SVG:

  <filter id="shadow" x="-10%" y="-10%" width="130%" height="130%"></filter>

https://deepswe.datacurve.ai

> Starting January 1, 2027, $1.50/1M input tokens and $7.50/1M output tokens will apply.

Compare this to Luna which is at $0.2/1M input ($0.02 cached) and $1.2/1M output.

https://developers.openai.com/api/docs/models/gpt-5.6-luna

I've always considered the Flash series of models to be for low-cost, high-volume, mostly text-based use cases (e.g. summarization, parsing, formatting), emphasis on low-cost.

[edit: ah, benchmarks here: https://blog.google/innovation-and-ai/models-and-research/ge...

more of a Terra than Luna competitor which is an interesting positioning. I feel like differentiation at the mid-tier of models is pretty difficult.]

So it's better than 3.6 Flash, at half the price. I've been pretty excited about Gemini models recently, they just feel so fast after spending most of the day at work waiting for Opus 5.

The selling point for gemini continues to be speed and particularly end-to-end response time.

Google AI Mode consistently gets me consistently good results and good speeds. It really changes what "googling" is for me.

Unfortunately, it's often not strong enough for heavy refactoring and long running development loops.

I fail to see the usecase where DS V4 Pro is not enough, but Flash 3.7 is - except multimodal.

Luna is similar, and also 8x cheaper. Source: artificialanalysis

The only benefit I can see is the speed, that looks to be outstanding, probably thanks to their TPUs.

> Coding and agentic tasks: Significantly higher quality on real-world software engineering and agentic benchmarks, improving issue resolution and reducing failed agent loops.

> Web development and stronger design parity: Generates higher-fidelity desktop and web application code directly from design mocks, with strong gains in design adherence and in auditing existing codebases against mocks to verify 1:1 design parity.

> Promotional pricing: Gemini 3.7 Flash will be available at an introductory price of $0.75/1M input tokens and $3.75/1M output tokens. We’re also applying this new rate to 3.6 Flash. Introductory pricing expires on December 31, 2026; after, $1.50/1M input tokens and $7.50/1M output tokens will apply.

Still no sign of 3.5 Pro. Will have to test it, low expectations given every other model from the Gemini 3 lineage, but one can hope. Just struggle to understand the promotional pricing being temporary for four months. Given this industry, I'd be hard pressed if 3.7 Flash was still in use by end of year, so why not make it the official pricing?

[0] https://ai.google.dev/gemini-api/docs/latest-model

It hallucinated in both cases despite being given an API key and building a lot of pipes to access data using this. It was a simple "oh shit" fix moment for the model, but weird how eager it was to hallucinate despite the process being designed for it to be data-driven.

We should move past the idea that benchmarks alone tell us whether a model is getting better. I would've had the same experience a year or two ago with 1.5, and the solution would've been similar (keep prompting). I've been investing time into making system prompts and input prompts more meticulous, but the fundamental "it will make shit up" problem still remains, even though it shouldn't when the job involves calling tools.

I know this sounds like I'm expecting superpowers of it (I'm not), but my point is just that these incremental benchmark gains may not reflect user experience.

Well, I think I'll stick to 3.6 for now. Based in a very scientific sample size of exactly one attempt each: https://imgur.com/a/fDOkBDm

Both got the same prompt and example screenshot. I mean, they both suck but that's normal this early in, but the 3.6 version (first screenshot) actually changes the displayed threads depending on selected categories, and the messages of whatever selected thread, as obviously described in the prompt. You might say it more or less does what it should. Both versions have an ugly flash/jerk in the category pane when selecting/deselecting a category, so that's a wash.

The 3.7 version doesn't work at all, i.e. it always shows all threads, and no messages for any of them. I can post new messages in threads but they don't show up, and it doesn't even increase the message counter for the thread. I guess it's a matter of taste but I don't like the look either, while 3.6 actually is in the spirit of the screenshot I added the prompt, using 11k of CSS versus 3.7's 16k. The code is also less, and the backend split into 3 files (instead of just 2 as 3.7 did it), so assuming it sucks in either case, it'll be easier to read and massage.

edit: geez, 3.6 even properly fades/disables the "new message" button when no thread is selected, 3.7 didn't bother which is smart since everything else is broken anyway. Maybe it's better at really complex things, but for simple things, what I'm experimenting with, I already saw enough.

The intelligence index vs cost pareto frontier is crazy now, its basically a flat line with 9 people all at or right at the edge of the frontier along various parts of the graphs. Insanely competitive right now.

> Introductory pricing expires on December 31, 2026. Starting January 1, 2027, $1.50/1M input tokens and $7.50/1M output tokens will apply.

Also have to compare to the recent Grok 4.6 release, which appears to straight up be better AND cheaper

Hard to understand why anyone would choose 3.7 Flash under these conditions.. is Deepmind still a frontier lab?

Personally whenever I use Gemini I've just been using 3.1 Pro because I've had insane trouble with them getting things incorrect like this. Hopefully they'll fix it soon / they've fixed it with 3.7 Flash.

So i launched agy and find seems it have a 3.7 flash i thought its latest,until i see this article i know it's just released

after serveral rounds prompt(the initial prd prompt is by chatgpt), it use 2.9k user message token and 42.5k reponse tokens with gemini 3.7 flash(low) after i check the status, i got a working on device translator, its cool

the model seems also p and retty fast and the generated app looks good and easy to use

but agy cli is a bit unintuitve and i also check the guy that release the latest agy, seems the guy does not commit much ? or perhaps agy does not open source and only use github as a issue feedback channel

It did fine on my usual benchmark about configuring old Sparc hardware, maybe output slightly faster than before. Even included something new to check in the firmware.

I honestly wonder how long this calliope can keep playing before it crashes to the ground.

(I have no business relationship to anything mentioned here except as a regular retail customer who went bargain-hunting)

Only models that matter are the edge fable class models people use for code, and Google struggled with that.

I'm currently using omp with Kimi K3 as the planner and DeepSeek v4 Flash 0731 as the implementer, or CC + Fable for planning and Opus 4.8 for implementation. For API(not coding), I just use DeepSeek v4 flash 0731 and MiMo.

I'm pretty happy where I am, but I'm wondering if these new models provide some new kind of advantage

Bonus feature from Gemini: a toggle to opt back into Google Assistant, but it doesn't work. Still stuck with Gemini.

Maybe this has to do with all the RLVR it went through, where reasoning through difficult academic/coding problems caused it to think and speak a certain way.

The benchmarks looks great but it doesn't feel as legible, so maybe it's more meant to be an agentic model rather than an everyday model whose outputs are read by humans?

GPT-5.6 or Claude models haven't delivered to me non-running code in ages.

Whenever I have Gemini in the flow, it's fast, but mistake riddled. I have low confidence in the output.

I've had some success with Opus driving Gemini models. It's pointless for GPT family since Sol is cheap enough or can drive terra/luna for arguably better performance, same speed, and better outcome.

As for all of the talk in this thread about modalities. Every SOTA model takes screenshots and verifies work now. Grok-4.6 does this, Luna does it, etc. They can also all work _from_ a screen shot or mockup provided.

I don't think it's a major selling point when every model can do it well and reasonably fast.

That said, eagerly awaiting "pro" and improvements to antigravity.

We are now considering dropping support for the model family all together. All of their models require significant scrubbing of errant thinking blocks, inner monologues, and it's consuming more engineering resources than it's worth.

this is hilarious. it is not 2025 any more, by Jan 2027 there will be at least 3 newer generation of models (from other provider) released already. nobody would use flash 3.7 at that time.

sure we used to cling to gemini models in the past, demanding 2.5 models to continue to serve, but since google betrayed us with those price hike, people already spent their time making their production pipeline less dependent on google since then.

heck, even now I'm not sure I even care if they cut the pricing even lower. there are too many models with cheaper price and similar performance now.

Introductory pricing until December 2026 implies no significant Gemini Flash developments until the next year.

today i wanted to link agy to a google cloud project, for that i had to enable 5 different APIs in google cloud UI, then create a subscription for Gemini Enterprise (whatever that is), then link it to a project, then assign it to a user. and after all that, agy couldn't find the subscription.

the best part: i couldn't cancel the subscription. so i just paid $35 for one month and left it.

Also impressed with Grok for some stuff.

They compare it to 5.6 Terra, however https://cognition.com/frontiercode puts Terra at about 1/2 the price

Also have to compare to the recent Grok 4.6 release, which appears to straight up be better AND cheaper

Hard to understand why anyone would choose 3.7 Flash under these conditions.. is Deepmind still a frontier lab?

https://threejseval.com/ranking

I also think Google is still the best at fitting the most overall intelligences into their models, but for some reason it seems like the model architecture is just bad.

I'm mostly serious here. Aside from Gmail and search, it feels Google doesn't have a good track record for maintaining things.

It really makes me wonder... if key people are leaving, what's going to happen? The Google graveyard is pretty big.

Somewhere in the same neighborhood as GPT 5.6 Tera and Sonnet 5, depending on the bench.

I hope so. It seems mind boggling to me that an user needs to surf around different sections (plural) of google cloud console, then this Vertex and do a dozen clicks to issue a simple key.

In my evals 3.6 Flash (pre price change) was usually a bit more token efficient than 3 Flash, so I‘m expecting same or even lower cost-per-task on 3.7.

Maybe a play by Google to deprecate 3 Flash soon.

Large lumbering enterprise with massive inertia. Where innovators leave as soon as they get a better offer.

None of the authors of the seminal "Attention Is All You Need" paper are still at Google.

Fast forward a decade and Google will be reduced to hiring the kind of mediocrities who deign to work at IBM and Accenture.

Theoretically there is some difference between Fable and Opus or Grok and GPT, but at the end of the day I'd look at the bottom left of my screen and to my amusement find out that for the past 3-4 hours I've been using model ______.

If the results are semi-decent, I'd keep it on, if not - I'd randomly switch the model and try again.

Actual thing that would affect my selection would be a number of unused tokens I have left for a model ____ for this week.

Maybe it's cause I'm using those for programming and log parsing and all of them are decent enough, but other than that - there are no leaps I see.

1. How the new model performs against the other top models in the same category.

2. The pricing of the new model against the other top models in the same category.

but luna is hard to beat @ capability / cost

For Google, this is still gemini-3.1-pro-preview, right?

We still don't have a 3.5 Pro, and along comes 3.7 Flash?!

What am I missing?

They can't seem to be able to produce a frontier model, fine.

Just be quiet about it and work hard until you manage to put one together.

[EDIT]: Come to think of it. Maybe they're trying to build the Toyota corolla of AI ... let's see if that wins them the battle long term. I personally doubt it.

Same training dataset, same software, same hardware, same architecture...

I'm wondering what they changed actually for the model to be more powerful if the benchmark results are real and relevant.

Maybe just tweak settings or the reasoning prompts and called it a new version of their model?

If so, now I understand why they didn't want to release this model