Gemini 3.7 Flash
blog.google
blog.google
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.
The grok test was ran through the cursor cli agent however.
It's not a bad model by any means, but I just don't know what situation I'd reach for 3.7 Flash first for. Google really needs a differentiator, especially given how hard it is to get an API key from them. They can't be high friction and non-pareto.
Maybe things there have improved some, but when I was looking it was a huge runaround.
Not sure how much the benchmarks can be trusted though: https://www.reddit.com/r/GoogleGeminiAI/comments/1vbq5vf/com...
I'm not sure what event-based traders are doing now, but back in the day NLP sentiment analysis was all the rage, so I'm assuming they've now incorporated LLMs too.
1. HFT doing ass-simple arbitrage where only latency matters 2. More sophisticated slower trading taking in deeper signals
Those are two points along a continuum. If you are reacting to an earnings announcement by having an LLM read the earnings release and listen to the call, getting the results a few seconds earlier lets you get your trade in a few seconds earlier. Just because "not HFT" doesn't mean "completely latency insensitive".
A single full wafer likely can run qwen3.6-27b alone. But won't be enough to run bigger models, which are pretty much all popular models.
Disclaimer: I work in Google so it might be that this link is not publicly well known
At a high level though, as a rule of thumb Google assumes that they're serving companies at Google scale first, and at a human scale second. For other companies it's the opposite. Generally what that means is the first experience you get with a Google product will route you through 8 different dashboards to set up ACLs before you've hired your 2nd employee.
Also:
> Google assumes that they're serving companies at Google scale first
So much this. I'm currently grandfathered in until the end of the year on Google's Search API, but the $35,000 they want to continue usage of my < 1000 personal searches per month, not going to happen. It has honestly been easier to use Anthropic to help me build my own search index & crawling infrastructure than deal with Google.
> It has honestly been easier to use Anthropic to help me build my own search index & crawling infrastructure than deal with Google.
That's great to hear! FWIW I agree with you that it's harder for independent professionals to get started on Google dev services than others, but the Search API is not developer service and was never intended to be.
Brave, Mojeek & Marginalia and EU Search Perspective have certainly been much friendlier to deal with.
I think that's actually a very interesting insight that would be helpful for PMs on GCloud to take note of. As a single founder, setting up Google Cloud, it's like they start out by assuming you're bigco, forcing (I assume most) of their users into a arduous process of removing components they don't need.
Google AI Studio is one of Google's solutions to this problem, but in typical Google fashion, it's bolted-on without any clear connection in the ecosystem. If you're also using GCloud, it's hard to remember it's even there.
OpenAI's platform, by contrast, is streamlined, easy to use. With Google, I feel like I need to wade through the documentation first before even using the darn thing.
My impression of anything enterprise (big or small) related to Google is that they just don't care / value solving it.
Which is sad, because "How does an enterprise customer pay for X?" is a non-trivial and incredibly important UX problem.
They have great technical solutions, but these are hamstrung by a frankly amateur understanding of how companies (startups to Fortune 500s) need to sign up, pay for, and track things.
From an outside perspective, one of the biggest gaps seems to be that internal Google product teams don't have to dogfood the full GCP et al. project/org experience. They get prebuilt billing structures (or just get to avoid them with internal cross-billing).
---
If I could waive a magic wand, Google would appoint an "Enterprise Czar", reporting directly to Pichai, who is a non-technical, retired founder / CEO.
That person would have one job: try to sign up, run (their team), and budget strategic Google initiatives (like AI) as a blind external party.
They would then deliver continuous reports to Pichai about how hard / easy this is.
Because potential Google customers don't give a shit if it's this internal team or that internal team's responsibility for integrating New Product X into GCP billing.
They care that the experience is terrible, filled with friction, and often flat out doesn't work.
I wanted a typical dev/qa/prod with medium specced boxes.
I was denied for quota, with an esoteric process for review.
I'd just made a case for deploying to GCP over AWD so got a bit of egg on my face. Went over and had it done on AWS in a few minutes.
A couple days later, the Google product team contacted me. I told them what happened.
It got escalated, and I ended up on a call with like 5 or 6 people from Google, some very senior. I told them what happened.
They made very concerned sounding noises and told me how this was a product failure on their part, how they'd get it corrected, etc... and they'd fixed my account so I could now make the machines. Of course, I was already deployed to AWS at that point.
That company grew and ended up with a pretty big cloud spend eventually. Google totally missed it.
I was at a new startup a few years later and decided to deploy to GCP.
Denied for quota.
“Failed to create project, The request is suspicious. Please try again” or “ You do not have permission to create a key in this project”. You can then navigate multiple screens in GCP to make it work but it’s a hassle compared to any other provider (OAI/Ant/OpenRouter or any of the Chinese labs).
It was literally two clicks, and didn't even leave the page: the dialog asked to create a project and type in a name, I did that, clicked submit and then it was selected as the default project. One more click and I had the free tier API key.
Not saying you didn't have that experience at the time, but personally I have had zero issues with AI Studio and consider it the most dead simple/fastest dev dashboard to get started compared to the others like OpenAI/Anthropic (thanks to Google's free tier that lets you skip billing setup annoyances just to play around with Gemini).
Despite what HN threads (that are also frequently confused and talking about GCP instead) portray as universal/widespread issues or the process being complex and time consuming somehow.
Sorry but it's not worth waking up with a 100k$ bill, fix your platform first.
"Experimental: The feature is experimental and limited in scope. You are subject to overages for around a 10 minute latency period."
Why in 2026 can't Google do a database lookup in real time? This is so ridiculous.
Biggest traffic day of the decade and our site was down because of google.
Using Google products in general is an effing nightmare as soon as you have to give them money.
The one thing you want in a business is to remove friction when people want to give you money, a concept Google has never been able to understand.
Spending money via Google Pay on Android is extremely easy, Google does know how to accept customer's money (in the consumer space)
Eventually I gave up and run a few hundred million tokens (edit a few billion) through openrouter.ai using Gemini Flash 1.5 to Flash 2.5
Every since price increases on Flash 3.0 I've stopped using Gemini, too expensive for basic classification, sentiment detection, ocr etc.
As other posters said Google assumes you are some bigcorp trying to use their products. The Vertex versus AI studio confusion did not help.
I do pay for OpenAI, Anthropic, and ElevenLabs keys.
The fact that there's two ways to get keys is also very confusing.
Isn’t this the insecure thing that gives all your Google API keys access to Gemini, even those that were intended to be semi public (eg maps API keys embedded in websites or apps)
Even if you have GCP projects, I'd still recommend the AI studio UI - easier to figure out. Also, you can easily see the free tier in AI studio and just use your API key from there.
Because whatever internal team owns AI Studio fought for approvals to do so and GCP didn't?
But ironically my experience is that Codex/Claude navigate GCP better than Gemini.
Now following up with Google support team without luck to find the logs. Prompts send to the model and the responses including the thinking was available in the ai studio. But it’s unclear where to find the same in console.
To make matters worse there is vertex api and rebranded to Gemini something and making it very confusing.
The primary reason for me has been that Google autonomously decides to downgrade usage tiers and then upgrade them again - and does this incorrectly.
Over the last week itself, in the span of two days, our account for first downgraded and then upgraded. This is despite matching the criteria to remain at the tier we operate at throughout.
Google Support (when you finally get to a human) has accepted that these are potentially bugs, but the first time it happened, we were rate limited so severely for ~4 hours that I find it really difficult to continue trusting Google.
By the way - I love Gemini's personality . it's phenomenal to work with
The reason i want my own harness - is the custom tools that i provide vs the low tier tools that come with the custom harnesses.
You'll would really benefit , if we could use Gemini in our own harness and not be forced to use it via agy . i've tried using gemini to circumvent - but not been successful.
If you see this - please reply here
I know this is probably a pretty small edge case, but it is a bit frustrating. Any other provider lets you sign up with an email and give them a payment processor/card, but because google wants me to only use their unified workspace for signing up, I'm completely locked out now.
Google being Google, their models tend to be better at finding, organizing and presenting information, from my experience.
I’d be very willing to try it out as an API, but it’s far too complicated to set up payment, and I don’t want to risk taking a wrong step and being locked out of other Google services. So Anthropic and Mistral get my money instead.
You reach for it every time you do a Google search
[my self-important Kagi shtick awakens, pokes at it's restraints]
What does this mean? Anybody can get an API key
Of course, we just used OpenRouter for testing and never touched a Gemini model anymore.
I guess I need to try harder. :)
Opus can't generate images since A\ doesn't have a diffusion model.
I did have one question about the tool, is it possible to set how many variations you want per step? I would rather be able to guide it manually at some steps where maybe I know pretty well what I want or just need minor tweaks, and then let it loose on others when really trying to experiment with an idea.
https://image.non.io/12275ee8-71e9-4941-823b-e51fec157b4d.we...
The agent is told to generate assets as part of the buildout. It gets to decide what the prompt is for them / whether to do postprocessing like background removal / what type of asset to generate.
I'm used to incremental Figma wireframe -> final product and working together with a designer.
EDIT: OKAY I see it's mostly the "image" generation, not so much the HTML... Noticeable in the food photos and the foodtruck/cart photo
i do wonder why gpt sol was not compared here but honestly it's not really known to be the best at UI
a fable 5 comparison would've been also interesting and likely the best.
Plus is the ramen in HK even any good?
This is testing if it can match visuals that have already been established, and represent them with all the tools available to a web developer. The ramen example was chosen in particular because there are a lot of things that aren't easy to do with CSS, and require creative strategies: 45deg button cuts, angular repeating pattern elements, blending of raster art and svgs, microglyphs, low contrast subtle elements, etc.
Don't ask yourself whether it's a good design, as yourself whether it's a good test.
This is the build step generated by my diffusion-based ui tool's copy-for-agent action.
Edit: oh wow, diffui looks nice!
(I don't have the Bodega signs, but I'm thinking of shit like this, from a quick google: https://linkstub.com/en/wet-wild-foam-party)
“Let me explain: on average, illustrators don't make any money. They are already one of the most immiserated, precarized groups of workers out there. They suffer from a pathology called "vocational awe." That's a term coined by the librarian Fobazi Ettarh, and it refers to workers who are vulnerable to workplace exploitation because they actually care about their jobs – nurses, librarians, teachers, and artists.
If AI image generators put every illustrator working today out of a job, the resulting wage-bill savings would be undetectable as a proportion of all the costs associated with training and operating image-generators. The total wage bill for commercial illustrators is less than the kombucha bill for the company cafeteria at just one of Open AI's campuses.
The purpose of AI art – and the story of AI art as a death-knell for artists – is to convince the broad public that AI is amazing and will do amazing things. It's to create buzz. Which is not to say that it's not disgusting that former OpenAI CTO Mira Murati told a conference audience that "some creative jobs shouldn't have been there in the first place," and that it's not especially disgusting that she and her colleagues boast about using the work of artists to ruin those artists' livelihoods.”
OK so after all that, THIS (bad foam party posters) is what we get!
I don't know how well this actually translates to AI, though. We all understand that AI art has a pretty low quality, but sometimes low quality is enough. If I want an image for my D&D character that only I and my DM will likely ever see, I am fine with a 7 cent AI-generated image, but I'm not willing to pay 150$ for an artist to do it - not that I don't value their time, I just don't value the image that much. Before AI this was the same, I'd just have used an image from Pinterest and thought "Well, this isn't exactly a good match, but I can't find anything better".
But I assume real illustrators do things like illustrations for children's books? I'd like to believe that those are still done by actual people, not AI.
The thing is though that most of the nerds here are oblivious to all that, so it will take a while for them to fully acknowledge what's happening. In a positive note, I'm here on a AI thread discussing AI-image generation and I haven't yet seen any link to the dreaded pelican on a bicycle thing, so hopefully we're getting into the right direction.
so far, IMHO, the best has been opus and fable\mythos.
Gemini 3.7 looks the best. Opus 5 looks almost as good as Gemini. Grok 4.6 looks pretty terrible.
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>
This suggests you primarily use Safari.
While the bike renders, the pelican doesn’t in Chrome and Firefox.
Probably one of the more serious defects I’ve seen with the pelican. It’s one thing when animated SVGs have bugs, but another when plain ones do.
Anthropic for instance announced a couple of days ago that they are making Sonnet's 'introductory pricing' permanent https://xcancel.com/claudeai/status/2086891169217122586
Those kind of workloads would be hit by an end of introductory pricing. And it's exactly the kind of cases that are not very price sensitive. Where we are price sensitive we track new model releases closely, where we aren't other issues get priority as long as llm performance is good enough
I work for Google and their free, internal Gemini API isn't quite as graceful. They once turned down a model arbitrarily and it broke our tests. I had to scramble to fix it, then build warning systems for the turndown as well as a special validator to make sure any upgrades make the same determinations.
Or maybe one of your installed extensions modifying the page (presumably private tabs disable those)?
Gemini 3.7 Flash produces SVG that works in all three browsers. My vibe-coded tool for rendering those SVGs in a document was stripping some attributes.
I've fixed that bug now and https://tools.simonwillison.net/markdown-svg-renderer.html#u... shows the pelicans as they should have been rendered across all three browsers.
(Here's the fix: https://github.com/simonw/tools/commit/78fca0e647a9e20eff169... )
> 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.
I'll use it locally too, but we use Cursor for work
You don’t know what you’re missing until you’ve seen it. For me it’s almost like going from standard def to HD for the first time.
(This applies to other open models too — Kimi K3 in real world feels below Opus 5 in terms of raw intelligence, but significantly above Opus 5 in usability and personality. And no silly refusals — the model feels like it’s working for me; not working for Anthropic who’s always holding a leash over the model while I pay for it).
I gave DeepSeek $50 around june, and I haven't been able to exhaust them yet. The model is super cheap and more than enough for my needs.
I'm my opinion, Flash V4 is less pedantic than OpenAI models, less prone to unsolicited prescriptions and less prone to "helpfully" reinterpreting my instructions (wrongly, of course).
I always thought it was a little odd that gpt-5-mini was the leader for so long when more popular benchmarks placed gpt-5-mini further down the roster, but it seems you had the same result too.
How much does that matter if it's reset at every turn?
Gemini[0] for example passes along a snapshot of the reasoning state but it's not the equivalent to keeping all the reasoning tokens in the context.
[0] https://ai.google.dev/gemini-api/docs/thinking#signatures
Edit: Apparently it does take the same space in the LLM latent space so I was wrong.
I'm not sure if that's right. I only just recently learned that the "snapshots" (aka cached tokens) necessarily contain the entire context history, not just an image of a "state as of the final token" (well, it is the state as of the final token, but that state contains the whole history). So I'm not confident of my grasp of the structures here.
Your Prompt 1: Prompt Content 1 -> cache-1
LLM Response 1: <Thinking>Thinking Content 1</Thinking> Response Content 1
Your Prompt 2 (client side): prompt-1 + response-without-thinking-1 + Prompt Content 2
Your Prompt 2 (server side): cache-1 + response-without-thinking-1 + Prompt Content 2 -> cache-2
LLM Response 2: <Thinking>Thinking Content 2</Thinking> Response Content 2
Etc...
Edit:
I've realised I was incorrect, the thinking doesn't get passed back and forth but the latent snapshot does which result in using memory just the same.
But that's not the full reasoning token context, just a snapshot of the latent state at the end of it, no?
Have a look at the gemini ones they're pretty small.
Looping back latent state isn't that easy. The hidden data is the entire contents of the KV cache which can be massive. I don't think any provider is trying to loop the KV cache through the client, and neural compressions of the reasoning would be lossy / an advanced technique that is still firmly in the realm of research papers, as I understand.
I don't think I want a gastown-style "just yolo everything" approach, in fact I really want more control over how decisions are made and with what info. Does this exist?
On complete, it moves the user story from Doing to Done. I also have a MEMORY.md file that the agent read and writes in the beginning of a new conversation and at the end of our conversation to update stale information. These files are referred to every time I start a new conversation.
Regarding forking, I know Codex has such button underneath each message that lets you fork the whole conversation. I usually do that when I want to sidetrack and discuss something.
I use no SKILLS or commands like /goal. I’ve come a long way with just prompts and markdown files. Its all a different way of encapsulating instructions anyways.
However, I've noticed 2 drawbacks with Luna. Context rot is much more palpable than Terra and Sol. It tends to get confused and go into rabbit holes when it's context gets filled up. In addition, when instructions are vague, it performs poorly and tends to write way to more code than necessary, but that is to be expected of smaller models. In all, for clearly defined, bite-sized coding tasks, Luna's price-to-performance has been insane. It might have very well commanded the price tag of Sol if it came out just a year ago.
It's so freaking fast, but you gotta tell Fable to watch Deepseek like a hawk or it'll go off the rails.
We run an agent company and we do a bunch of different things with agents. Where we used Gemini before Deepseek v4 Flash is taking the lead on price. It's like 5x cheaper than 3.6 and well 2.5x cheaper than 3.7 "introductory price". Comparable quality.
You define roles for different agents like this:
modelRoles:
task: fireworks/kimi-k3-fast:high
plan: fireworks/kimi-k3-fast:max
slow: fireworks/kimi-k3-fast:max
smol: fireworks/deepseek-v4-flash-0731:low
tiny: fireworks/gpt-oss-20b
vision: fireworks/qwen3.7-plus:high
designer: fireworks/qwen3.7-plus:high
advisor: openai-codex/gpt-5.6-sol:high
main_worker: fireworks/kimi-k3-fast:high
fast_worker: fireworks/deepseek-v4-flash-0731:low
vision_worker: fireworks/qwen3.7-plus:high
research_worker: fireworks/glm-5.2:medium
code_worker: fireworks/kimi-k2.7-code-fast:high
review_worker: anthropic/claude-fable-5:high
security_review_worker: fireworks/kimi-k3-fast:max
minimal_worker: fireworks/gpt-oss-20b
default: fireworks/kimi-k3-fast
task:
agentModelOverrides:
task: "@main_worker"
sonic: "@fast_worker"
scout: "@fast_worker"
designer: "@vision_worker"
librarian: "@research_worker"
reviewer: "@review_worker"
security-reviewer: "@security_review_worker"
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.]
Regardless you are right - I can roll my own.... but why
I'm thinking about inlining an AI chat window directly - I guess I might fire off a prompt to do that right now.
But I don't do "compare visual output to target" as much as 3D reasoning type tasks (eg: "Build a G1 curve where the -X face meets the +Z face" etc)
Luna way cheaper. DeepSeek used to be, but I think it's somewhere on Sol's curve after the price hike.
Damn, Luna on max is as good on DeepSWE as Kimi k3, I think I dismissed this model unjustly.
Kimi K3 is a beast though, just costly.
I pay for ChatGPT and Gemini, and while Sol is a total beast with anything text, it still poisoned my cucumber bed. Which I will be bitter about for at least a few years while the bed recovers. Gemini (even flash) is exceptionally talented at viewing photos and telling you what to do/what it is (and telling me I just misidentified the problem with my cucumbers and spraying off the "bugs" actually just spread the bacteria everywhere.)
However afterwards, still feeling odd that I didn't see many bugs, I double checked with Gemini who is my typical standby for vision tasks. Gemini pointed out that its actually the fungus Corynespora cassiicola...which spreads by water splashing, and can live in your bed for years once it gets into the soil.
So now having two answers I need to research and check myself, and sure enough, it was obvious match for the fungus and didn't look much at all like the mite damage.
There is another story a few days later of 5.6 looking at my heatpump install and telling me it was probably borked and to call an expert. Gemini told me to just open a valve and it was good.
I don't really trust any other LLM besides gemini for vision stuff. It also tracks as google is the only lab still pursuing vision related tasks.
I also had really poor results with all major models from plant identification to plant treatment which is weird considering how much training material is out there.
Reminds me of recent headline: Chinese farmer kills 25 acres of crops due to LLM pesticide recipe: https://www.tomshardware.com/tech-industry/artificial-intell...
And in a typical enterprise environment dealing with documents, images, and broader business reasoning skills matter. Agents are not just for code and text :)
Gemini doesn't have adjustable reasoning effort (at least on the graph) so each of its curves is just one point.
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.
I know, it’s cloud storage, a NAS lets you own your data, but for non techies who have a bunch of photos and videos; it seems like an easy recommendation.
I think its the same price..
https://ai.google.dev/gemini-api/docs/pricing today has 3.6 Flash at $0.75/$3.75 until December 31st 2026, then doubling.
https://web.archive.org/web/20260809105129/https://ai.google... Internet Archive copy of that page from 9th August has 3.6 listed at $1.50/$7.50 with no mention of the price changing.
The selling point for gemini continues to be speed and particularly end-to-end response time.
> Wait — I don't understand where you've got to here. Re-pitch that: give me a little bit of context, talk in ASD-STE100 Simplified Technical English, and use the ubiquitous language from CONTEXT.md.
This is what I do too.
Edit: and Sol medium actually has the same AA intelligence score as Gemini 3.7, and has >7x fewer tokens, actually making it faster
Worth noting that OpenAI just announced that they got the full GPT 5.6 Sol model running on Cerebras at 750 tokens per second. No announcement of the pricing though...
Good catch! You're right to point that out. My previous marketing copy missed that specific detail. Thank you for bringing it up!
The application isn’t so complicated that you need opus level reasoning or code writing, we need “good enough” data retrieval and processing with natural language queries and the ability to answer follow up questions.
For that Gemini works well for a decent price.
Google AI Mode consistently gets me consistently good results and good speeds. It really changes what "googling" is for me.
I find that GPT models and Claude tend to talk in strong slangs and in-group jargon, but love Gemini's massive general knowledge corpus — reminds me of Richard Feynman from his lectures.
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.
That's why DS4 already had a huge price hike announcement.
Deepseek as a company can just increase prices for the crazily cheap cache they have, that's their only lever.
Well, compared to 2 months ago, it's no longer 100x more expensive for similar levels of quality...
If they continue monthly-ish releases by 3.9 - by Halloween - they should be close to the best in terms of what you get for what you pay for.
In 2 months, they've gone from basically the bottom of the pack to at least being somewhat usable and competitive.
OpenAI and Anthropic release in a month, and change things. OpenAI is claiming to be close to an Astra release - but that seems like a Fable type release - where they're just releasing a better more expensive model, not more cost effective models.
It's terribly outdated and way overpriced now and they do need something to compete at that "fast, cheap and ok" level.
I could probably do text only for my workflow (feature development/debugging for web microservices) but sometimes it is easier to just toss a screenshot into the Claude prompt, so that gives it an edge.
If your workflow is 100%, certifiably never ever going to involve an image, then yeah, this isn't going to be huge.
The only thing that works at scale is gemini flash.
> 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?
It was probably to placate some kind of general internal pricing/revenue benchmark that doesn't account for new model releases. Politicians do shit like this incessantly and it reeks of bureaucracy.
"Hey, we are not in a race to the bottom. This is our usual pricing, but this now is a promotion because we know we're coming from behind and need to entice users."
They're drawing a line in the sand on monetisation and signalling that to everyone, while in reality offering it a deep discount (no idea if profitable or not) knowing that this model will probably be obsolete before then.
Flash is great, but Codex models are also fast, as is DeepSeek v4 Flash.
Anyone who's Anthropic-pilled should really get out and explore and see how unbelievably terrible they are when it comes to speed and cost vs quality.
Anthropic has good models, they're just way too expensive and slow for what you pay for.
It can regularly cost more than Fable, take longer, and deliver far far lower quality.
I'm much more interested how this compares to Luna - which on price is terribly - but at least on quality the benchmarks make this look competitive / usable.
If Google continues monthly Flash releases like Sundar said they would, and they continue to have this much of an improvement in cost/quality - then in a few months this could reasonably be very competitive with the best of the best.
It is not there yet, but at least it's super fast, I guess.
One word: Haiku
Although maybe that was competitive when released? I don't recall, but it's an expensive, outdated model now.
Less than half its price.
More than 50% discount.
OpenAI has too much money. They’re spending their money in stupid ways.
I think they got some data center deals very cheap when no on was thinking about data centers. Dario didnt want to take that risk so Anthropic didnt make the deals earlier but now paying Google and Elon higher rates.
As a business Google might want to focus on fundamental research 2-3 years from now and not compete on who acquires more money losing customers. Just stay little behind and invest money better.
I am not sure they can afford to subsidize Gemini more than they already do.
Future growth needs to materialize for this to be profitable. Right now what they have is a cash-flow negative reality.
> There is no extra unsold capacity to use.
I have no evidence of this.
Neither I have evidence that they would be in the position of subsidizing further AI usage.
I continually don't understand how nobody points out Flash 3.6 being much faster than any other model, and seems 3.7 is even faster still. That by itself is a major selling point.
> Introductory pricing expires on December 31, 2026. Starting January 1, 2027, $1.50/1M input tokens and $7.50/1M output tokens will apply.
I wonder if this counts as evidence against that hypothesis? That multi-modal is struggling to keep up with SotA and the best they can offer is competent and fast?
Google probably crunches more tokens daily than the other labs combined, just because basically the entire global population uses Google (sans china) and Google has shoved Gemini into everything.
This feels absurd to me (my gut is "I want the SMARTEST model I can get!!"), but often I find that my experience of using a flash/sonnet model for every-day workhorse coding they are better.
Its not the same thing, but when I think of that I am reminded of working with some engineers in the past who are incredibly smart and have PhDs (or to put it another way, over-qualified) and they were crap engineers because they'd just not be able to focus on the task and ONLY the task at hand and would get easily distracted by the "why" or "more interesting" things when I just asked them to fix a simple bug or whatever. Again, its not the same thing at all, but it certainly comes to mind when I think of this or experience a pro/opus model suggesting we make huge refactors when a tactical fix is all that is required etc.
Of course, the opus-sized models are great when it comes to huge comprehension/research/debugging efforts where the deeper reasoning is actually useful.
If it implements something simple like a file export, it just knows that the file should have a meaningful name. Vibecoded feature beats most software's lazy "untitled.png".
So, yes, I want the smartest model even for simple stuff. Maybe especially for simple stuff because the tokens burned will be trivial so the cost doesn't give lower models a comparative advantage.
Consistently, lower intelligence models provide worse results in my own work. But I don't have evals on my side, just vibes.
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?
At this point, I think they're mostly targeting Google One and Workspace subscribers, except doing worse compared to Microsoft because they don't have Microsoft's huge enterprise moat built from their DOS and Windows days.
Per-token cost isn't a great metric given that some use way more tokens than others.
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.
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.
1) It makes sense to try 3.7 flash before cancelling.
2) Prompting models to be honest is surprisingly effective in my recent experience. But only if they listen to instructions.
I was paying for Ultra, then downgraded to Pro and at this point I'm near abandoning it. Agy as a harness also has a tendency to constantly request significant elevations to perform routine operations.
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.
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.
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.
Reading the google blog and these discussions makes me feel like I'm taking crazy pills, seriously. Side-by-side comparisons with the exact same inputs or it didn't happen, that's my rule going forward. Test all the things, believe nothing.
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)
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
Later you can make decision to drop low performing ones.
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.
I've gotten good results with it, but it definitely is more hands on.
Introductory pricing until December 2026 implies no significant Gemini Flash developments until the next year.
Gemini 4 is apparently just around the corner so unless there's a 3 month delay... there's at least a new Flash update.
Bonus feature from Gemini: a toggle to opt back into Google Assistant, but it doesn't work. Still stuck with Gemini.
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.
They should call it 'face saving pricing after we realized just how terribly did we mis-price the flash 3.5'
> since google betrayed us with those price hike, people already spent their time making their production pipeline less dependent on google since then.
This is my first hand experience. I spent at least $3000 on gemini-3-flash-preview. And exactly $0 total on (3.5+3.6+3.7)
a decent model with a decent harness will determine when the knowledge base is lacking and attempt to fill the holes; thus the good general models can be very easily brought up to speed on niche domains.
Think like a regulator.
Some model are more aggressive by default, some are more verbose by default. To get the result you want for your specific application, you run experiment with prompts and parameters.
and now we have ai agents to automatic migrate the system with new models. in the past we would need to spend hours to design the prompts, then test the output, then write codes to babysitting it. nowadays any ai agent can do it effortlessly.
Personally, I feel like Google blundered on their pricing because while I was using the free version of the Gemini harness, they took away most of the free limits and made people move over to their Anti-Gravity harness for no apparent reason. I was about to splurge for a Pro sub since I already used Google for extra storage but putting up limits like they did made me not want to trust they wouldn't do more price shenanigans. Now their models are behind and it seems like they're scrambling.
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
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.
Antigravity is their agentic coding app / IDE. There is two products, one is chat-only the other is more standard IDE.
Google AI Studio is a consumer/developer playground with a in-browser IDE meant for prototypes or demos, there is a gallery of demos etc. Easily shared, easy key access.
Vertex is the AI offering from the GCP side of the company, that is going to target more enterprise or business solutions (scaling, data governance, security, production deployents, etc)
But, I think it’s also based on what they are being used for, most LLM users are still mainly SWEs or similar as I understand and there’s a ton of data to train them for coding.
My point is mainly that was never the pitch that got ai the hype it did and imo doesn't justify the valuations even if we all lose our jobs to ai. Because it no longer seems like they even think its making other jobs go away.
Same with those that use to improve marketing outcomes for SEO and such, now there are AI based reports with automatic improvements.
Finally on other domains you already have robots on supermarkets, fast food, and gas stations, where the customer does the work of the (now gone) employees without any kind of price reduction.
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?
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.
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?
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.
I honestly don't know what I would want more/better than what I was already getting... so I'm curious if I will see any improvements with 3.7.
We still don't have a 3.5 Pro, and along comes 3.7 Flash?!
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?
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.
each new checkpoint can benefit from better reasoning training, RL on specific tasks and more synthetic data
So why do they seem to release around the same time ? my guess is because they time major releases around quarterly earnings, investor meetings and other important business milestones. Once one company announces a major update, the others also have an incentive to ship their latest checkpoint rather than look like they r falling behind.
Somewhere in the same neighborhood as GPT 5.6 Tera and Sonnet 5, depending on the bench.
And yet the entire thread here is people bitching that it's neither 5.6-sol nor Opus or Fable 5.
BTW why are OpenAI and Anthropic even releasing models like terra/luna and Sonnet?
Why? Just why?
Is there a... market?
For you can't have it both ways: either Sonnet and terra/luna make zero sense for Anthropic and OpenAI or Google is a player.
Only models that matter are the edge fable class models people use for code, and Google struggled with that.
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.
Google's Opus competitor is 3.1 Pro Preview which is essentially obsolete (competed with Opus 4.6). They do not have a Fable/Sol competitor.
I guess Google's betting on consumers being price-elastic (preferring to tradeoff intelligence for significant cost savings)
FWIW neither is xAI, there is no "big 3". xAI has had momentary peaks (I think they are having one right now) but they have never been able to claim to consistently push the frontier in any particular direction. You can also infer they aren't a frontier lab from the fact that they sell their compute.
And it's arguably not crazy, at least if SemiAnalysis's estimates are to be believed:
* 20% of all TPU shipments from Q3 2026 through Q4 2027 are sold to SPVs serving Anthropic ($150B of contracted revenue); vs
* ~$12B ARR for Gemini.
Because they compete for the same scarce resource, the result is a resource crunch for the group that's lost: https://www.latimes.com/business/story/2026-05-18/inside-ai-...
Citation needed.
also, why can't a massive company do two things?
There are other factors at play, but they're more recent/second-order.
It's a variation of opportunity cost. A company that has an opportunity to take $1 and make $1.50 on it can't justify an opportunity to spend $1 and make $1.25, even though a less profitable company may make a good living on that. When considering capital allocation, Google has to consider the opportunity cost of investing more into their highly lucrative ads business. Another company that has no access to such a lucrative business uses different opportunity cost when it comes to allocating capital. It can easily be the case that Google could end up justify being in the business of renting out shovels and end up chased out of the business of using the shovels to create AIs entirely because that turns out not to be where the money is. I'm not saying that's obviously inevitable; I'm saying it's a possible and reasonable outcome.
That's why even though the industry produces giants, these giants can never just eat everything. Even though it seems like they have all the money, it isn't practical for them to try to do everything and in fact limits get hit very quickly for anything other than the primary, lucrative business.
Apparently there is no snappy term for this in the business space, according to such AI searches as I have run.
Are they? They provide AI overview to majority of web searches, and that alone requires enormous resources. Anthropic, OpenAI and others only serves their AI customers. Regarding the power of their model, my own experiences are that it doesn't fall behind. I've done many successful projects already, including quite a big one in Pascal. So no, I don't feel any difference between Gemini and others. I think it's just a long lasting fashion to whine about Google and its services.
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.
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.
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.
Google continuing to ship generations of Gemini using its mediocre teams is an existence proof of that
For genuine advances one needs the Hintons and Vasvanis, not yet another bunch of mediocre Kookaid drinking product managers at Apple and Google
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?
Flash is better than Pro for now.
Path A: Deprecated, do not dare use
Path B: Beta, do not rely
Also impressed with Grok for some stuff.
If so, now I understand why they didn't want to release this model