Gemini 3.7 Flash

Here's a image->html test. Gemini has always swung above its weight class for vision work, so I'm always eager to try it with this.Original images: https://image.non.io/neonRamenDesigns.webpGemini 3.7 build: https://html.non.io/neonRamenGemini3.7Opus 5 build for comparison: https://html.non.io/neonRamenOpus 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. reply: Other thoughts: I really think Google has fallen behind here. Even as a high speed offering (this build took ~7min, which is pretty good!), it wont be able to claim dominance for long with cerebras announcing the Sol preview today: https://www.cerebras.ai/blog/accelerating-gpt-5-6-sol-ultraf... .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.

The "introductory pricing" for this 3.7 Flash model is really weird.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: Filters are meant to contain additional elements, not be empty: https://drafts.csswg.org/filter-effects/#FilterElement - so maybe Chrome and Firefox remove the element that references the broken filter but Safari doesn't? reply:...

It is also doing pretty well in threejseval. Frontier there for the price. Much better than 3.6.https://threejseval.com/ranking

Ever since the insane discount with GPT-5.6 Luna, not much excites me anymore. I mean just look at the benchmarks, even though Gemini 3.7 Flash performs well on the DeepSWE 1.1, Luna (Max) still performs way better. I personally have stuck to Luna (Xhigh) because its been more than enough and does not bloat up the context window too fast with reasoning tokens.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 reply: GPT-5.6 Luna is an insanely powerful model for its price. It's been great for coding workflows where I guide the LLM's hand step by step. It's also insane to see my weekly limit drop by than 2% after an hour of coding ever since the discount.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....

They need to release benchmarks against Luna/Terra. Luna is much cheaper which feels like it undercuts the need for Flash.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.] reply: flash-lite is more of their luna tier competitor but even still not quite there yet, but gemini's dominance on multimodal and image understanding i think really gets downplayed on this site when most people think the only think you can do with LLMs is write code

Actual announcement: https://blog.google/innovation-and-ai/models-and-research/ge...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. reply: Yes, 3.6 Flash is very fast. I used to get a fair amount of usage of the Gemini Flash models on the free tier. I signed up for their $4.99/month tier (includes 400GB of Google space which was also enticing) and it turns out I only get about 15 to 20 minutes of usage before I get a come-back-in-7-days message. Comically low usage limits on that plan.

I just subbed to Gemini a week ago and have been using antigravity and 3.6 flash. The speed is absolutely a differentiator compared to Claude.

https://artificialanalysis.ai/models/gemini-3-7-flashThe selling point for gemini continues to be speed and particularly end-to-end response time. reply: It's funny that they don't mention this at all in the marketing or tech specs when it's obviously the biggest selling point by far. Without this it would be completely irrelevant.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...

We run a platform that serves models from many of the frontier players to power conversations and workflows. Gemini flash-2.5 was a game changer for us when it came out. Cheap, fast, and reliable.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.

Gemini Flash is one of the best "good-enough" models. I use this type of model daily, for automation and quick development iteration loops.Unfortunately, it's often not strong enough for heavy refactoring and long running development loops.

The Gemini Flash models makes perfect sense to me coming from a company like Google. Google AI Mode for search is a product I really find useful. It makes sense that Google focused on smaller, faster yet smart enough models that wouldn't break your bank on inference. It plays well into their product ecosystem.Google AI Mode consistently gets me consistently good results and good speeds. It really changes what "googling" is for me.

The multimodal abilities are great, but if you deal with text only, what is the benefit of using this over DS V4 Flash/Pro? 13-26x cheaper with comparable intelligence, and available across many different inference providers.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: artificialanalysisThe only benefit I can see is the speed, that looks to be outstanding, probably thanks to their TPUs.

> What's new in Gemini 3.7 Flash [0]> 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

Was excited to try it, since I've been use 3.6 Flash in the last few days to make simply experiments/prototypes. My loop is writing a prompt, maybe adding a screenshot of the closest to what I want I have so far, then based on the result I modify/extend the prompt, maybe use another screenshot.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/fDOkBDmBoth 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 don't get it, Google could heavily subsidy their Gemini models to make it more attractive, but they prefer to not do it. I don't know one soul who is using Gemini models to code. Even OpenAI who doesn't have money or capacity is offering their Luna model at $1.2 per 1M/out.

Feels like Gemini Pro will arrive directly as Gemini 4 Pro

This is genuinely a competitive model, considering it beats Claude Sonnet 5 on almost all benchmarks and is more than half its price. Seems like Google is back in the game, though not leading the frontier anymore.

And I'm over here on SiliconFlow using Stepfun AI Step-3.5-Flash at $0.10/M input and $0.30/M output tokens (262K context window) for complex market analysis work in rust utilizing vectorized instruction sets. It provides me with amazing results.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)

> 3.7 Flash is available through the end of the year at an introductory price of $0.75/1M input tokens and $3.75/1M output tokens.> Introductory pricing expires on December 31, 2026. Starting January 1, 2027, $1.50/1M input tokens and $7.50/1M output tokens will apply.

Have you tried the new DeepSeek Pro v4, Qwen 3.8, Gemini 3.7 Flash, and Grok 4.6? Do they make sense for any use cases?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

They compare it to 5.6 Terra, however https://cognition.com/frontiercode puts Terra at about 1/2 the priceAlso have to compare to the recent Grok 4.6 release, which appears to straight up be better AND cheaperHard to understand why anyone would choose 3.7 Flash under these conditions.. is Deepmind still a frontier lab?

I was going to cancel my gemini membership today ..... still going ahead. In my experience, gemini 3.1 pro, 3.5, 3.6 flash constantly lie too much about completing their tasks whereas sol (even though equally dumb) never claims something has been done when it hasn't been.

Maybe this is just my experience, but have people had trouble with 3.6 Flash just... getting things it has seen in its context correct? I don't know if it's been insanely benchmaxxed or what, but it'll pull information from websites and immediately get it wrong the token after. Or for example (this is something that happened like yesterday) I asked it to compare the uses of A and B in a language I was learning, and the way I typed it was "Please compare how these two are compared differently: A VS B", and then... it proceeded to compare "VS" and "B". I'm not kidding.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.

On a related note, I see all these quantitative benchmarks and the models getting really good at them over time. One thing I've been wondering: if the GPT series of models performs so well quantitatively, why do I still kind of hate using them relative to Claude? There’s a missing “vibes” or “taste” benchmark I think.

Does Google believe people want fast models because they have some sort of evidence of that preference? Or are they no longer capable of delivering a Pro model?

It's on Google AI Studio, which I use for free when I'm not on computers I control.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.

Has anyone noticed that antigravity has been working really well for the last few weeks. Now with this model it should be working much better. Hope the Google AI Pro Subscription can be used to do some real agentic coding now.

3.7 Flash gets 56 on AA up from 52 for 3.6 Flash. But it seems like this is at the cost of more output tokens per task: 3.6 Flash is 26k, 3.7 Flash is 37k. Due to 3.7 Flash's 2x slashed pricing it's still cheaper per task.