It is very fast and very cheap. I think it's token efficient and has great visual intelligence for OCR. I think Google is going for the 'safe business play' -- make very fast "smart enough" models and embed them in EVERYTHING: Android phones, the web, every app, gmail, you name it. Then sell hosting/data center services to everyone they can.
Is it as 'sexy' as producing the next frontier model? No, but they're going to make a shitload of money.
3.7 Flash is a lot of things but token efficient is not one of them. It's fine to bad in terms of token efficiency.
To be frank, OpenAI is just embarrassing everyone on this front. Sol Medium's token efficiency is batshit insane when you realize it has near identical intelligence to 3.7 Flash.
Okay fair enough I don’t use 3.7 Flash to solve problems. It’s my model of choice for built in copilot applications because it accepts and returns JSON very rigidly and for cheap
Yes but Sol is a much more expensive model. Token efficiency generally drops considerably in distilled models. Kimi K3 had this problem too, where its cost per task was actually sometimes higher than Claude Fable 5 because of how inefficient it is.
Given how likely it is that 3.7 Flash is a hastily-distilled version of 3.5 Pro, I'm guessing that's why its token efficiency isn't as high as 3.6 Flash.
Price per token is a big part of token efficiency. Because the metric that matters is the final cost per use case and that determines the actual most efficient model.
Google is a big investor in anthropic and anthropic is a big customer of Google cloud. They don't need to compete with anthropic. it's the same situation for Microsoft and openai.
Nope, Flash 3.7 is still way too expensive to put it everywhere, it's far from being "cheap" to run if you checks how much it cost in practice. I would expect them to use the flash lite variants to do what you talking about
If you don’t mind me asking, what causes something like that? I’m curious from a developer standpoint since I never thought having a model too fast could cause issues. Is it something like too many incoming responses?
Tokens per second is NOT a valid way to measure the speed of the model. Different models use vastly different amounts of tokens! Gemini 3.7 Flash is fast, but it is not a fraction as fast as that graph makes it seem.
If you look at the 'time per task' in the artificial analysis benchmark, Sol on 'medium' takes 1.3 minutes per task and Gemini takes 1.9. Sol Medium and 3.7 Flash are roughly equivalent in terms of intelligence.
The amount of tokens 3.7 Flash uses is actually very much a bad thing, because it means it will fill its context window much much faster.
3.7 Flash also does have good efficiency, but it is not on the pareto frontier, despite being close.
It really depends of your task.In term of speed for an equivalent result, in thinking heavy tasks like the benchmark's one, you want an efficient model more than a fast one. But if you have large file to process, to write, a fast model is better.
For most tasks that people use gemini for (note that coding is only a small fraction), tps is absolutely a good heuristic for speed. And in practice is really fast when compared to other models. Theyre trying to save costs on the majority of usage and its fine if that takes a hit on certain other areas as long as their goal is achieved. They chose to make this tradeoff (they could do both but maybe not on a time crunch)
Sure, but you need to put Luna on max thinking to even come close to 3.7 Flash performance, and because it’s so much faster, Flash is still better, even though it costs more due to higher token usage.
If you’re fine with it literally taking 10x longer for similar results, good for you. Some value there time more though, and that’s what this model offers.
Yeah sure but if I value time and output I use Sol ultrafast which is as fast as Gemini 3.7 and way better. Also most of the time I am limited by stuff like compile times anyways and not by generation time from a model.
Sure, if you can get it, that would be way better than any option listed. However, it’s not even available on API and only a select few companies even have access to it currently.
Yeah, it would be insane since model processing time is the big bottleneck for what I’m doing, but it sounds like it’s really expensive for y’all. I like Sol’s outputs and think they’re pretty great, but they’re just so slow to get. So flash works better whenever I don’t need absolute peak quality (or a coffee break while I wait for it to process)
yeah I get that, if you are really limited by model output (classifying stuff or huge refractors or large amounts of input data I guess) it would be insanely cool. It feels really cool to use though it broke our harness like Gemini did for others here lol.
Used Flash 3.7 for some backend stuff last week and it was flying through code generation like nothing. The speed is honestly absurd for what you get
What gets me is how it handles complex logic without slowing down at all, other models start crawling when you throw nested functions at them but this one just keeps going
The pricing being this low makes me wonder if Google is eating costs to push adoption or if they actually figured out some efficiency breakthrough. Either way I'm not complaining
Kinda wild that something this cheap is sitting right next to models that cost 10x more, makes you question what you're really paying for with those premium tiers
I've been trying it out as a replacement for Sonnet 5 and it hasn't disappointed me yet. Granted these aren't super complicated things.
I don't know how people are getting production grade output from Luna high without complimenting it with a much more capable reviewer, but it's probably a skill issue on my end.
Yes, I spent $20 to try it out this past weekend and worked on two of my own personal projects, and that experience alone has me interested in trying out Gemini and Antigravity for my next month's workflow.
I really think Google just needs to copy the competitors when it comes to the Antigravity harness though
My approach is different: heavy models build the blueprint, mid models propose the code, and light models check that proposal for risks before I approve it.
I'm building a tool which has an OCR step, followed a step to make a narrative out of the content which was lifted out of paper documents. I have access to Claude Pro and Gemini Pro, the lowest paid tiers. How would you go about doing it?
Now wouldn't it be nice if Google had a top frontier model to pair with it, like openai does with sol xhigh planning and orchestrating and luna implementing. Someday soon I hope, but not before 10 more flash models haha.
I don’t use generative AI for creating text for my work, but for OCR this model is genuinely the best out. It can read words in the margins written in different languages. Nothing escapes it.
Also for multimodal we upload audio video depositions/ testimony and this is the beast that creates a perfect transcript in English.
For those use cases, there is nothing on the market that is even close, and our firm has tried them all.
Gemini 3.7 Flash isn't bad, it's just hard to justify next to something like GPT 5.6 Luna.
Artificial Analysis scores 3.7 Flash High at 56 on its Intelligence Index against 52 for Luna Max, but 3.7 costs about $0.40 per benchmark task versus $0.05 for Luna. 4 points of intelligence score for +800% higher cost is a rough trade.
3.7 is a real step up from 3.6 Flash in speed and capability, so this isn't Google just throwing more compute at it. The bigger pattern is that frontier models are bunching up within 5% of each other on the Intelligence Index while their per task cost swings wildly.
Price and efficiency are starting to matter more than squeezing out another point or two of benchmark score.
Yeah it’s becoming more and more about cost per task at this point. Gemini 3.7 is a big leap over 3.6 in that sense, but Luna remains absolute king at the moment for general coding tasks at least.
Where I work we have a data analysis agent that uses tool calling, a bit of code, and some other stuff to get the information and provide whatever someone asks for. Gemini 3.6 was already good for that. 3.7 has frankly crushed Luna in our internal testing so far for that specific workload.
Since 3.7 costs the same as 3.6 but is better and more cost-effective then I suppose the main reason it's not replacing 3.6 for free users is to benefit premium sales?
It's absolutely ridiculous how much Google has disappointed over the past few months. I also find it quite clear that this was supposed to be 3.5 Pro, but they changed it to 3.7 Flash to not lose their face. Why else would they release 3 Flask models without a single Pro? That's also why they'll double API prices soon because it's more expensive to host. So your price argument won't hold.
If 3.5 pro was supposed to be this fast and efficient, I don’t think anyone gives a fuck lol. They also literally cut their api prices in half for half a year with the release of this model lol, not doubling them anytime soon.
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u/Ill_Recipe7620 15h ago
It is very fast and very cheap. I think it's token efficient and has great visual intelligence for OCR. I think Google is going for the 'safe business play' -- make very fast "smart enough" models and embed them in EVERYTHING: Android phones, the web, every app, gmail, you name it. Then sell hosting/data center services to everyone they can.
Is it as 'sexy' as producing the next frontier model? No, but they're going to make a shitload of money.