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>3.7 Flash is available through the end of the year at an introductory price 1 of $0.75/1M input tokens and $3.75/1M output tokens. This price combined with the enhanced model performance enables developers and customers to scale production-ready agents cost effectively.

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

Gemini 3.6 Flash was offered with the same introductory pricing (also until Jan 2027) when it was released less than 4 week ago.
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?

Gemini models are still good for knowledge as per omniscience benchmarks on artificial analysis
When are we getting another pro model from Gemini? Or are they simply focusing on the niche of fast but moderately capable models?
Another failed 3.5 pro run branded as 3.7 flash. It's getting sad.
Model card: https://deepmind.google/models/model-cards/gemini-3-7-flash/

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

So basically Google is 5 weeks behind with a Fast model that is as good (on the bench they picked it's mostly ahead btw) as the models that the two darlings of HN (OpenAI and Anthropic) released five weeks ago.

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.

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.
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: artificialanalysis

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

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.
> * For 3.6 and 3.7 Flash, introductory price expires on December 31, 2026. Starting January 1, 2027, $1.50/1M input tokens and $7.50/1M output tokens will apply.

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.

what a week - lets see it draw a weird animal doing a weird thing on a bicycle
> 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.

> 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

Is that he model that supposed to be Pro, but then they changed their mind?
I'm only interested in the state-of-the-art model by each provider.

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

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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.

Yup. I use it for a ton of mundane queries (stuff that I might have used Google search for in the past) and it's great. Nice and fast and correct more often than not, especially if you prompt it in a way that it invokes Google search (but filters out ads and SEO slop). It's even alright at programming tasks but if it stumbles then I'll escalate to Gemini Pro with extended thinking.
strong improvement over 3.6 flash

but luna is hard to beat @ capability / cost

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I'm really curious about this: the foundational paper behind today's LLMs came from Google, and some of the world's best scientists were at Google. So why are they falling so far behind in the AI race?
> why are they falling so far behind in the AI race?

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.

Offering a 'temporary introductory discount' until Dec 2026 on an LLM is hilarious. In this market, by Jan 2027 this model will be superseded by 5 different providers offering 10x the performance at half the post-discount price anyway.
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.]