> * 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.

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.]

How does it compare to Opus 5.0 and Fable 5 for coding? E.g. in Cursor or OpenCode?

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.

For those looking for the full benchmark figures and technical overview, Google's primary announcement post is here: https://blog.google/innovation-and-ai/models-and-research/ge...

> 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.

https://artificialanalysis.ai/models/gemini-3-7-flash

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

> 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

At the discounted rates, upgrading from 3 Flash to 3.7 Flash is finally reasonable.

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.

Reposting my comment from the other thread https://news.ycombinator.com/item?id=49288847

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?

Well, you do get 1 million tokens and the ability to reason over video natively and many of us are forced to pay for 20usd plan anyway due to google drive 5TB, not to mention notebooklm, so it’s not a nothing burguer, it’s just an almost nothing burguer

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?

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.

[dupe] https://news.ycombinator.com/item?id=49288847 (35 points, 8 comments)

When are we getting another pro model from Gemini? Or are they simply focusing on the niche of fast but moderately capable models?

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.

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

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.

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.

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?

Is that he model that supposed to be Pro, but then they changed their mind?

strong improvement over 3.6 flash

but luna is hard to beat @ capability / cost

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?

Another failed 3.5 pro run branded as 3.7 flash. It's getting sad.