Gemini 3.7 Flash arrives with a 1M token context and thinking modes
Google has released Gemini 3.7 Flash, the latest in its Gemini 3 series of natively multimodal reasoning models. It supports text, image, video, audio, and PDF inputs, with a 1,048,576-token input limit and a 65,536-token output limit. The model offers low, medium, and high thinking effort levels, but not minimal, and includes capabilities like code execution, computer use (preview), file search, function calling, and grounding with Google Search and Maps. It is available via the Batch API, Flex inference, and Priority inference, and a stable version is already out.
Note: `minimal` is not supported and returns an error.
- jjcm
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.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.
- mchusma
This is a solid release (at the intro pricing, the other pricing is dumb). I do think its a missed opportunity to really blow things out of the water and have this be another 1/2 off, but clearly they don't have the inference efficiency for it. Speed is good, the knowledge in google's models is solid for those usecases, price is reasonable (after 3.5/3.6 major missteps).
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.
- simonw
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:
<filter id="shadow" x="-10%" y="-10%" width="130%" height="130%"></filter>
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?
- Alifatisk
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.
> 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.
- wxw
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.]
- parasti
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.
- twelvechairs
https://artificialanalysis.ai/models/gemini-3-7-flash
The selling point for gemini continues to be speed and particularly end-to-end response time.
- qudat
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.
- nicolamanzini
It is also doing pretty well in threejseval. Frontier there for the price. Much better than 3.6.
- fmind-dev
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.