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.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.

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?

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

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.]

Still fairly poor limits in anti gravity despite the price cut, which seems to be API only.