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Comment by twelvechairs | Hacker News Reader

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

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

It's funny that they don't mention this at all in the marketing or tech specs when it's obviously the biggest selling point by far. Without this it would be completely irrelevant.

Worth noting that OpenAI just announced that they got the full GPT 5.6 Sol model running on Cerebras at 750 tokens per second. No announcement of the pricing though...

> It's funny that they don't mention this at all in the marketing or tech specs when it's obviously the biggest selling point by far. Without this it would be completely irrelevant.

Good catch! You're right to point that out. My previous marketing copy missed that specific detail. Thank you for bringing it up!

I use this in a customer facing application and Gemini’s speed makes the experience feel much better.

The application isn’t so complicated that you need opus level reasoning or code writing, we need “good enough” data retrieval and processing with natural language queries and the ability to answer follow up questions.

For that Gemini works well for a decent price.

Cerebras is crazy to watch on GPT OSS or Gemma, I feel like we need a new VibeOS demo but with Cerebras, the OS would literally build itself in a few seconds.

https://youtu.be/7NfyZhV1dKM?t=52

Thw bottleneck become compiling and running the code every iteration so it wont be seconds

JIT compilers largely solve that. The real question in my mind is whether fast LLMs bias stuff towards languages that either don't have a compile/typecheck phase at all or have one that's very fast (e.g. Java).

It's like the Intel Optane of AI

I've blown away by flash 3.6's speed while Opus chugs along for _hours_ on similar tasks. I've gotten into a opus designed -> gemini implemented -> opus reviewed dev cycle recently.

I am actively using Gemini flash to "translate" what Opus says into human language. I let opus do the design (with my assistance) and implementation, but then the report that Opus writes gets translated by Gemini so that I don't have to waste time to understand it.

That’s like the army guy in movies from the 90s who shouts “IN ENGLISH, PLEASE!” after the scientist explains the conflict of the plot.

Matt Pocock has a great /wait-what skill for this: https://www.aihero.dev/skills-wait-what, which is an extremely short prompt of:

> Wait — I don't understand where you've got to here. Re-pitch that: give me a little bit of context, talk in ASD-STE100 Simplified Technical English, and use the ubiquitous language from CONTEXT.md.

> I've gotten into a opus designed -> gemini implemented -> opus reviewed dev cycle recently.

This is what I do too.

Last time I tried it, Flash introduced too many errors due to sloppiness. Is it more reliable at following instructions now?

It seems reliable to me now.

You can also customize Gemini Flash. It's a niche thing benefitting few, but you can tune gemini-3.7-flash in Google Vertex (now named "Agent Platform"?)

What's the typical response time for Gemini compared to other models?

On my benchmark where AIs generate ~20 different 3D models about 1/2 the time of Opus and 1/3 of the time of Kimi K3 and 2/3 of time of sonnet.

Sol high is almost the same speed if you take into account drastically lower token use. Look at the artificial analysis speed vs token use. Gemini is 7x faster but 5x more tokens. And that's with Sol high being a substantially better model.

Edit: and Sol medium actually has the same AA intelligence score as Gemini 3.7, and has >7x fewer tokens, actually making it faster

is presumes you are doing longer difficult agentic tasks, if youre doing a simple problem in 1 or 2 shots, not really multi turn then theres no comparison.

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