Gemini 3 Flash: Frontier intelligence built for speed

Post by jrk » Thu, Dec 18, 2025, 1:47 AM UTC

danpalmer wrote: post #310

mips_avatar wrote: post #288

Earlier quoted context omitted.

OpenAI made a huge mistake neglecting fast inferencing models. Their strategy was gpt 5 for everything, which hasn't worked out at all. I'm really not sure what model OpenAI wants me to use for my applications that require lower latency. If I follow their advice in their API docs about which models I should use for faster responses I get told either use GPT 5 low thinking, or replace gpt 5 with gpt 4.1, or switch to…

Hardware is a factor here. GPUs are necessarily higher latency than TPUs for equivalent compute on equivalent data. There are lots of other factors here, but latency specifically favours TPUs. The only non-TPU fast models I'm aware of are things running on Cerebras can be much faster because of their CPUs, and Grok has a super fast mode, but they have a cheat code of ignoring guardrails and making up their own world…

Post by kartayyar » Thu, Dec 18, 2025, 2:04 AM UTC

samyok wrote: post #4

Don’t let the “flash” name fool you, this is an amazing model. I have been playing with it for the past few weeks, it’s genuinely my new favorite; it’s so fast and it has such a vast world knowledge that it’s more performant than Claude Opus 4.5 or GPT 5.2 extra high, for a fraction (basically order of magnitude less!!) of the inference time and price

Post by dimitri-vs » Thu, Dec 18, 2025, 2:06 AM UTC

gaigalas wrote: post #121

catigula wrote: post #100

Earlier quoted context omitted.

Correct. Opus 4.5 'solved' software engineering. What more do I need? Businesses need uncapped intelligence, and that is a very high bar. Individuals often don't.

If Opus is one-size-fits-all, then why Claude keeps the other series? (rethorical). Opus and Sonnet are slower than Haiku. For lots of less sophisticated tasks, you benefit from the speed. All vendors do this. You need smaller models that you can rapid-fire for lots of other reasons than vibe coding. Personally, I actually use more smaller models than the sophisticated ones. Lots of small automations.

Post by floundy » Thu, Dec 18, 2025, 2:09 AM UTC

alex1138 wrote: post #160

simonsarris wrote: post #10

Even before this release the tools (for me: Claude Code and Gemini for other stuff) reached a "good enough" plateau that means any other company is going to have a hard time making me (I think soon most users) want to switch. Unless a new release from a different company has a real paradigm shift, they're simply sufficient. This was not true in 2023/2024 IMO. With this release the "good enough" and "cheap enough" int…

I just can't stop thinking though about the vulnerability of training data You say good enough. Great, but what if I as a malicious person were to just make a bunch of internet pages containing things that are blatantly wrong, to trick LLMs?

Post by grog454 » Thu, Dec 18, 2025, 2:10 AM UTC

nl wrote: post #407

grog454 wrote: post #376

Earlier quoted context omitted.

This thought process is pretty baffling to me, and this is at least the second time I've encountered it on HN. What's the value of a secret benchmark to anyone but the secret holder? Does your niche benchmark even influence which model you use for unrelated queries? If LLM authors care enough about your niche (they don't) and fake the response somehow, you will learn on the very next query that something is amiss. No…

I have a bunch of private benchmarks I run against new models I'm evaluating. The reason I don't disclose isn't generally that I think an individual person is going to read my post and update the model to include it. Instead it is because if I write "I ask the question X and expect Y" then that data ends up in the train corpus of new LLMs. However, one set of my benchmarks is a more generalized type of test (think a…