111–120 of 525 posts
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.
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?
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?
I have read that "pro"/"opus"/etc models can actually be worse for everyday coding as they reason "too deeply" and turn over too many stones over-thinking the problem and potentially getting distracted. This feels absurd to me (my gut is "I want the SMARTEST model I can get!!"), but often I find that my experience of using a flash/sonnet model for every-day workhorse coding they are better. Its not the same thing, bu…
How does it compare to Opus 5.0 and Fable 5 for coding? E.g. in Cursor or OpenCode?
Google's Opus competitor is 3.1 Pro Preview which is essentially obsolete (competed with Opus 4.6). They do not have a Fable/Sol competitor.
> 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.
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.
I think its the same price..
Grok, Meta, Gemini and others all released updates to their models within around a month or two from their respective last release and made significant jumps in benchmarks all around the same time. Any guesses as to why that is? Is it just the release season and/or everyone is benchmaxxing?
its essentially the same model being trained continuously 24/7 with the company periodically publishing just a new checkpoint each new checkpoint can benefit from better reasoning training, RL on specific tasks and more synthetic data So why do they seem to release around the same time ? my guess is because they time major releases around quarterly earnings, investor meetings and other important business milestones.…
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?
All the leaks say their latest attempt at a Pro model was not competitive.
I wonder if this counts as evidence against that hypothesis? That multi-modal is struggling to keep up with SotA and the best they can offer is competent and fast?
I also think Google is still the best at fitting the most overall intelligences into their models, but for some reason it seems like the model architecture is just bad.
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 com…
gemini flash is probably the best model for visual tasks right now. they also make it really easy to ingest videos