3x price increase for a similar model almost. And they said AI would be cheaper and ubiquitous.
Ubiquitous like the crack epidemic.
Gemini 3.5 Flash
221–230 of 692 posts
Re: Gemini 3.5 Flash
#222Per million input/output tokens: Gemini 2.5 flash: $0.30/$2.50 Gemini 3.0 flash preview: $0.50/$3.00 Gemini 3.5 flash: $1.50/$9.00 Interesting pricing direction. I don't think we have ever seen a 3x price increase for in the immediate next same-sized model (and lol @ 3 only ever getting a preview). 3.5 flash costs similar to Gemini 2.5 pro which was $1.25/$10
If Google is actually getting cheaper inference than everyone else with their TPUs, this smells like trouble to me. Maybe serving LLMs at a profit is proving difficult. Or maybe they think because their benchmarks are good they can ramp up the prices. Seems like they don’t have the market share to justify a move like that yet to me.
Its probably that in 1 or 2 years local (free) models will completely take the place of cheap models so cheap models need to move up the quality chain.
You have free local models for most tasks, $20 subscriptions for near-frontier intelligence, and API per token costs for frontier intelligence.
Flash seems to be targeting the near-frontier category.
Re: Gemini 3.5 Flash
#223Am I really so old that when someone says "Flash" my immediate response is... "consider HTML5 instead" ??
Lol. Young uns!
Flash, ah, ah, saviour of the universe. Flash, ah, ah, he'll save every one of us!
Every time I have heard the word flash for goodness knows how many years.
Re: Gemini 3.5 Flash
#224Is there a good benchmark tracking hallucinations? The models are all incredibly good now, even the open ones, and my hope is that the rate of hallucinations is something that's falling off in concert with larger and larger context lengths.
It really depends what you are asking it. If the answer is in the training data, then the odds of it lying to you are much lower than if you are asking it for something it has never seen before.
Re: Gemini 3.5 Flash
#225Per million input/output tokens: Gemini 2.5 flash: $0.30/$2.50 Gemini 3.0 flash preview: $0.50/$3.00 Gemini 3.5 flash: $1.50/$9.00 Interesting pricing direction. I don't think we have ever seen a 3x price increase for in the immediate next same-sized model (and lol @ 3 only ever getting a preview). 3.5 flash costs similar to Gemini 2.5 pro which was $1.25/$10
just subscribe to the plan, cheaper
Re: Gemini 3.5 Flash
#226The pelican is a lot : https://github.com/simonw/llm-gemini/issues/133#issuecomment... Not a great bicycle though, it forgot the bar between the pedals and the back wheel and weirdly tangled the other bars. Expensive too - that pelican cost 13 cents: https://www.llm-prices.com/#it=11&ot=14403&sel=gemini-3.5-fl...
That pelican looks like it's in Miami for a crypto conference.
Pelican in a white Testarossa.
Re: Gemini 3.5 Flash
#227Is there a good benchmark tracking hallucinations? The models are all incredibly good now, even the open ones, and my hope is that the rate of hallucinations is something that's falling off in concert with larger and larger context lengths.
People complain about them incessantly, but I can almost never get people to actually post receipts. Every provider allows sharing chats, and anyone can share a prompt that reliably produces hallucinations. More often than not, people are using images in responses that go awry. Which is fair, the models are sold as multi-modal, but image analyses is still at gpt-4.0 text-analyses levels. Also knowledge cutoff issues,…
Are the knowledge cut off issues well known? I don't remember seeing them prominently displayed.
Also, prompts that reliably produce hallucinations is kind of a hard ask. It's inconsistent. One day the LLM I work with quotes verbatim from the PCIe spec and it's super helpful. The next day it gives me wrong information and when I ask it what section of the spec that information comes from it just makes up a section number
Re: Gemini 3.5 Flash
#228Has anyone switched from Claude 4.7 Opus or ChatGPT 5.5 to this?
How does it feel? Dumber? Worth it for the speed? I'd love someone's subjective take on it, after doing a long session of coding.
Reiner Pope gave a talk on Dwarkesh Patel about token economics. I guess faster is a lot more expensive, generally.
Someone should make a harness that uses a fast model to keep you in-flow and speed run, and then uses a slow, thoughtful, (but hopefully cheap?) model to async check the work of the faster model. Maybe even talk directly to the faster model?
Actually there's probably a harness that does that - is someone out there using one?
Re: Gemini 3.5 Flash
#229The benchmark that matters - can it actually program as well as Claude code. If not then I’m not using it. Cancelled my account 3 months ago, only Claude code level capability would bring me back.
I spent 10 minutes with it in their new "agy" CLI tool and immediately found it is nowhere close to GPT 5.5 high in codex. It was sloppy and made poor assumptions in its analysis. It would have produced a mess if I let it go ahead with its plan. And it was just like previous versions of Gemini with poor tool use (e.g. "I wrote a file with the plan..." but file was never written.)
For reference, this is a Rust codebase, deep "systems" stuff (database, compiler, virtual machine / language runtime)
They're still months behind OpenAI and Anthropic on coding.
Mind you I also find Claude Code careless and unreliable these days, too. (But it's good at tool use at least).
I do use Gemini for "lifestyle" AI usage (web research etc) tho.
Re: Gemini 3.5 Flash
#230The pelican is a lot : https://github.com/simonw/llm-gemini/issues/133#issuecomment... Not a great bicycle though, it forgot the bar between the pedals and the back wheel and weirdly tangled the other bars. Expensive too - that pelican cost 13 cents: https://www.llm-prices.com/#it=11&ot=14403&sel=gemini-3.5-fl...
This is a perfect illustration of something I noticed with llm progress. Ask them to improve an svg like this, and it never fixes the missing crossbar or disconnected limbs, it just adds more stuff. In this example they have obviously improved greatly, and it contains a ridiculous amount of detail, but they still to get the basic shape of the frame wrong. It's weird. And the pattern shows up everywhere, try it with a…
When you say "improve an svg like this", how are you imagining setting that workflow up? Are you just feeding them the SVG to iterate on; or are you giving them access to a browser to look at the rendering of the SVG?
I ask because:
Insofar as the original pelican test is zero-shot, it effectively serves as a way to test for the presence of a kind of "visual imagination" component within the layers of the model, that the model would internally "paint" an SVG [or PostScript, etc] encoding of an image onto, to then extract effective features from, analyze for fitness as a solution to a stated request, etc.
But if you're trying to do a multi-shot pelican, then just feeding back in the SVG produced in the previous attempt, really doesn't correspond to any interesting human capability. Humans can't take an SVG of a pelican and iteratively improve upon it just based on our imagined version of how that SVG renders, either! Rather, a human, given the pelican, would simply load the pelican SVG in a browser; look at the browser's rendering of the pelican; note the things wrong with that rendering; and then edit the SVG to hopefully fix those flaws (and repeat.)
I imagine current (mult-modal and/or computer-use) LLMs would actually be very good at such an "iterative rendered pelican" test.