131–140 of 692 posts
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...
3.5 Flash was more expensive than 3.1 Pro to run the Artifical Analysis test suite. $1551 for 3.5 Flash [0] vs $892 for 3.1 Pro [1]. That's 74% more cost while ranking lower. It's 2.5x as fast but I don't think the bang for the buck is there anymore like it was with 3.0 Flash. I'm a bit bummed out to be honest. I did not expect such a huge (3x) price increase from 3.0 Flash and I bet many people will not just blindly…
3.1 has 57M output tokens from Intelligence Index, 3.5 Flash has 73M, so not a lot more, and 3.5 is a bit cheaper, I don't get how 3.5 can be 74% more expensive.
Per 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.
My guess: it's the price at which they make more money than if they rent the TPUs to other companies.
The Gemini team has had trouble securing enough TPUs for their user's needs. They struggle with load and their rate limits are really bad. Maybe at a higher price, they have a better chance at getting more TPUs assigned?
Earlier quoted context omitted.
Maybe the margins are just very large for Google because they predict so much demand for 3.5?
This combined with locally runnable models getting pretty good recently (e.g. Qwen 3.6) tells me that it's time to seriously consider local dev setup again
The 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...
$9/1M output
I wonder if this is because it's a larger model or maybe just because they can? Although with the latest Deepseek it's really tough to compete pricing wise. Inference speed and integration (e.g. Antigravity) might be their only hope here
The Antigravity harness is really well done, so I do agree it's their strong suit. Can't say the same about gemini-cli (though it has a really nice interface)
Would still choose Deepseek for the price
Per 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
Per 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
Gen AI is unprofitable, especially at the insanely cheap rates they've been offering to get people in the door. So expect more increases in the future.
Is 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,…
The fix is easy enough though, a line in my global AGENTS.md instructing agents to search/ask for documentation before working on API integrations.