151–160 of 692 posts
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
We need another "Deepseek moment" or else it will become impossible for the regular dude to use AI. It will become something that only big companies can afford.
Earlier quoted context omitted.
Google’s pro models are almost certainly bigger than Openai’s lol
Why would that be? I am curious why do you think that.
For intelligence/size only OpenAI and Anthropic are the frontier. Google has more compute so it can compensate for that with size of the models...
Earlier quoted context omitted.
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,…
I can reliably produce hallucinations with this genre of prompt: "write a script that does with ." Even the frontier models will hallucinate the perfect API endpoint that does exactly what I want, regardless of if it exists. 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.
```
Build a Nango sync that stores Figma projects.
Integration ID: figma
Connection ID for dry run: my-figma-connection
Frequency: every hour
Metadata: team_id
Records: Project with id, name, last_modified
API reference: https://www.figma.com/developers/api#projects-endpoints
```
Note: You do need a Nango account and the Nango Skill installed before it could work.
Knowledge cutoff: January 2025 Latest update: May 2026 I have a very bad feeling about this lag.
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...
Engineers at google have publically stated that the models are too big and are far from their potencial. Glad they're being proven right with every release. They continue to focus on smaller models while openai and anthropic are increasing compute requirements for their SOTA models.
Nobody really knows the answer to which one is more optimal
* Large model trained on a large amount of data across multiple domains, that doesn't need any extra content to answer questions.
* Smaller model that is smart enough to go fetch extra relevant content, and then operate on essentially "reformatting" the context into an answer.
The Artificial Analysis benchmark results are pretty underwhelming. Roughly the same "intelligence" as MiMo-V2.5-Pro for over 3x the cost. We'll have to see how that translates to actual usage but it's not a great sign.
That really depends on whether they have similar parameter counts, doesn't it? Unless you know that, the comparison is just strange
The Artificial Analysis benchmark results are pretty underwhelming. Roughly the same "intelligence" as MiMo-V2.5-Pro for over 3x the cost. We'll have to see how that translates to actual usage but it's not a great sign.
That really depends on whether they have similar parameter counts, doesn't it? Unless you know that, the comparison is just strange