121–130 of 285 posts
Earlier quoted context omitted.
Honestly naming conventions in the AI world have been appalling regardless of the company
Mistral vs mistral.rs, Llama and llama.cpp and ollama, groq and grok. It's all terrible.
I upgraded my llm-gemini plugin to handle this, and shared the results of my "Generate an SVG of a pelican riding a bicycle" benchmark here: https://simonwillison.net/2025/Feb/5/gemini-2/ The pricing is interesting: Gemini 2.0 Flash-Lite is 7.5c/million input tokens and 30c/million output tokens - half the price of OpenAI's GPT-4o mini (15c/60c). Gemini 2.0 Flash isn't much more: 10c/million for text/image input, 70c…
Is there a way to see/compare the shared results for all of the LLMs you've tested this prompt on in one place? The 2.0 pro result seems decent but I don't have a baseline if that's because it is or if the other 2 are just "extremely bad" or something.
I tried voice chat. It's very good, except for the politics We started talking about my plans for the day, and I said I was making chili. G asked if I have a recipe or if I needed one. I said, I started with Obama's recipe many years ago and have worked on it from there. G gave me a form response that it can't talk politics. Oh, I'm not talking politics, I'm talking chili. G then repeated form response and tried to c…
I find it horrifying and dystopian that the part where it "Can't talk politics" is just accepted and your complaint is that it interrupts your ability to talk chilli. "Go back to bed America." "You are free, to do as we tell you" https://youtu.be/TNPeYflsMdg?t=143
OP can go talk politics until he's blue in the face with someone willing to talk politics with them.
It’s funny, I’ve never actually used Gemini and, though this may be incorrect, I automatically assume it’s awful. I assume it’s awful because the AI summaries at the top of Google Search are so awful, and that’s made me never give Google AI a chance.
These names are unbelievably bad. Flash, Flash-Lite? How do these AI companies keep doing this? Sonnet 3.5 v2 o3-mini-high Gemini Flash-Lite It's like a competition to see who can make the goofiest naming conventions. Regarding model quality, we experiment with Google models constantly at Rev and they are consistently the worst of all the major players. They always benchmark well and consistently fail in real tasks.…
> available via the Gemini API in Google AI Studio and Vertex AI. > Gemini 2.0, 2.0 Pro and 2.0 Pro Experimental, Gemini 2.0 Flash, Gemini 2.0 Flash Lite 3 different ways of accessing the API, more than 5 different but extremely similarly named models. Benchmarks only comparing to their own models. Can't be more "Googley"!
You missed the first sentence of the release: >In December, we kicked off the agentic era by releasing an experimental version of Gemini 2.0 Flash I guess I wasn't building AI agents in February last year.
I upgraded my llm-gemini plugin to handle this, and shared the results of my "Generate an SVG of a pelican riding a bicycle" benchmark here: https://simonwillison.net/2025/Feb/5/gemini-2/ The pricing is interesting: Gemini 2.0 Flash-Lite is 7.5c/million input tokens and 30c/million output tokens - half the price of OpenAI's GPT-4o mini (15c/60c). Gemini 2.0 Flash isn't much more: 10c/million for text/image input, 70c…
What is the model I get at gemini.google.com (i.e. through my Workspace subscription)? It says "Gemini Advanced" but there are no other details. No model selection option. I find the lack of clarity very frustrating. If I want to try Google's "best" model, should I be purchasing something? AI Studio seems focused around building an LLM wrapper app, but I just want something to answer my questions. Edit: what I've lea…
This is funny how bad UI is on some of websites which are considered the best. Today I tried to find prices for Mistral models but I couldn’t. Their prices page leads to 404…
These names are unbelievably bad. Flash, Flash-Lite? How do these AI companies keep doing this? Sonnet 3.5 v2 o3-mini-high Gemini Flash-Lite It's like a competition to see who can make the goofiest naming conventions. Regarding model quality, we experiment with Google models constantly at Rev and they are consistently the worst of all the major players. They always benchmark well and consistently fail in real tasks.…