111–120 of 285 posts
Okay this is a nitpick but why wouldn't you increment a part of the version number to signify that there is an improvement? These releases are confusing.
Non-AI Summary: Both models have improved intelligence on Artificial Analysis index with lower end-to-end response time. Also 24% to 50% improved output token efficiency (resulting in lower cost). Gemini 2.5 Flash-Lite improvements include better instruction following, reduced verbosity, stronger multimodal & translation capabilities. Gemini 2.5 Flash improvements include better agentic tool use and more token-effici…
Any idea what "output token efficiency" refers to? Gemini Flash is billed by number of input/output tokens, which I assume is fixed for the same output, so I'm struggling to understand how it could result in lower cost. Unless of course they have changed tokenization in the new version?
Non-AI Summary: Both models have improved intelligence on Artificial Analysis index with lower end-to-end response time. Also 24% to 50% improved output token efficiency (resulting in lower cost). Gemini 2.5 Flash-Lite improvements include better instruction following, reduced verbosity, stronger multimodal & translation capabilities. Gemini 2.5 Flash improvements include better agentic tool use and more token-effici…
Any idea what "output token efficiency" refers to? Gemini Flash is billed by number of input/output tokens, which I assume is fixed for the same output, so I'm struggling to understand how it could result in lower cost. Unless of course they have changed tokenization in the new version?
Which is a good thing in my book as the models now are way too verbose (and I suspect one of the reasons is the billing by tokens).
Google seems to be the main foundation model provider that's really focusing on the latency/TPS/cost dimensions. Anthropic/OpenAI are really making strides in model intelligence, but underneath some critical threshold of performance, the really long thinking times make workflows feel a lot worse in collaboration-style tools, vs a much snappier but slightly less intelligent model. It's a delicate balance, because thes…
Can't agree with that. Gemini doesn't lead just on price/performance - ironically it's the best "normie" model most of the time, despite it's lack of popularity with them until very recent. It's bad at agentic stuff, especially coding. Incomparably so compared to Claude and now GPT-5. But if it's just about asking it random stuff, and especially going on for very long in the same conversation - which non-tech users h…
Seems llm progress really is plateauing. I guess that was to be expected.
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They kinda have that already, no? https://openrouter.ai/apps?url=https%3A%2F%2Faider.chat%2F
Aggregating by tokens causes the problem simonw mentions in that one poweruser can skew the chart too much.
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chatgpt also has lots of reliability issues
If anyone from OpenAI is reading this, I have two complaints: 1. Using the "Projects" thing (Folder organization) makes my browser tab (on Firefox) become unusably slow after a while. I'm basically forced to use the default chats organization, even though I would like to organize my chats in folders. 2. After editing a message that you already sent,you get to select between the different branches of the chat (1/2, an…
On mobile (android) opening the keyboard scrolls the chat to the bottom! I sometimes want to type referring something from the middle of the LLMs last answer.
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Can't agree with that. Gemini doesn't lead just on price/performance - ironically it's the best "normie" model most of the time, despite it's lack of popularity with them until very recent. It's bad at agentic stuff, especially coding. Incomparably so compared to Claude and now GPT-5. But if it's just about asking it random stuff, and especially going on for very long in the same conversation - which non-tech users h…
My pet theory without any strong foundation is because OpenAI and Anthropic have trained their models really hard to fit the sycophantic mold of: =============================== Got it — *compliment on the info you've shared*, *informal summary of task*. *Another compliment*, but *downside of question*. ---------- (relevant emoji) Bla bla bla 1. Aspect 1 2. Aspect 2 ---------- *Actual answer* ----------- (checkmark e…
I added support to these models to my llm-gemini plugin, so you can run them like this (using uvx so no need to install anything first): export LLM_GEMINI_KEY='...' uvx --isolated --with llm-gemini llm -m gemini-flash-lite-latest 'An epic poem about frogs at war with ducks' Release notes: https://github.com/simonw/llm-gemini/releases/tag/0.26 Pelicans: https://github.com/simonw/llm-gemini/issues/104#issuecomment...
Who wins in the end? the frogs? the ducks? or the pelicans?
Earlier quoted context omitted.
Can't agree with that. Gemini doesn't lead just on price/performance - ironically it's the best "normie" model most of the time, despite it's lack of popularity with them until very recent. It's bad at agentic stuff, especially coding. Incomparably so compared to Claude and now GPT-5. But if it's just about asking it random stuff, and especially going on for very long in the same conversation - which non-tech users h…
My pet theory without any strong foundation is because OpenAI and Anthropic have trained their models really hard to fit the sycophantic mold of: =============================== Got it — *compliment on the info you've shared*, *informal summary of task*. *Another compliment*, but *downside of question*. ---------- (relevant emoji) Bla bla bla 1. Aspect 1 2. Aspect 2 ---------- *Actual answer* ----------- (checkmark e…