What people seem to miss very hard is that they get interactive chat mode of all the models, including the best and newest (Gemini 2.5 Pro, 2.5 Flash, 2.5 Flash Lite and older) totally for free. I mean when working from chat at https://aistudio.google.com/ the entire 1M context window and all is totally free of charge. You really get a very good AI for nothing.
Funny you mention this, I literally just got done loading the context window of AI studio up for an hour doing some prototyping and then was frustrated when I couldn’t see where I was at from billing (knew it couldn’t be that much, but I still like to know).
I assumed because I’m on paid tiers it would still cost behind a certain usage amount, but I guess not.
IMO, a good contest between LLMs would be data compression. Each LLM is given the same pile of text, and then asked to create compact notes that fit into N pages of text. Then the original text is replaced with their notes and they need to answer a bunch of questions about the original text using the notes alone.
Summarization ? I'm pretty sure there are benchmarks for this because people used summarization to build search indexes (at least a few years ago when I was working on this they did and there were benchmarks)
Note that (in the first test, the only one where output length is reported), Gemini Pro returned more than 3x the amount of text, at less than 2x the amount of time. From my experience with Gemini, that time was probably mainly spent on thinking, length of which is not reported here. So looking at pure TPS of output, Gemini is faster, but without clear info on the thinking time/length, it's impossible to judge.
> Claude’s overall response was consistently around 500 words—Flash and Pro delivered 3,372 and 1,591 words by contrast.
It isnt clear from the article whether the time they quote is time-to-first-token or time to completion. If it is latter, then it makes sense why gemini* would take longer even with similar token throughput.
output tokens must be generated in order (autoregressive decoding), inputs don’t have that constraint, so prefill is parallel, with stronger kernels, KV-cache handling, and batching, Claude can outrun Gemini.
I built a tool that lets you prompt Gemini and Claude at the same time so you can compare their answers side by side. You should check it out : www.tantyai.com
i’m really curious how well they perform with a long chat history. i find that gemini often gets confused when the context is long enough and starts responding to prior prompts, using the cli or it’s gem chat window.
From my experience. Gemini is REALLY bad about context blending. It can't keep track of what I said and what it said in a conversation under 200K tokens. It blends concepts and statements up, then refers to some fabricated hybrid fact or comment.
Gemini has done this in ways that I haven't seen in the recent or current generation models from OpenAI or Anthropic.
It really surprised me that Gemini performs so well in multi-turn benchmarks, given that tendency.
I’ve not experimented with the recent models for this but older Gemini models were awful for this - they’d lie about what I’d said or what was in their system prompt even with short conversations.
https://i.imgur.com/pgfRrZY.png
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