GPT-4o mini: advancing cost-efficient intelligence
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Re: GPT-4o mini: advancing cost-efficient intelligence
#2[deleted]
Re: GPT-4o mini: advancing cost-efficient intelligence
#3GPT-4o mini is $0.15/1M input tokens, $0.60/1M output tokens. In comparison, Claude Haiku is $0.25/1M input tokens, $1.25/1M output tokens.
There's no way this price-race-to-the-bottom is sustainable.
Re: GPT-4o mini: advancing cost-efficient intelligence
#4GPT-4o mini is $0.15/1M input tokens, $0.60/1M output tokens. In comparison, Claude Haiku is $0.25/1M input tokens, $1.25/1M output tokens. There's no way this price-race-to-the-bottom is sustainable.
I think it's heavily quantized, so it doesn't cost them (too much). But I think it's still at cost...
Re: GPT-4o mini: advancing cost-efficient intelligence
#5GPT-4o mini is $0.15/1M input tokens, $0.60/1M output tokens. In comparison, Claude Haiku is $0.25/1M input tokens, $1.25/1M output tokens. There's no way this price-race-to-the-bottom is sustainable.
> There's no way this price-race-to-the-bottom is sustainable.
Why not?
Re: GPT-4o mini: advancing cost-efficient intelligence
#6It looks like the vision costs the same for GPT-4o vs mini.
Both start with 150x150px and if you click the (i) it says mini uses way more base tokens and way more tile tokens, it still costs the same...
Re: GPT-4o mini: advancing cost-efficient intelligence
#7GPT-4o mini is $0.15/1M input tokens, $0.60/1M output tokens. In comparison, Claude Haiku is $0.25/1M input tokens, $1.25/1M output tokens. There's no way this price-race-to-the-bottom is sustainable.
I think the place for generating larger total revenue/margins would be in the highest end models. Budget models almost "come with" the effort put towards making those high end models so it's alright they are a race to the bottom (so long as someone actually realizes return on higher end models, which is a problem in itself at this moment).
Re: GPT-4o mini: advancing cost-efficient intelligence
#8This is great - Though I am confused on two things:
1. How is it possible that GPT-4o mini outperforms 3.5 turbo but 3.5 turbo is more expensive? Like why would someone use a worse model and pay more?
2. Why is the GPT4o vision and GPT4o-mini vision cost the same?
Re: GPT-4o mini: advancing cost-efficient intelligence
#9This is great - Though I am confused on two things: 1. How is it possible that GPT-4o mini outperforms 3.5 turbo but 3.5 turbo is more expensive? Like why would someone use a worse model and pay more? 2. Why is the GPT4o vision and GPT4o-mini vision cost the same?
Predictability with a particular set of prompts and processes. Over time, you'd migrate to the lower cost, higher performing model, as long as it can be at least as consistent as the higher cost model. People have built really weirdly intricate chains of dependency on things that particular models are good at, and sometimes 3.5 turbo can accomplish a task dependably where other models might refuse, or have too wide a variance to be relied on.
Over time, reliability and predictability will be much less an issue.
Re: GPT-4o mini: advancing cost-efficient intelligence
#10It looks like the vision costs the same for GPT-4o vs mini. Both start with 150x150px and if you click the (i) it says mini uses way more base tokens and way more tile tokens, it still costs the same...
Good catch: the calculators here are bizarre. For GPT-4o, a 512x512 image uses 170 tile tokens. For GPT-4o mini, a 512x512 image uses 5,667 tile tokens. How does that even work in the context of a ViT? The patches and its image encoder should be the same size/output.
Since the base token counts increase proportionally (which makes even less sense) I have a hunch there's a JavaScript bug instead.