Comment by coffeefirst
7 hours ago
> The cost for the same quality of output is going to drop at least 10x over the next 18-24 months.
How do you know that?
In 2026 the prices have been spiking. It now costs orders of magnitude more than it did in November.
Price of the current frontier may vary, but price for a given level of capability tends to drop pretty fast.
April of last year you'd get 1431 ELO[0] from o3-2025-04-16 for $8.00 per million output tokens. April of this year you can get 1436 ELO from deepseek-v4-flash for $0.2 per million output tokens.
[0]: https://huggingface.co/spaces/lmarena-ai/arena-leaderboard
Sure, but i don't think it's reasonable to hold given level of capability constant in a landscape where a give consumer of AI also has competitive pressures.
I can't use last year's SOTA model when my competitors can use the current SOTA model.
This is also baked in the eye watering valuations of model companies.
> I can't use last year's SOTA model when my competitors can use the current SOTA model.
You can use open source models of equivalent or better capabilities for ~90% less cost...
If you kick and scream hard enough, you can always find a data point to make sure you're correct.
No one is saying that the Opus model last year costs 90% less now than it does this year.
That's not how it works.
There are better, more efficient models with equivalent capabilities that are 90% cheaper (see DeepSeek v4 Pro).
> How do you know that?
Historic trends, every 18 months, performance for the same level of quality has gone down 90%.
See: https://www.reddit.com/r/LocalLLaMA/comments/1gpr2p4/llms_co...
And Chart 13 here: https://www.rdworldonline.com/ais-great-compression-20-chart...
And here: https://epoch.ai/data-insights/llm-inference-price-trends
The technology already exists now on the algorithmic front for the next 10x drop between everyone adopting DeepSeek's MLA, MoE (mostly already done), Medusa (a better version of Google's speculative decoding), Kimi's Attn Residuals, and Mimo's Sliding Window Attn, and (possibly) Microsoft's 1.58b (this may be a nothing burger).
Historically, algorithmic gains are only ~30% of the pie, but there's enough out there to get to 10x, with just what's available already. The other ~70% of the pie is better training data (often synthetic) and distilling frontier knowledge. There's no sign we are tapped out on that front.
> In 2026 the prices have been spiking.
That's not for the SAME level of output...