What will the inference cost of the best publicly available LM be in 2030? | Manifold

What will the inference cost of the best publicly available LM be in 2030?

Basic

5

Ṁ391

2031

7%

Other

12%

.01-.1 docs

23%

.1-1 docs

26%

1-10 docs

12%

10-100 docs

13%

100-1K docs

2%

1K-10K docs

0.2%

10K-100K docs

4%

.001-.01 docs

Consider the best publicly available language model in 2030. For a single 2023 US dollar, how many 2K word documents can I each generate five words for?

I will pick a somewhat conservative estimate of the best inference cost I can achieve after working for three weeks with whatever funds I have at the time and without using publicly inaccessible resources.

Multimodal models that can operate on text count as LMs for the purpose of this question.

I will only accept answers that range over an OOM like so:
.1-1 docs, 1-10 docs, 10-100 docs

I may trade in this market.

This question is managed and resolved by Manifold.

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The new ChatGPT model (https://openai.com/blog/introducing-chatgpt-and-whisper-apis) is $.002/1K tokens, so

187 docs/$ = 1/[(2.005 x 1K words/doc)*(4/3 tok/word)*(0.002 $ / 1K tok)]

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