When OpenAI announces GPT-5, this market resolves to the largest context size measured in tokens that they announce support for.
GPT-3: 2048 tokens
GPT-3.5: 4096
GPT-4: 8k, 32k
GPT-5: ???
Anthropic's Claude announced a 100k variant, there are rumors of upcoming 1 million context size models, and surely OpenAI would want the most impressive-sounding model on release.
In the unexpected case they don't mention a specific context size or their architecture is changed so fixed context sizes no longer make sense, I'll wait until I have access and test its recall using very large documents.
If the largest context size isn't on this table, then this market resolves to a weighting of the surrounding entries. k is a multiplier of size 1024. GPT-4 would resolve "32k". Claude would resolve "log2(100k) = 16.61, so 2^16 = 64k would get weight 39% and 2^17 = 128k would get weight 61%".
1,000🏅 Top traders
| # | Name | Total profit |
|---|---|---|
| 1 | Ṁ1,695 | |
| 2 | Ṁ1,342 | |
| 3 | Ṁ911 | |
| 4 | Ṁ458 | |
| 5 | Ṁ456 |
People are also trading
If the largest context size isn't on this table, then this market resolves to a weighting of the surrounding entries. k is a multiplier of size 1024. GPT-4 would resolve "32k". Claude would resolve "log2(100k) = 16.61, so 2^16 = 64k would get weight 39% and 2^17 = 128k would get weight 61%".
not an official ruling but I'm pretty sure this is saying percentage (plus anecdotally other Mira MC numeric markets have almost always followed a similar system)
@Ziddletwix I agree with that and am making it official. If I screwed up let me know!
I get log2(400000) = 18.6096 rounds to 18.61; 61% on 512k and 39% on 256k.
Note some units inconsistency -- previous models have had power of 2 contexts, and Mira's description makes it clear that 512k = 2^19 exactly in this context. However, the GPT-5 context length is 400000, not 400*1024.
@EvanDaniel 400,000 value from here:
https://platform.openai.com/docs/models/gpt-5
Front page says 400k, but I think the 400000 value is correct. If it's actually 400*1024 then the 64/36 split is the correct resolution.
Google Gemini is 2 million tokens already, with tests up to 10 million. If OpenAI competes with Google, I might have needed more levels on my scale...
It resolves 100% 4096k(4 million) no matter how larger the final context is, since I can't add options.
@Gen Is it possible to admin add levels to this? 8192k and 16384k would be nice to have. Anything larger would be basically infinite. The context sizes grew faster than I expected.
There's now a standard test suite for the kind of recall test I was thinking of doing: How Long Can Open-Source LLMs Truly Promise on Context Length? | LMSYS Org
Leaving it as a comment here so I can remember to find it again in 2 years, if it's needed.
@ShadowyZephyr This is an intentional choice because it allows higher leverage if you have a strong opinion on a narrow numerical range.
@Mira I heard from other people that the math of the multi-choice markets is not good for compensating people who make correct bets early on.
@ShadowyZephyr I would disagree with them, but I usually don't debate people. You can bet on @firstuserhere 's binary markets if you like, since I stole his market idea for this.
@dmayhem93 It would resolve to the largest entry if it can pass the test at any size without errors, in a single API call.
If it has an increasing error rate like RNNs often do, I'll resolve to the highest size I get at least 50% successful recall.
It will be a simple "locate a matching entry" task, so even if its performance degrades for more complex reasoning it's likely to be able to pass as having a high context size.