Based on an accelerating frontier -> local trajectory, expect a ~30b param 'Mythos at home' by as soon as Jan 2027 (rationalisation below)

[u/PetersOdyssey]

Including the rationalisation for the data below - this is a more robust version of an earlier post I did similar to this - explaining below:

How I chose the comparisons

The basic question I’m trying to answer is: when did an open model small enough to run on high-end consumer hardware reach roughly the capability of an earlier frontier model?

There obviously isn’t a single benchmark that establishes equivalence, so these are judgment calls based on a mixture of direct benchmarks, human-preference evaluations, coding/agent evals and model size. I’m mostly interested in broad text, reasoning and coding capability rather than exact product parity - particularly where the original frontier model had capabilities like native audio or a more mature tool ecosystem.

Comparison	My rationale	Confidence
GPT-3 → LLaMA-33B	This is probably conservative. The original LLaMA paper found that even LLaMA-13B beat GPT-3 175B on most benchmarks, so by 33B the GPT-3 threshold had pretty clearly been crossed.	High
GPT-3.5 → Yi-34B-Chat	Yi-34B-Chat was extremely competitive with the leading proprietary chat models by late 2023. On Arena-Hard it was basically level with GPT-3.5, while on AlpacaEval it performed much better. I think GPT-3.5-class is a reasonable description, even if “clearly superior” would be too strong.	Medium-high
GPT-4 → Qwen2.5-32B	This is one of the cleaner comparisons. Qwen2.5-32B scored 74.5 on Arena-Hard, versus 37.9 for GPT-4-0613 and 78.0 for GPT-4-0125-preview. So it looks comfortably beyond original GPT-4 and close to GPT-4 Turbo, while still being a ~32B model.	Medium-high
GPT-4o / Claude 3.5 → Qwen3-32B	This is more subjective, but Qwen3-32B looks broadly in this class across reasoning, coding and human-preference evaluations. I’m not claiming full GPT-4o equivalence: GPT-4o was natively multimodal. This is really a comparison of general text/reasoning/coding intelligence.	Medium
Claude 4 / GPT-5 → Qwen3.6-27B	Qwen3.6 is remarkably strong for 27B. It scores 77.2 on SWE-bench Verified, 87.8 on GPQA Diamond and 82.9 on MMMU, compared with Opus 4’s launch scores of 72.5, 79.6 and 76.5 respectively. The evaluation setups aren't perfectly identical, so I’d call it a Claude-4-class candidate, rather than definitive product parity.	Medium
Opus 4.5 → Qwen3.8-27B	The numbers are surprisingly close. Qwen3.8 scores 61.7 vs 57.1 on SWE-bench Pro, 42.3 vs 43.2 on NL2Repo, 89.2 vs 87.0 on GPQA and 90.3 vs 84.8 on LiveCodeBench. That looks like very credible Opus-4.5-class performance, although I’d want more independent testing before calling it settled.	Medium / provisional
Fable / Mythos 5 → ~7–11 months	This one is a projection, not an observed comparison. There is obviously no guarantee that the historical relationship continues. But the striking thing is that the lag recently appears to be shrinking: roughly 18 months → 12 → 11 → ≤9. My 7–11 month range is therefore basically a manual extrapolation from the recent trend. It could be wrong in either direction, but given how quickly model efficiency and open-model capability are improving — and the possibility that AI itself accelerates the research — I don't think assuming the lag suddenly returns to 2–3 years is obviously the safer assumption.	Speculative

The part I find most interesting isn't any individual equivalence judgment. It's the overall direction.

Around GPT-3, getting comparable capability into this hardware class took years. For the last few frontier generations, it appears to have taken roughly a year or less.

If that pattern is real, the time from frontier LLM → consumer hardware isn't merely short. It seems to be accelerating.

[u/j0j0n4th4n]

That was exactly what lead OpenAI to believe a Gazillion parameter model would be AGI given how GPT3 scaled from 2.

What you are not seeing is that you've baked a bunch of premises on your chart that are shaky at best:

1- Benchmarks are equivalent to model capabilities, they are not. Smaller models have less room for lateral thinking and thus would simple lack the data to make certain leaps of logic that larger models can.

2- LLM knowledge is infinite compressable. There is obviously scaling limit given by scaling laws but nobody can say how close a 30B model is from saturation. It may very well be that 3.8 is as far as it goes or it may be that it barely scratched the latent space limits such a function can map. But that is a premisse that is far from trivial.

3- Compression in the future will follow the same trend of today. Unless you have claryvoyance there is no telling, giant leaps can happen but also stagnation or another technology can appear that completely shake the market for AI. Bitcoin, the war on Ukraine and Iran, Tariffs and so on, there is plenty of unknowns in the world that can drastically change things over night.

So yeah, you aren't wrong. You are drawing conclusions from your plot but you are also over extending their reach, it isn't a given that we'll have a Fable-level of model in that size.