[Legend2440]

No, you are misunderstanding the paper.

https://arxiv.org/abs/2607.06764

The base model got 15%. They built an elaborate looping harness that allows it to burn 100k tokens "thinking" about the problem, which got the 57%. This is just an alternative approach to reasoning.

[Zababa]

I'm not referring to this paper, I'm referring to this leaderboard: https://arcprize.org/leaderboard. Set it to "arc agi 1", "base LLM" and you'll see deepseek at 57%. Submitted 2025-12-01, $0.120 per task. The paper you linked was later than that, and also says "We do not report an official ARC Prize leaderboard score".

So this paper doubled the price to get the same exact result at base Deepseek 3.2 at launch, and wasn't even tested on the verified set.

[Legend2440]

I think this is an error in the leaderboard. Looking at the test logs, they had reasoning effort set to 'high'. So it should be in the CoT category instead of the base LLM category.

https://huggingface.co/datasets/arcprize/arc_agi_v1_public_e...

  "kwargs": {
     "max_tokens": 100000,
     "stream": true,
     "reasoning_effort": "high",
     "rate_limit": {
        "rate": 2,
        "period": 60
     }
  }

The other paper ran Deepseek v3.2 without reasoning as a baseline and got 15.5%, which is much more in line with other base LLMs like GPT-5.2.

[Zababa]

Interesting, good find! Yeah I may be wrong and this may be an error in the leaderboard. Weirdly it shows no reasoning cost and no reasoning tokens used, but for example here https://huggingface.co/datasets/arcprize/arc_agi_v1_public_e... the answer is super short but it says "4945" completion tokens.