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OpenAI’s AGI number came from a harness, not the model

Posted on September 6, 2026September 6, 2026 By Emily Chen No Comments on OpenAI’s AGI number came from a harness, not the model

OpenAI’s AGI Claim: A Critical Look

OpenAI’s AGI Number: A Question of Harness vs Model

OpenAI’s declaration of the Artificial General Intelligence (AGI) era was based on a 99.9% score from GPT-6 Astra. However, when running the same model through the benchmark software provided by ARC Prize, the score dropped significantly to 62.7%. This disparity highlights the importance of the harness, or the software surrounding the model, in determining its performance.

The Harness Effect

A harness dictates what tools a model can access, its contextual memory, and how it manages conversations. When OpenAI’s Provider Adapter harness was used, preserving the model’s opaque reasoning state, Astra achieved a 99.9% score at a lower cost compared to the standard ARC Prize harness.

Benchmark Results: A Closer Look

ARC Prize ran Astra under both harnesses and published the results:

  • Standard harness: 62.7% at maximum reasoning, costing $26,098.
  • Provider Adapter: 99.9% at high reasoning, costing $18,817.

Interestingly, setting the reasoning effort to none within OpenAI’s adapter still yielded a respectable 96.7% score, surpassing the standard harness by 34 points.

Token Efficiency and Speed

The Provider Adapter runs were more efficient, using 49% fewer tokens and completing tasks roughly 3.66 times faster than the standard harness.

Misinterpretation of Results

The reported figure of 99.9% against GPT-5.6 Sol’s 7.8% was widely shared, including in our own coverage. However, this comparison is between different harnesses, not like-for-like. The benchmark itself states it is "not claiming that [Astra] is AGI".

Evolving Scores

After publication, Fortune discovered that some scores had changed. OpenAI altered five metrics, with Astra’s hallucination rate fluctuating from 4.2% to 2%. Other models’ scores also shifted, with Sol’s ExploitBench score doubling.

Conclusion

The AGI debate continues, with a focus on the role of the harness in model performance. As ARC Prize moves forward, it will publish both harness results side by side for increased transparency.

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