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reasoning benchmark

RSI Index

The RSI (Recursive Self-Improvement) Index is OpenAI's aggregate metric across a bundle of internal AI-research evaluations, including debugging research systems, optimizing kernels and training recipes, and improving other models, measuring progress toward recursive self-improvement.

Updated Aug 11, 2026

Models3
Model coverage3
MetricScore
EvidenceB

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  • Distribution
  • Highlights
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  • FAQ

RSI Index Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

Show columns

01OPGPT-5.6 SolOpenAI57.9%100.0%3CAug 11, 2026
02OPGPT-5.6 TerraOpenAI56.3%50.0%3CAug 11, 2026
03OPGPT-5.6 LunaOpenAI41.9%0.0%3CAug 11, 2026

RSI Index Score Distribution

A closer view of the leading scores on this benchmark.

RSI Index

RSI Index Highlights

The leading models and scores on this benchmark.

Rank #1GPT-5.6 Sol57.9%Rank #2GPT-5.6 Terra56.3%Rank #3GPT-5.6 Luna41.9%

What is RSI Index?

What RSI Index measures and how its scores work.

The RSI (Recursive Self-Improvement) Index is OpenAI's aggregate metric across a bundle of internal AI-research evaluations, including debugging research systems, optimizing kernels and training recipes, and improving other models, measuring progress toward recursive self-improvement.

Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.

Family
RSI Index
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
Yes
Evaluation key
rsi-index|llm-stats-current

Benchmark scores retain their original unit. Overall score eligibility is shown separately.

FAQ

Common questions about RSI Index.

Which model scores highest on RSI Index?

GPT-5.6 Sol is currently ranked first with 57.9%.

What does RSI Index measure?

The RSI (Recursive Self-Improvement) Index is OpenAI's aggregate metric across a bundle of internal AI-research evaluations, including debugging research systems, optimizing kernels and training recipes, and improving other models, measuring progress toward recursive self-improvement.

Is a higher score better?

Yes. Higher values rank better for this benchmark.

How many models are compared?

3 model results are currently shown.

Does this benchmark affect the overall score?

Yes. This benchmark can contribute to the current LLMBoard capability score.