reasoning benchmark
RSI Index is an aggregate metric across internal AI-research evaluations related to recursive self-improvement.
Updated Sep 5, 2026
Higher score ranks better on this benchmark.
Rank | Model | Score | Percentile | Participants | Evidence | Evaluated |
|---|
| Rank01 | ModelOP | Score57.90% | Percentile100.00% | Participants3 | EvidenceC | Evaluated |
| Rank02 | ModelOP | Score56.30% | Percentile50.00% | Participants3 | EvidenceC | Evaluated |
| Rank03 | ModelOP | Score41.90% | Percentile0.00% | Participants3 | EvidenceC | Evaluated |
The leading models and scores on this benchmark.
A closer view of the leading scores on this benchmark.
The first five results on this benchmark, with official price and output speed added where the model identity can be matched.
What RSI Index measures and how its scores work.
The RSI (Recursive Self-Improvement) Index is an aggregate metric across a bundle of internal AI-research evaluations.
It measures progress toward recursive self-improvement through evaluations including debugging research systems, optimizing kernels and training recipes, and improving other models, reported as a Score with unit ratio.
Scores are shown in ratio. This benchmark is not independently verified and has an evidence level of B.
LLM Stats. Benchmark scores retain their original unit. Overall score eligibility is shown separately.
Common questions about RSI Index.
GPT-5.6 Sol is currently ranked first with 57.90%.
The current leaders are GPT-5.6 Sol (57.90%), GPT-5.6 Terra (56.30%), and GPT-5.6 Luna (41.90%).
GPT-5.6 Luna has the lowest matched official input price at $0.20 input / $1.2 output per 1M tokens.
The fastest matched records are GPT-5.6 Terra (99.35 tok/s via OpenAI), GPT-5.6 Luna (41.87 tok/s via OpenAI), and GPT-5.6 Sol (2.36 tok/s via OpenAI).
No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.
It measures progress toward recursive self-improvement through evaluations including debugging research systems, optimizing kernels and training recipes, and improving other models, reported as a Score with unit ratio.
Yes. Higher values rank better for this benchmark.
3 model results are currently shown.
No. This benchmark is shown for reference but does not contribute to the overall score.
Ranking basisThis rsi index AI model leaderboard uses descending score in the benchmark original unit. The leaderboard ranking keeps matched price and speed data separate from benchmark evidence.
Selection summary
GPT-5.6 Sol currently leads RSI Index with 57.90%. It is the top model on this specific benchmark, while the best LLM for the broader task should also be checked against other benchmarks, price, and runtime.
Use this leaderboard with the supporting benchmark results and coverage details above. A leaderboard position summarizes the selected ranking signal; it does not replace workload-specific testing.