llmboard.aiAI model intelligence
Home

Model Rankings

OverallOpen ModelsAgentCodingReasoningMathKnowledgeInstruction FollowingTextVision
Image GenerationImage Editing
Video GenerationImage to VideoVideo Editing
Text to SpeechSpeech to Text
Embeddings

Efficiency

Chat Token PricingImage PricingVideo PricingAudio Pricing
Chat Speed & LatencyProvider Reliability

Benchmarks

GPQAMMLU-ProAIME 2025SWE-Bench VerifiedMMLUHumanity's Last ExamLiveCodeBenchMATHHumanEvalMMMU-Pro
All Benchmarks

Tools

Model Directory

Scoring & Data

Scoring & Data
1224 models729 benchmarks

Leaderboard Center

Overall RankingCodingCore BenchmarksPrice & ValueRuntime Performance

Modalities

All ModelsImage GenerationImage EditingVideo GenerationImage-to-VideoVideo EditingText-to-SpeechSpeech-to-TextEmbeddings

Data & Methods

Scoring MethodAll Benchmarks
llmboard.aiCopyright 2026 llmboard.ai

reasoning benchmark

RSI Index Leaderboard

RSI Index is an aggregate metric across internal AI-research evaluations related to recursive self-improvement.

Updated Sep 5, 2026

Models3
Model coverage3
MetricScore
EvidenceB

On this page

  • Ranking
  • Highlights
  • Distribution
  • Top models
  • About
  • FAQ

RSI Index Ranking

Higher score ranks better on this benchmark.

3 rows
Columns

Show columns

Sort by
Rank
Model
Score
Percentile
Participants
Evidence
Evaluated
Rank01ModelOPGPT-5.6 SolOpenAIScore57.90%Percentile100.00%Participants3EvidenceCEvaluatedSep 8, 2026
Rank02ModelOPGPT-5.6 TerraOpenAIScore56.30%Percentile50.00%Participants3EvidenceCEvaluatedSep 8, 2026
Rank03ModelOPGPT-5.6 LunaOpenAIScore41.90%Percentile0.00%Participants3EvidenceCEvaluatedSep 8, 2026

RSI Index Highlights

The leading models and scores on this benchmark.

RSI Index Score Distribution

A closer view of the leading scores on this benchmark.

RSI Index

The Top AI Models for RSI Index

The first five results on this benchmark, with official price and output speed added where the model identity can be matched.

What is RSI Index?

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.

Family
RSI Index
Modality
text
Primary category
reasoning
Score direction
higher
LLMBoard eligible
No
Evaluation key
overall

LLM Stats. 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 57.90%.

What are the top three models on RSI Index?

The current leaders are GPT-5.6 Sol (57.90%), GPT-5.6 Terra (56.30%), and GPT-5.6 Luna (41.90%).

Which RSI Index model has the lowest official input price?

GPT-5.6 Luna has the lowest matched official input price at $0.20 input / $1.2 output per 1M tokens.

Which models are fastest among RSI Index results?

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).

Does the highest score result prove overall model quality?

No. This benchmark measures one defined capability or task. The overall LLMBoard score uses a separate aggregation across eligible benchmark evidence.

What does RSI Index measure?

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.

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?

No. This benchmark is shown for reference but does not contribute to the overall score.

Rank #1GPT-5.6 Sol57.90%
Rank #2GPT-5.6 Terra56.30%
Rank #3GPT-5.6 Luna41.90%

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.

  1. 01
    OP
    GPT-5.6 SolOpenAI
    Score
    57.90%
    Price
    $4.0 input / $20 output per 1M tokens
    Speed
    Up to 2.36 tok/s via OpenAI

    Strengths

    • Ranks #1 of 3 compared models
    • 100th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures RSI Index, not total model capability
  2. 02
    OP
    GPT-5.6 TerraOpenAI
    Score
    56.30%
    Price
    $2.0 input / $12 output per 1M tokens
    Speed
    Up to 99.35 tok/s via OpenAI

    Strengths

    • Ranks #2 of 3 compared models
    • 50th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures RSI Index, not total model capability
  3. 03
    OP
    OpenAI
    Score
    41.90%
    Price
    $0.20 input / $1.2 output per 1M tokens
    Speed
    Up to 41.87 tok/s via OpenAI

    Strengths

    • Ranks #3 of 3 compared models
    • 0th percentile on this benchmark
    • C evidence result

    Considerations

    • This result measures RSI Index, not total model capability

Selection summary

Best AI Models for RSI Index

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.

GPT-5.6 Luna
Benchmark rank #1GPT-5.6 Sol57.90% · $4.0 input / $20 output per 1M tokens
Benchmark rank #2GPT-5.6 Terra56.30% · $2.0 input / $12 output per 1M tokens
Benchmark rank #3GPT-5.6 Luna41.90% · $0.20 input / $1.2 output per 1M tokens