AIEvals

Benchmarks

Vals RSI Index

Four autonomous model-development tasks: compression, language-model training, harness engineering and post-training.

Autonomous researchVals AI23 models run by Vals AIUpdated 29 September 2026

How it is run

Version 1.1 normalizes each task to frozen baseline, reference and theoretical-maximum anchors; the overall index averages the four task scores.

What it cannot tell you

The percentage is a reference-normalized index, not task accuracy. Task budgets and vendor-native agent harnesses differ.

Leader
Claude Opus 5.537.31% · Vals AI's own list
Top-ten spread
13.43%Leader minus tenth place, or last place in smaller fields.
Median
21.76%of the 23 rows published
No published result
26of 46 models on this site

Independent leaderboard · Vals AI

Top 23 of 23 runs by Vals AI, one harness.

#ModelScore
1Claude Opus 5.537.31%
2Claude Fable 5.136.09%
3Claude Opus 533.02%
4Gemini 4 Argon30.55%
5GPT-6 Sol28.11%
6GPT-6 Astra27.07%
7Grok 4.625.46%
8Claude Fable 524.32%
9Claude Opus 4.823.98%
10GPT-5.6 Sol23.88%
11GLM 5.322.91%
12Qwen3.8 Max21.76%
13Kimi K320.88%
14Grok 4.720.20%
15Gemini 3.8 Flash19.93%
16Muse Spark 1.3 Max19.64%
17Claude Opus 4.719.08%
18Gemini 3.7 Flash18.27%
19GPT-5.516.48%
20Gpt 5.2 2025 12 1115.80%
21GPT-5.415.48%
22Claude Opus 4 5 2025110114.82%
23Kimi K2.5 Thinking6.85%

No published result tracked

26 of the 46 models on this site have no published number on this benchmark, so their cells in the home table stay blank rather than reading as zero.

Sources

Every page used for this benchmark, read on 17 October 2026.