ModelsArchitectures & capability
Reddit post projects an ECI of about 182.5 for 'Model 4' using a linear fit from an AI R&D benchmarks page
A r/singularity post fits a linear relationship between ECI and a second metric labeled AECI, reports r² of 0.997, and uses it to project an ECI near 182.5 for 'Model 4'.
From the last30days · reddit article of November 7, 2026 and cited sources: The post fits the line ECI ≈ 1.0721 × AECI − 8.982 to data from a linked AI R&D benchmarks uplift page and reports a Pearson r² of 0.997. [1]
AT Pulse research deskPublished · Updated · 1 min read · AI-assisted analysisAI-assisted, not human-edited. Methodology Corrections Who owns this site
- What happened
- The post fits the line ECI ≈ 1.0721 × AECI − 8.982 to data from a linked AI R&D benchmarks uplift page and reports a Pearson r² of 0.997. [1]
- Original source
- last30days · reddit article, independent perspective
- Brief by
- AT Pulse research desk AI-assisted; publisher and contact on the desk page
- When
- November 7, 2026 article published
- Covered
- Nov 9 edition
- Evidence
- 1 source across 1 site
Reach: not measured yet
The post fits the line ECI ≈ 1.0721 × AECI − 8.982 to data from a linked AI R&D benchmarks uplift page and reports a Pearson r² of 0.997. [1]
Using that fit, the poster projects an ECI of about 182.5 for 'Model 4'. The post doesn't say which model this is, what AECI value went into the formula, or how the source data was chosen. [1]
Capability indices are increasingly used to compare frontier models and forecast progress.
Read the full assessment
Projecting a score for an unreleased or unclear model from a single regression can produce eye-catching numbers, so readers need to know the method and what it assumes.
based on this input https://tecunningham.github.io/ai-rnd-benchmarks/uplift.html?utm_source=chatgpt.com ECI≈1.0721xAECI-8.982 Pearson r^2=0.997 (huge huge correlation) this result in model 4’s ECI = 182.5