Based on real cutoff data from past years — simple math, no guesswork.
Enter a rank above to see the conversion
| Branch | IIIT Rank (Delhi) | JEE Rank | IIIT Rank (Outside) | JEE Rank |
|---|---|---|---|---|
| CSAI | 782 | 7,009 | 620 | 5,472 |
| CSE | 1,413 | 10,557 | 629 | 5,557 |
| CSAM | 2,254 | 15,152 | 881 | 7,545 |
| CSD | 2,606 | 17,205 | 1,008 | 8,530 |
| CSB | 3,580 | 23,012 | 2,709 | 17,892 |
| CSSS | 3,609 | 23,202 | 2,371 | 15,879 |
| ECE | 3,896 | 25,106 | 1,133 | 9,688 |
| EVE | 4,418 | 28,282 | 1,198 | 9,893 |
A mathematically rigorous approach to rank conversion
Official cutoff data from 5 years (2021–2025) of IIIT Delhi admissions, including the latest Round 5 data. Both JEE CRL ranks and IIIT merit ranks are extracted from official PDFs.
Data is normalized to percentile space to account for varying applicant pool sizes across years (7K–26K for IIIT, 8.6L–15.4L for JEE). Outliers and bonus-contaminated entries are automatically removed.
Piecewise Cubic Hermite Interpolating Polynomial ensures smooth, monotonic mapping — if your IIIT rank is better, your JEE estimate is always better too. No oscillation artifacts.
Each prediction includes a confidence zone based on data density and local slope. Dense regions near popular branch cutoffs have higher accuracy than sparse tail regions.
Transparent metrics so you know what to trust
| IIIT Rank Zone | Data Density | Confidence | Typical Error | Notes |
|---|---|---|---|---|
| 1 – 500 | Medium | ●●●○○ | ±800 | Top ranks — few data points, but tight range |
| 500 – 5,000 | High | ●●●●● | ±1,200 | Best accuracy — densest data, most branch cutoffs here |
| 5,000 – 10,000 | High | ●●●●○ | ±3,000 | Good accuracy — stable ratio zone |
| 10,000 – 18,000 | Medium | ●●●○○ | ±8,000 | Steepening curve, reserved category overlap |
| 18,000 – 26,000 | Low | ●●○○○ | ±25,000 | Tail region — high variability, use with caution |