What are the CAT percentile cutoffs, fees, and placement packages for India's top 95-80%ile B-schools?

This breakdown compiles CAT cutoff percentiles, complete fee structures, and placement data for India's leading business schools across the 95–80%ile range, with honorable mentions for three institutions in the 75%ile band. The data tracks degree types, sectional and overall percentile cutoffs with seat counts, mean placement packages, speculated top-25% cohort salaries, and complete fees including tuition, accommodation, and other charges — all as of 2024–2026.

A few important nuances have emerged from the conversations we've tracked, and they're worth walking through carefully.

On cutoffs and what they actually mean

IIT Roorkee's effective cutoff remains genuinely unclear — estimated at 92–94%ile — because the institute doesn't publicly share its composite score methodology, unlike the top four IITs. That's not a data gap we can paper over, and applicants should factor in that uncertainty when planning.

TISS's listed estimate of 94%ile has drawn pushback from experienced observers who place it closer to 97%ile or above. The counter-argument, which we find reasonable, is that TISS made a relatively recent shift to CAT and historical data is still thin. Both positions have merit; we'd treat the 94%ile figure as a floor rather than a reliable central estimate.

IMI Delhi generated the most substantive debate in this area. Some candidates have reported conversions at 85–90%ile, while the more defensible position is that 90%ile represents a safe threshold for general category candidates — with the important caveat that 95%ile applicants have been known to miss calls when their academic profile is weak. This is one of the clearest illustrations of something we emphasize consistently: percentile is a filter, not a guarantee.

For private B-schools like GIM and IMT, cutoffs have shown meaningful variation depending on application round and remaining seat availability. IMT's cutoff was subsequently corrected to 92%ile, and IMI's to 90%ile — both worth noting if you're working from older reference points.

On fees

Fee structures drew strong reactions, and understandably so. XIMB at ₹27.92 lakhs — which includes non-AC mandatory residential housing — struck many as difficult to justify relative to competing options. BITSoM's fee positioning at near-ABC-tier levels similarly raised questions given where its placement outcomes currently stand.

A practical point worth keeping in mind: fees increase year on year, and variations of ₹1–2 lakhs at the time of admission should be expected. The figures here are reference points, not locked-in numbers.

DoMS IIT Kanpur fees were revised to ₹5.11 lakhs — one of the more dramatic corrections in this dataset, and relevant for anyone evaluating the IIT DoMS category on a value basis.

On placement data

This is where the most caution is warranted. Concerns about placement stat inflation are legitimate, particularly at institutions like BITSoM and MU, where independent investigators have flagged methodology questions. In our experience, IIM and IIT placement data carries meaningfully more credibility — it can be verified through RTI requests. Private sector placement reporting operates with far less transparency, and we'd strongly recommend treating those figures as directional rather than precise.

That said, stat-padding in some form is widespread enough across Indian B-schools that the more useful exercise is triangulating multiple data points — alumni conversations, sectoral hiring patterns, median versus mean distinctions — rather than relying on any single published figure.

The bigger picture

The picture that emerges from all of this is that percentile is the starting point of a much more layered evaluation. Composite scores, academic records, GDPI performance, and category all interact in ways that can move outcomes significantly in either direction. A 92%ile candidate with strong academics and a compelling profile may outperform a 96%ile candidate who hasn't built a coherent application. We've seen this pattern consistently enough that we'd caution against treating any cutoff number as the whole story.

Use this data as a structured starting framework — but build your college list with the full picture in mind.