Artificial Intelligence and Machine Learning cutoff, Punyashlok Ahilyadevi Holkar Solapur University, Solapur

Artificial Intelligence and Machine Learning at Punyashlok Ahilyadevi Holkar Solapur University, Solapur closed at merit 1,13,051 (71.52 percentile) in CAP 2026 Round I on open seats, 1,18,269 in the last round.

CAP 2026, Round I closing
1,13,051 (71.52 percentile)
CAP 2026, last round closing
1,18,269 (70.34 percentile)
Seat type
General open, other than home university
CAP seats
60
Years of data
2026
Choice code
0604192110
CAP 2026 Round I closing by seat type
Seat typeClosing merit numberPercentile
General open, other than home university1,13,05171.52
General OBC, other than home university1,16,67070.69
General SEBC, other than home university1,33,64465.48
General SC, other than home university1,41,54062.82
Ladies open, other than home university1,01,37274.98
Economically weaker section (EWS)2,06,07330.90
Tuition fee waiver (TFWS)1,00,72775.17

Questions

What was the Artificial Intelligence and Machine Learning cutoff at Punyashlok Ahilyadevi Holkar Solapur University, Solapur in CAP 2026?

It closed at merit 1,13,051 (71.52 percentile) in CAP 2026 Round I on open seats, 1,18,269 in the last round.

What were the category cutoffs (OBC, SC, ST, EWS, TFWS)?

Round I closing merit numbers in CAP 2026: General OBC, other than home university 1,16,670; General SEBC, other than home university 1,33,644; General SC, other than home university 1,41,540; Ladies open, other than home university 1,01,372; Economically weaker section (EWS) 2,06,073; Tuition fee waiver (TFWS) 1,00,727.

How many CAP seats does Artificial Intelligence and Machine Learning at Punyashlok Ahilyadevi Holkar Solapur University, Solapur have?

60 seats in the CAP 2026 seat matrix, across all seat types.

Similar cutoffs nearby

Computer & IT engineering colleges in Solapur

Other branches at Punyashlok Ahilyadevi Holkar Solapur University, Solapur

Closing merit numbers from the official State CET Cell CAP lists. Past cutoffs describe what happened, not what will happen.