Gmr Institute of Technology | Cut-Off Details | Vidyavision

Srikakulam, Andhra Pradesh

College Type
Autonomous
Co-Ed Status :
Co-Education
Minority Status :
Non Minority
Affiliated To :
Jawaharlal Nehru Technological University, Kakinada

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Gmr Institute of Technology Cut Off Details

GMR Institute of Technology (GMRIT), established in 1997 in Rajam, Srikakulam district, Andhra Pradesh, is a highly regarded autonomous engineering college managed by the GMR Varalakshmi Foundation. Over the years, the institution has recently transitioned into GMR University, adopting a Deemed-to-be-University status.

Securing a seat at GMRIT requires navigating a structured, highly competitive cut-off threshold.For undergraduate technical streams, admission relies directly on performance metrics from state and national-level engineering entrance exams.

1. Admission Criteria & Intake Structure

Admissions to GMRIT follow structural guidelines mapped out across transparent quota allocations:

Convenor Quota (Category-A - 70% Seats): Filled through state-level counseling frameworks. Candidates must hold a valid rank in the Engineering, Agriculture, and Pharmacy Common Entrance Test (AP EAPCET / formerly AP EAMCET) or AP ECET for lateral entries.
Management Quota (Category-B - 30% Seats): Filled directly by the institution based on past academic performance (Class 12th aggregates) and competitive weights from national exams like JEE Main. With its transition toward a Deemed University framework, in-house entrance channels alongside national score assessments are increasingly prioritized for specific direct intakes.
Postgraduate Entry: Admission to the M.Tech specializations is filtered through the AP PGECET ranking matrix alongside valid GATE

2. AP EAPCET B.Tech Cut-Off Trends: General Category (OC)

Computer Science and engineering (CSE), along with its specialized fields like Artificial Intelligence & Machine Learning (AI&ML), routinely claim the highest cutoff brackets. The following table showcases the closing ranks for the General Category (Home State Quota) candidates over recent academic cycles:

B.Tech Specialization	2025 Closing Rank	2024 Closing Rank	2023 Closing Rank
Computer Science and Engineering (CSE)	6,292	6,765	7,417
CSE (Artificial Intelligence & Machine Learning)	5,529	9,843	11,696
Artificial Intelligence and Data Science (AI&DS)	11,600	11,221	12,434
Information Technology (IT)	11,913	15,125	15,927
Electronics & Communication Engineering (ECE)	11,177	12,911	15,924
Electrical and Electronics Engineering (EEE)	26,750	30,467	32,708
Mechanical Engineering	18,582	40,986	24,083
Civil Engineering	27,600	43,845	29,724

3. Category-Wise Cut-Off Analysis

Depending on your category status and local/non-local domicile rules, the entry closing barriers open up dynamically. Below are the general category-wise closing trends for high-demand specializations like CSE and AI&ML recorded across the recent 2025 counseling runs:

Specialization: B.Tech Computer Science and Engineering (CSE)

Reservation Category	Male / Gender-Neutral Closing Rank	Female Specific Closing Rank
General (OC)	6,145 – 7,108	7,908
EWS	11,644	14,200
BC-A	9,787	17,486
BC-B	12,162	16,980
BC-D	10,687	13,250
BC-E	27,726	33,290
SC	19,456	42,500
ST	90,135	1,13,454

4. AP PGECET M.Tech Cut-Off Trends

For postgraduate candidates aiming for a structural transition into research and specialized industry design paths, admission numbers are based on the state’s Post Graduate Engineering Common Entrance Test (AP PGECET). The baseline closing numbers for the primary M.Tech engineering tracks are summarized below:

M.Tech Specialization Track	Open Category (OC) Closing Rank Range
Transportation Engineering	412 – 543
Thermal Engineering	631 – 850
VLSI & Embedded Systems Design	159 – 2,027
Computer Science and Engineering	1,413 – 2,462

5. Critical Variables Shifting GMRIT Cut-offs

Every year, candidate requirements fluctuate based on specific external drivers:

Shifting Academic Status: GMRIT's transformation into a specialized university setup means direct admissions are restructuring, which influences the traditional state counseling rank distribution.
The "Tech-First" Shift: Branches combining data intelligence (AI&DS, AI&ML) show tighter cut-offs year-over-year compared to traditional manufacturing fields.
EAPCET Difficulty Index: A hard or complex math-science layout in the state engineering entrance test stretches the overall distribution of ranks, creating lower closing barriers for subsequent counseling rounds.

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