Best Framework STEM Students Laptops, standard weight, with Touchscreen in Mexico

What a STEM Student laptop means: a laptop that holds up to engineering coursework,fast CPU for compilation, enough RAM to run simulations or a VM, GPU headroom for the occasional ML or CUDA assignment, while still portable enough for daily campus life. Typical use: MATLAB, SolidWorks, AutoCAD, Python and Jupyter notebooks, compiling C++ or Rust, occasional CUDA work, recorded lectures. Fits engineering, computer science, physics, math, and data-science majors,undergrads through PhD.

Also relevant for: STEM laptops · engineering laptops · CS student laptops · MATLAB & simulation

Updated: June 2026
Products evaluated: 2281
How we work: Editorial principles →

Showing: Framework or Microsoft · standard weight · Touchscreen

Showing 1–3 of 3 stem students laptops
  1. #1
    Champion
    Objective profile
    Microsoft Surface Laptop 7 15"
    (Core Ultra 7 258V, 32GB, 1TB)
    Microsoft · 15" · 2025
    MX$46,823 – MX$57,228
    STEM Student SelvaScore
    72 / 100
    Weight1.66 kg
    Battery15.0 hr
    Display15.0" 2496x1664
    RAM32 GB
    Storage1 TB
    SelvaScore breakdown
    • 51
      Multi-Core Performance Multi-Thread Scalability
    • 56
      System Memory RAM Capacity
    • 62
      Single-Core Speed Single-Thread Velocity
    • 58
      Storage Speed Storage Throughput
    • 81
      Battery Life Autonomous Runtime
    • 70
      AI Acceleration NPU Inference Capacity
    • 59
      Weight Gravitational Load
    • 66
      Thermal Stability Thermal Throttling Resistance
    Mexican retailer links coming soon, check back shortly.
    View full review →
  2. #2
    Runner-Up
    Objective profile
    Microsoft Surface Laptop Studio 2
    (i7-13800H, 32GB, 1TB)
    Microsoft · 14.4" · 2023
    MX$63,031 – MX$77,039
    STEM Student SelvaScore
    68 / 100
    Weight1.98 kg
    Battery8.0 hr
    Display14.4" 2400x1600
    RAM32 GB
    Storage1 TB
    SelvaScore breakdown
    • 62
      Multi-Core Performance Multi-Thread Scalability
    • 56
      System Memory RAM Capacity
    • 53
      Single-Core Speed Single-Thread Velocity
    • 43
      Storage Speed Storage Throughput
    • 55
      Battery Life Autonomous Runtime
    • 43
      AI Acceleration NPU Inference Capacity
    • 49
      Weight Gravitational Load
    • 19
      Thermal Stability Thermal Throttling Resistance
    Mexican retailer links coming soon, check back shortly.
    View full review →
  3. #3
    Top Pick
    Objective profile
    Microsoft Surface Laptop Studio 2
    (i7-13700H, 64GB, 2TB)
    Microsoft · 14.4" · 2023
    MX$77,421 – MX$94,625
    STEM Student SelvaScore
    48 / 100
    Weight1.98 kg
    Battery8.0 hr
    Display14.4" 2400x1600
    RAM64 GB
    Storage2 TB
    SelvaScore breakdown
    • 52
      Multi-Core Performance Multi-Thread Scalability
    • 70
      System Memory RAM Capacity
    • 53
      Single-Core Speed Single-Thread Velocity
    • 34
      Storage Speed Storage Throughput
    • 55
      Battery Life Autonomous Runtime
    • 43
      AI Acceleration NPU Inference Capacity
    • 49
      Weight Gravitational Load
    • 49
      Thermal Stability Thermal Throttling Resistance
    Mexican retailer links coming soon, check back shortly.
    View full review →

Quick Comparison

#LaptopScorePrice WeightBatteryDisplayRAMStorage
1 Microsoft Surface Laptop 7 15" (Core Ultra 7 258V, 32GB, 1TB) 72 MX$46,823 – MX$57,228 1.66 kg 15.0 hr 15.0" 2496x1664 32 GB 1 TB
2 Microsoft Surface Laptop Studio 2 (i7-13800H, 32GB, 1TB) 68 MX$63,031 – MX$77,039 1.98 kg 8.0 hr 14.4" 2400x1600 32 GB 1 TB
3 Microsoft Surface Laptop Studio 2 (i7-13700H, 64GB, 2TB) 48 MX$77,421 – MX$94,625 1.98 kg 8.0 hr 14.4" 2400x1600 64 GB 2 TB

How we ranked these

SelvaScore is independent. Rankings reflect our editorial judgment, not vendor relationships. No laptop manufacturer pays for placement, and affiliate revenue from retailers does not affect which laptop wins this list.

Every laptop here has been evaluated through the same lens for this use case. The same machines are evaluated differently for other use cases. What works for one use case doesn't always work for another.

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