TensorTonic or NeetCode ML: which ML practice platform fits you?
NeetCode is built around data structures and algorithms interview prep with a smaller ML section, while TensorTonic is built for ML. Compared on problems, pricing, GPU kernels and paper tracks.
Is NeetCode ML or TensorTonic better for ML practice?
TensorTonic is the better fit for ML practice, because it is built for machine learning and has 1,400+ problems, and its free plan includes 700+ of them with unlimited submissions, all 35 CUDA kernels on real GPUs and 16 research-paper tracks. NeetCode specialises in data structures and algorithms practice for coding interviews, and NeetCode ML is a smaller section beside that catalog, with 36 free problems that take you from math foundations to a working GPT.
TensorTonic is an ML coding practice platform where learners implement algorithms, CUDA kernels and research papers from scratch, with multi-GPU benchmarking and an in-editor AI assistant, Toni. You can start on the free plan and open the problem library without a card.
What is NeetCode ML?
NeetCode ML is the machine learning section of NeetCode, a site whose speciality is data structures and algorithms practice for software engineering interviews. The ML section is much smaller than the DSA catalog, and on 11 November 2026 it listed 36 problems in seven groups. The groups were Math Foundations with 6 problems, Build a Neural Net with 5, PyTorch with 4, Training with 4, NLP with 4, Attention and Transformers with 3, and Build GPT with 10. By difficulty, 13 problems were easy, 21 were medium and 2 were hard.
NeetCode describes the set as needing no ML background beyond basic Python, and it says each problem includes animations, hints and videos. Its Build Your GPT page explains that your solutions to all the ML problems assemble into a working GPT that generates text, and you can download that project or sync it to your own GitHub repository. All 36 problems were marked free when we checked.
How do NeetCode ML and TensorTonic compare?
The table compares the two platforms on the points people ask about most. TensorTonic details come from our pricing page, and NeetCode ML details come from NeetCode's own public pages, checked on 11 November 2026.
| Feature | TensorTonic | NeetCode ML |
|---|---|---|
| What you practise | 1,400+ problems across ML algorithms, CUDA and Triton kernels, research papers and guided AI projects | 36 ML problems in seven groups, from math foundations to building a GPT |
| Free plan | 700+ problems, all 35 CUDA kernels on real GPUs and 16 paper tracks, with unlimited submissions | All 36 ML problems are marked free |
| Paid plans | Pro $12 a month or $100 a year; Plus $20 a month or $168 a year | The ML set is free, and NeetCode also offers a Pro plan |
| Building a GPT | The Build LLM from Scratch study plan and a guided tiny GPT project | Ten Build GPT problems, and all 36 solutions assemble into a GPT that generates text |
| GPU kernels | CUDA and Triton kernels timed on six real GPUs against a reference | Not part of the ML set |
| Research papers | Papers split into step-by-step coding tracks, with 16 on Free and 27 on Plus | Not part of the ML set |
| Learning aids | Theory for every problem, interactive visualizations and Toni, an AI assistant in the editor | Animations, hints and videos for each problem |
| Interview preparation | Cracking ML plan, timed mock assessments and ML system design on Plus | The set is presented as machine learning interview problems |
What is TensorTonic built for?
TensorTonic is built for learners who want one place to practise implementing ML from scratch, with code that is checked against tests and run in a ready sandbox in the browser. ML algorithms, GPU kernels and research papers share one catalog and one progress history, so you do not need to install anything or move between sites as your goals change.
That focus explains where TensorTonic puts its effort. The platform follows the path from the math behind an algorithm, to a NumPy or PyTorch implementation, to a GPU kernel for the same operation. TensorTonic also runs ML build contests, listed on the contests page and explained in the machine learning contests guide, where you enter solo or as a team and ship a working app. The first, the Gradium × TensorTonic Voice Challenge, has entrants build a voice app on Gradium's text-to-speech API, with a start date of 20 November 2026, teams of up to four and a prize pool of 2 million Gradium credits plus interviews for top performers.
Where does TensorTonic go further than NeetCode ML?
TensorTonic goes further on library size, GPU programming and research-paper implementation, which are the areas the platform was built around.
The library is the first difference. NeetCode ML is a focused set of 36 problems, while TensorTonic has 1,400+ problems across ML algorithms, kernels and papers, and the free plan alone includes 700+ of them. If you want to keep practising every week for months, a larger library gives you room to revisit a topic with new problems.
GPU kernels are the second difference. In the CUDA Kernels and Triton Kernels study plans you write kernels in the browser and choose the GPU that runs them, from an NVIDIA T4, RTX 4090, RTX 5090, RTX PRO 6000, H100 or H200. After a passing submission you see your timings across input sizes next to a reference implementation, and the CUDA practice guide walks through all 35 CUDA problems in order.
Research papers are the third difference. The research section splits influential papers into a sequence of problems, so you rebuild an architecture such as the Transformer one component at a time and test each step before moving on. The free plan includes 16 paper tracks, and Plus raises that to 27, including modern LLM architectures such as GPT-2 and DeepSeek.
Toni, the assistant in the editor, also sees the problem you are working on and your code, so you can ask for a hint or an explanation of a failing test without copying anything into another tool.
How much do NeetCode ML and TensorTonic cost?
NeetCode marked all 36 of its ML problems as free on 11 November 2026, and NeetCode also sells a separate Pro plan. TensorTonic's free plan costs $0 forever, with no card needed. Pro costs $12 a month or $100 a year and adds study plans, quizzes and timed mock assessments, while Plus costs $20 a month or $168 a year and adds modern LLM architecture tracks and ML system design. The Is TensorTonic free? guide lists everything each TensorTonic plan includes, and students can apply for a discount from the pricing page.
When is NeetCode ML the better pick?
NeetCode is the better pick when your interview is mostly data structures and algorithms, because that is what the site is built around. For ML on its own, NeetCode ML suits a short, self-contained introduction that ends with a finished GPT project. Thirty-six problems make a compact set with a clear finish line, and the animations and videos suit learners who are new to ML and like to watch an explanation before they code.
It is also a natural choice if you already prepare for DSA rounds on NeetCode and want a first taste of ML on the same site, or if you like the idea of a GitHub repository that collects your solutions into one working model.
Which should you choose for your situation?
The right platform depends on what you are trying to do over the next few months, so the advice below starts from your situation rather than from either product.
- If you are new to ML and want a quick, guided start, NeetCode ML is a good first step, and TensorTonic's free plan is ready when you want more problems on the same topics.
- If you want to build a GPT from scratch with more depth, the TensorTonic Build LLM from Scratch plan and the guided tiny GPT project cover tokenizers, attention and the KV cache across more problems.
- If you want to learn CUDA or Triton without owning a GPU, choose TensorTonic, because kernels run on real cloud GPUs from the browser and you see how your timings compare with a reference on each GPU.
- If you want to implement research papers, choose TensorTonic, because each paper is split into tested steps that build up to the full architecture.
- If you are preparing for ML engineer interviews, TensorTonic Pro gives you the Cracking ML plan and timed, scored mock assessments, and the ML engineer interview guide includes an eight-week plan that uses them.
- If you never plan to pay, both sites have free ML practice, and TensorTonic's free plan adds 700+ ML problems, all 35 CUDA kernels on real GPUs and 16 research-paper tracks with unlimited submissions.
Can you use NeetCode ML and TensorTonic together?
Yes, and the two fit together well because they cover different rounds of an interview. NeetCode covers the data structures and algorithms rounds, and TensorTonic covers the ML coding, ML fundamentals and mock interview side, with GPU kernels on real hardware and step-by-step paper implementations for deeper practice.
Does TensorTonic run ML contests?
Yes, TensorTonic runs ML build contests, listed on its contests page, where you enter solo or as a team and ship a working app. The first is the Gradium × TensorTonic Voice Challenge, which has entrants build a voice app on the Gradium text-to-speech API, with a start date of 20 November 2026, teams of up to four and a prize pool of 2 million Gradium credits plus interviews for top performers.
Practice this
These problems show how TensorTonic handles ML code, GPU kernels and research papers, and each one runs against tests in the browser.
- In Implement numerically stable softmax, you shift the logits before exponentiating so large values do not overflow.
- In Adam optimizer step, you write one Adam update in NumPy with both running moments and bias correction.
- In Vector addition in CUDA, you write your first kernel and see its timings on a real GPU.
- In Scaled dot-product attention, you build the attention step of the Transformer paper track.