MATLAB to Python Converters in 2026: What Actually Works

MathWorks ended perpetual MATLAB licenses in January 2026. Engineers and researchers who previously bought once are now facing $2,000+ per seat, per year. The migration from MATLAB to Python is accelerating, and the first thing most people search for is a converter.

The search results are not encouraging. Most of the tools that come up were built between 2008 and 2016, abandoned by their developers, and have not been updated for modern MATLAB. Here is an honest assessment of every converter available today, what each one actually does, and which ones are worth your time.

Status: Inactive. No new PyPI releases in over 12 months.

SMOP was the most ambitious open-source MATLAB-to-Python compiler. It uses a proper parser to build an abstract syntax tree from MATLAB code and then generates Python. In theory, this is the right approach.

In practice, SMOP self-describes as "alpha quality" in its own README. The generated Python is what the developers call "matlabic" — it looks like MATLAB written in Python syntax rather than idiomatic Python. Variable names, array indexing, and control flow all retain MATLAB patterns instead of using Python conventions.

The project has not received meaningful updates. There is a fork called smop3 described as "a placeholder in case maintenance halts," which tells you everything about the project's trajectory.

What it handles well: Basic syntax translation, function definitions, simple loops.

What it does not handle: Modern MATLAB features (string arrays, tables, datetime), toolbox functions, plotting, or any construct added to MATLAB after approximately 2015. The output requires significant manual cleanup to be usable.

Verdict: Was promising a decade ago. Not viable for production use today.

Status: Deprecated. The developer explicitly recommends using SMOP instead.

LiberMate is a regex-based translator that attempts to convert MATLAB .m files to Python. It was hosted on SourceForge, which gives you a sense of the era. The GitHub repository has a deprecation notice at the top.

The tool cannot handle MATLAB command syntax — the kind of code every engineer writes. grid on must be rewritten as grid('on') before LiberMate can process it. It does not map most MATLAB functions to their NumPy or SciPy equivalents.

What it handles well: Very basic syntax: variable assignments, simple loops, basic arithmetic.

What it does not handle: Command syntax, toolbox functions, plotting, cell arrays, structs, anonymous functions, switch/case, try/catch, or any MATLAB-specific construct beyond the simplest expressions.

Verdict: Deprecated by its own developer. Do not use.

Status: Stale. Originally from 2008. Online translator shut down in 2021.

OMPC takes a fundamentally different approach from other converters. Instead of translating MATLAB to idiomatic Python, it creates a MATLAB compatibility layer. Your code runs in Python but still looks and behaves like MATLAB — MATLAB-style indexing, MATLAB-style function calls, MATLAB-style everything.

This means your "converted" code depends on the OMPC runtime library. If that library stops being maintained — which it effectively has — your code stops working. You have not actually migrated to Python. You have created a dependency on an abandoned compatibility layer.

The online translator was shut down in 2021 due to server issues and never came back.

What it handles well: Running MATLAB code in a Python environment without actually changing it.

What it does not handle: Producing standalone Python code that uses standard libraries. Your output is not real Python — it is MATLAB running on a Python interpreter through a compatibility shim.

Verdict: Not a converter. It is a compatibility layer, and it is not maintained.

Status: Multiple abandoned projects sharing the same name.

Searching for "mat2py" on GitHub returns at least five different repositories, none of which are actively maintained:

The most commonly referenced one is a bash script that does simple find-and-replace operations on MATLAB files. It has not been updated in eight years.

Another mat2py project aims to create a browser-based MATLAB-compatible console. The goal is ambitious — a serverless MATLAB replacement — but the actual converter functionality is minimal.

A third project called matlab2python by ebranlard is a simple line-by-line converter that relies on SMOP internally. Since SMOP itself is inactive, this project inherits all of its limitations.

What any of them handle well: Basic syntax substitution — replacing end with pass, % comments with #, semicolons with nothing.

What none of them handle: Toolbox functions, index shifting, matrix operations, plotting, structs, cell arrays, or any of the constructs that make up real MATLAB code.

Verdict: None of these are production-ready tools. They are experiments and weekend projects.

Status: Active. AI-based. The most popular current option.

CodeConvert.ai is a generic code translation tool that supports 60+ language pairs, including MATLAB to Python. It uses a large language model to generate the translation.

The output is often cleaner than the open-source tools — it can make smart structural decisions like converting MATLAB structs to Python dictionaries and using default parameters instead of nargin checks. For simple scripts under 50 lines, it produces reasonable results.

The problems emerge with larger files. In our testing, CodeConvert.ai silently dropped 30% of a 50-line optimization visualization script — the entire plotting section was replaced with a comment saying "Plotting and other logic omitted." There was no warning, no flag, no indication of what was removed.

The output is also non-deterministic. Run the same file twice and you may get different Python code. This makes it impossible to build a reliable migration process or to audit what changed between runs.

What it handles well: Simple scripts, function signatures, basic control flow. Smart structural decisions on small code.

What it does not handle reliably: Large files (silently drops code), toolbox-specific functions (may hallucinate function names), plotting (often omitted or wrong), deterministic output (changes between runs).

Verdict: Useful for quick prototyping and learning. Not reliable for migrating production code where you need to know exactly what was converted and what was not.

Status: Active. Capable but unpredictable.

Many engineers skip dedicated converters entirely and paste their MATLAB code directly into ChatGPT or Claude with a prompt like "convert this to Python." This works surprisingly well for short scripts and isolated functions.

The quality depends heavily on the prompt, the model version, and luck. Sometimes you get excellent, idiomatic Python. Sometimes you get code that looks right but has subtle index-shifting bugs or uses functions that do not exist. There is no way to predict which outcome you will get.

The fundamental issue is the same as CodeConvert.ai: non-deterministic output with no flagging system. The AI does not tell you which parts it is confident about and which parts it guessed on. Every line has the same apparent confidence, whether it is a trivial assignment or a complex toolbox function mapping that the AI may have hallucinated.

For a single function you plan to test thoroughly, this approach works. For migrating a codebase of 50 scripts where you need auditable, reproducible output, it does not scale.

What it handles well: Short scripts, explaining what the code does, suggesting Pythonic alternatives.

What it does not handle reliably: Large files, consistent output, toolbox-specific accuracy, any form of "this part needs your review" flagging.

Verdict: Good for learning and one-off conversions you plan to test manually. Not a migration tool.

The honest answer is that no tool converts MATLAB to Python perfectly. The languages are different enough — 1-based vs 0-based indexing, the transpose/string ambiguity, toolbox-specific function signatures — that some human review is always required.

The question is not "which tool gives me perfect output" but "which tool saves me the most time while being honest about what it could not handle."

A deterministic converter that processes every line, flags what needs review, and produces the same output every time gives you a reliable starting point. An engineer who reviews 10-20 flagged items in a 500-line file spends 1-2 hours on cleanup instead of 15+ hours on manual translation.

The dead and abandoned tools — SMOP, LiberMate, OMPC, the various Mat2py projects — are not options for anyone migrating real code in 2026. CodeConvert.ai and direct AI prompting work for small tasks but silently fail on the complex code that matters most.

The gap in the market is a MATLAB-specific converter that understands toolbox functions, handles index shifting systematically, produces deterministic output, and tells you exactly what it could and could not convert. That is what we built.

Paste any MATLAB function into our converter — 50 lines free, no account required. Compare the output, the flags, and the compatibility report to any other tool. The difference is not subtle.