Want to turn a piece of software written in Java into Python without rewriting it from scratch? This is now possible thanks to LLM code translation.
What a time to be alive. Large language models (LLMs) like GPT or CodeT5 can now understand and translate programming languages. This helps developers migrate projects, modernize old codebases, and adopt new technologies much, much faster.
In this article, we’ll explain how AI models learn different programming languages, how code translation is different from code generation, and why translating code isn’t just about swapping syntax (and you guessed it, humans are still very much needed).
🧑🔬 Backed by science & jargon-free
LLM code translation is pretty remarkable. It’s grounded in cutting-edge research and proven developer practices. We’ll reference studies and real-world examples to give you a clear, practical understanding of how AI translates programming languages.
What is LLM code translation
LLM code translation is the use of large language models (LLMs) to convert code from one programming language to another. Instead of manually rewriting everything line by line, you can use AI models trained to understand programming syntax, logic, and structure.
Say you have a project built in Java but want to move it to Python. The best LLM translation models can take care of most of that heavy lifting, saving you hours (or even days) of work. They’re capable of recognizing how the code fits together and can make smart adjustments based on the language you’re moving to.
How AI models understand and translate programming languages
Large language models (LLMs) learn programming languages by studying massive amounts of code written in different languages. They pick up on how syntax is organized, how logic flows, and how different parts of a program work together to solve problems. That’s pretty much how AI translation works as well.
When you give an LLM a piece of code, it does the following:
- First, it breaks the code into smaller parts (called tokens)
- Then, it understands how they relate to each other (i.e., it builds an internal map of how the code flows and understands what’s expected behavior)
- Finally, it rebuilds the same logic in the new language (and it does so with proper rules, syntax, and with respect to best practices)
Tokens are small codes units like keywords (if, while), variable names, operators (+, -), and symbols ({}, []). Each token carries meaning in context, and the model learns the “grammar” of how these tokens fit together to form correct and functional code.
It works a lot like speaking two languages fluently. You would never translate word for word, would you? You’d always translate meaning and adapt it naturally to fit the new language’s rules and style.
🧠 Good to know
The technology has advanced so much that LLMs can fairly easily spot things that need to be adapted. For example, they can adjust how data structures are organized, or rewrite functions to match the new language’s conventions. In some cases, they will swap out libraries that aren’t available in the target environment.
Code translation vs. code generation: what’s the difference?
Code translation rewrites existing code in a new language without changing its behavior. Code generation creates entirely new code based on a description or prompt. Let’s take a closer look.
Code translation means taking existing code written in one programming language and converting it into another. The goal is to keep the logic, structure, and behavior the same. For example, you can translate a Java program into Python without changing what the software actually does.
Code generation, on the other hand, is about creating new code from scratch based on a prompt, a description, or an unfinished piece of code. In this case, AI isn’t just rewriting the code, but filling in the gaps and creating from scratch. It makes decisions about how to design the solution.
| Code translation | Code generation |
| Code translation needs the LLM model to preserve meaning. So, it’s the same idea translated to the new programming language. | Code generation asks the model to create something from scratch. We’re talking about new idea and new code. |
Using LLMs “out of the box” vs. training them for specific tasks
Some LLMs can handle code translation right “out of the box,” while others require extra training to do it well for specific situations.
So, how do you know what’s the right approach?
“Out of the box” models
“Out of the box” models are general-purpose. They’ve been trained on a wide range of programming languages and can handle common translation tasks without any extra work.
For example, if you need to translate a simple Python script into JavaScript, a general model like GPT-4 can usually do a solid job right away.
Specifically trained LLM models
For more specialized tasks, general models might fall short. Let’s say you want to translate old COBOL programs into modern C# while preserving specific banking regulations. Sounds like a nightmare because it can easily turn into one.
To prevent disasters from happening, developers fine-tune the model by feeding it examples from the specific domain they’re working with. This extra training helps the model learn the special rules and styles needed for accurate translation.
🧑💻 Learn from example
COBOL programs often hide critical business rules inside outdated, hard-to-read code. If an LLM only translates the syntax without fully understanding the logic, it can cause serious mistakes. In a highly regulated industry like finance, these errors can range from incorrect payment processing to breaking important compliance rules or data breaches.
This is why it’s equally important to pick the best AI translation tools for translating content, and the best LLM models to convert your code from one programming language to another. Clean and functional code, and relevant multilingual content is the winning combination.
Why translating code isn’t just about changing syntax
At first glance, code translation might seem like a simple matter of swapping syntax. You simply change a for loop in Java into a for loop in Python, or you replace curly braces with indentation. But code translation goes much deeper than that.
Different programming languages have different ways of thinking. This means they use different structures, paradigms, and libraries to solve problems. What’s considered “normal” or efficient in one language might not even make sense in another.
To manage this, LLMs go beyond memorizing syntax. They break down code into smaller units called tokens (keywords, operators, variables) and learn how these pieces fit together to create meaning.
🧑💻 Learn from example
LLMs treat code almost like another human language. They read it, understand it, and re-express it, with context and meaning in mind. Thanks to this deep understanding of the code, models are able to rebuild the same logic in a different language (even if the original structure needs to change).
For example, error handling in Python (with try/except) might need a different approach when translated into Go, which uses explicit error returns instead of exceptions.
Common challenges when translating code with LLMs
Even with all their progress, LLMs still face real challenges when translating code between languages. Here are some of the most common issues developers run into.
Loss of meaning
Sometimes, the AI gets the syntax right, but disregards the purpose behind the code. A function might technically execute, but the logic it was supposed to preserve could shift in subtle ways. This is why testing and human review are critical after any translation (especially for complex workflows).
Language mismatch
Some programming concepts don’t translate neatly across languages. Features like memory management, error handling, or type enforcement can work very differently between ecosystems. Without careful adjustment, the translated code might behave unpredictably or introduce new bugs.
💡 Good to know
Translation keys are primarily associated with software localization. They serve as identifiers that link user interface (UI) elements to their corresponding translations.While they might not be directly involved in the core logic of LLM code translation, they play a crucial role in ensuring that the translated application remains user-friendly and culturally appropriate.
Library and API differences
A library that exists in one language might not have a direct equivalent in another. Sometimes a full rewrite is needed because the original library’s functionality isn’t available elsewhere. Even when alternatives exist, slight differences in behavior can cause issues if they aren’t properly accounted for.
Performance problems
Code that works fine in one language can become slow or inefficient when translated without optimization. For example, nested loops or recursion that are fast in Python might severely impact performance when moved to JavaScript. Developers often need to refactor parts of the translated code manually to restore good performance.
🧑💻 Learn from example
Nested loops and recursion can behave differently across programming languages because of how those languages manage memory and execution speed.
Python is optimized for recursion and has a lot of built-in handling for it, even though it’s not the fastest language overall. JavaScript, on the other hand, traditionally wasn’t designed with deep recursion in mind (just think of environments like browsers where stack size is limited).
If you translate Python code with heavy recursion or deeply nested loops directly into JavaScript, it can quickly cause stack overflow errors or severe slowdowns.
JavaScript engines (such as V8 that’s used in Chrome and Node.js) are fast at simple loops. However, they can struggle with very deep function calls unless the code is rewritten using iterative approaches (like using loops instead of recursion) or other special techniques like tail call optimization.
How developers check if the translation actually works
To ensure such AI-produced code actually works as intended, developers are adopting a mix of validation techniques. Most common include rigorous unit testing, using benchmark test suites, applying formal verification or static analysis tools, and performing thorough manual code reviews.
Unit testing
Sometimes, AI-generated unit tests can contain incorrect assertions or miss edge cases. One of the first steps is running a set of unit tests on the translated code to see if it behaves the same way as the original. Developers often use benchmark suites like HumanEval or LeetCode to systematically check functionality.
Static analysis tools
Static analysis tools help catch problems that unit tests might miss. These tools review code without running it, scanning for potential bugs, bad coding practices, security vulnerabilities, or logical errors.
🧠 Did you know?
Static analyzers can spot issues like unhandled exceptions, unsafe memory access, or unused variables. This helps developers clean up translated code before it causes further issues in production.
In 2025, the tool LeakGuard analyzed 18 real-world software projects and identified 129 previously undetected memory leak bugs. All of these were independently verified and confirmed by the respective development teams. This serves as proof that it’s best to combine tool and human review.
Formal verification
For critical code, developers sometimes turn to formal verification. This means mathematically proving that the code behaves exactly as intended, according to a set of strict specifications.
While full formal verification can be complex and time-consuming, lighter approaches are becoming more accessible. This can mean writing formal specifications for key functions or using theorem-proving tools.
🧠 Good to know
Formal verification methods are particularly useful when translating code for safety-critical areas like finance, healthcare, or aerospace, where even a small error can have serious consequences.
In finance, a small calculation error could result in millions lost. In healthcare, a bug in medical software could affect patient diagnoses or treatment. In aerospace, a glitch in flight control systems could risk lives.
Manual review
Even if the AI-generated code passes all the tests, developers need to take the time to read through it carefully, line by line. They are the ones who need to give the final green light that the logic makes sense and nothing important got lost in translation. That’s how you get the safe and clean code.
Human review is also where hidden problems come to light. Think small edge cases, subtle bugs, or parts of the code that look correct, but don’t quite do what they should. It’s also a chance to polish the code, making it easier to read, faster to run, and better suited for future maintenance.
Let’s take a closer look at the importance of human input.
Human input still matters: review, debug, improve
AI-generated (or AI-translated) code can often look plausible, but still hide bugs or mismatches in functionality. In fact, a 2024 empirical evaluation found that modern code-generation models could solve programming tasks only with constant supervision by human experts.
In fact, LLMs are excellent at producing code that looks right. The output is often marked by clean syntax and correct structure. But it’s easy for a small logical flaw, a missed condition, or a subtle mismatch in behavior to slip through if a developer isn’t closely involved.
That’s why reviewing, debugging, and improving the code matters.
After LLM code translation, you need to take that extra step to refactor messy code, optimize performance, update outdated patterns, and make the result easier to maintain over time.
A real human needs to make sure the code holds up in real-world use. There’s no way around it.
🧑🔬 What the tests show
Recent benchmark tests show that even the best LLMs (specifically GPT-4o, Claude 2, Claude 3, Gemini Pro, and Mixtral) succeed in fully translating up to 47% of code samples. Success rates drop further with larger code, especially once you hit around 100 lines.
While LLMs can significantly speed up parts of a project, fully translating entire real-world codebases is still an ongoing challenge. Human review and manual improvements are still very much needed.
Tools and platforms that support AI code translation
Today, several tools and platforms help developers use AI for code translation. Check the table for an overview of the most popular ones, along with their pros and cons.
| Tool | Description | Pros | Cons |
| Codex & newer GPT versions by Open AI | They can translate between popular languages like Python, JavaScript, Java, and C++ | Supports a wide range of programming languages; flexible and easy to use with natural language prompts | As a general-purpose model, it can miss technical nuances; output often needs manual review and adjustments |
| CodeT5+ by Meta | Specialized model designed specifically for code tasks, including translation across multiple programming languages | Good at preserving logic and structure of the original code | Might require fine-tuning for niche languages; also a bit less flexible for creative prompts |
| CodeGen by Salesforce | Used for code generation and translation, capable of handling longer and more complex codebases | Optimized for handling large inputs; designed to stay close to the original intent of the source code | May need technical setup for practical use (less accessible to those who are not very tech-savvy) |
| Hugging Face transformers | Offers access to a library of models trained for code tasks (e.g., CodeT5, PolyCoder) | Open source, highly customizable, easy to fine-tune on specific datasets or languages | Requires more setup and technical knowledge to deploy; quality of models varies |
🧠 Did you know?
While AI tools often focus on backend logic, platforms like Lokalise can help with translating HTML content (e.g., UI elements and metadata) while preserving structure and tags. This is especially useful when localizing web interfaces or static pages where layout integrity is crucial.
We covered the tools and resources, now let’s take a look at some real-life use cases.
Real-life use cases for AI-powered code translation
Developers are already using it today to speed up their projects and reduce costs. To be clear, AI isn’t replacing developers. Instead, it’s becoming a powerful tool to handle tedious, time-consuming code translation work.
Here are a few ways AI-powered code translation is already making a difference.
Migrating legacy systems to modern languages
Many companies still rely on old codebases written in languages like COBOL or VB.NET. AI models can help translate these into modern languages like Java, Python, or C#. This makes it easier to maintain, scale, and integrate with new technologies.
Switching tech stacks during modernization
When businesses modernize their platforms, they sometimes switch tech stacks entirely. AI can help translate backend code, APIs, or microservices between frameworks and languages.
Creating a more unified “language”
Large systems often mix several programming languages. AI can assist developers in translating modules, so the whole system speaks a more unified “language”. This reduces maintenance complexity and, in a way, it “future-proofs” the code.
Supporting learning and onboarding
LLM code translation also helps new developers understand legacy codebases. When you translate older code into more familiar languages, you can onboard new team members faster and reduce training time.
What’s next: The future of LLM code translation
So, what’s next for LLM code translation? Get ready for an even smoother ride.
As models continue to learn from bigger, more diverse codebases, they’ll pick up on subtle patterns. Think framework conventions, idiomatic styles, even project-specific quirks. That means fewer manual tweaks and faster rollouts when you migrate or modernize a codebase.
We’ll also likely see tighter IDE integrations and real-time collaboration. Imagine a pair-programming session where your AI assistant suggests a Pythonic rewrite as you type Java, and then runs lint checks on the fly? Maybe we’re not that far from it being a reality.
Still, no AI can replace the human touch. Developers will remain the ultimate decision-makers. They are there to review the logic, spot edge cases, and weave in architecture decisions that no model can foresee.
Your deep knowledge of business rules, performance trade-offs, and team standards stays irreplaceable. Tools like Lokalise help along the way.
Built from the ground up for developers and localization engineers, Lokalise combines code-friendly workflows with AI-powered translation features. It’s a glimpse of how the next generation of platforms will empower human teams. Let AI do the heavy lifting while people steer the ship.Want to learn more? Check out Lokalise blog.
