First Look: Testing Llama Code 3 for Python Tasks
Meta's Llama Code 3 offers a real step up for open-source code generation, but it still has rough edges for anything outside of mainstream Python.
Meta's new Llama Code 3 is a solid improvement for open-source code generation, particularly for Python. It's fast, capable on a local machine, and a viable alternative to closed models for specific tasks, but it's not a drop-in replacement for a fully integrated tool like Copilot.
What Did Meta Actually Ship?
Meta's new code model family, Llama Code 3, comes in 8B and 70B sizes, with a 34B model reportedly in training. According to Meta's official announcement post, the models show strong gains on benchmarks like HumanEval, especially the 70B version which is positioned as a top-tier open model. The key promise is better reasoning and fewer basic mistakes.
Does It Work for Real-World Code?
I ran the 8B instruct model locally to refactor a tangled Flask route handler. The task involved separating business logic from the request/response cycle. Llama Code 3 correctly identified the core logic, extracted it into a new function, and updated the original route to call it. It handled Python type hints correctly, which older open models often mangled.
The context window on Llama Code 3 feels more robust. I fed the model a multi-file bug report, and it managed to trace the error across a utility function and a model definition without getting lost. This is a noticeable improvement over its predecessors, which would often lose track of context past a single file.
What Broke?
Where Llama Code 3 stumbled was generating boilerplate for a less-common library. I asked it to create a new client class using the `dramatiq` task queue library. It confidently hallucinated method names and configuration parameters that don't exist. You still need to supervise the model closely when you step off the beaten path of popular frameworks like Django or FastAPI.
Should You Use It?
Yes, if you need a capable open-source model for local inference or fine-tuning. The 8B model is genuinely useful on a modern laptop for scripting, refactoring, and debugging Python code. If you're looking for a polished, multi-language IDE experience and don't care about the open-source aspect, you're probably not the target audience for this release.