Why do all models seem similarly good at Python?

Python is the most represented language in training data and in benchmarks, so the spread between models is compressed. The differences show up in library-version recency and long multi-file work rather than in basic syntax or logic.

How do I stop a model inventing library functions?

Paste the relevant documentation section into the prompt for any library where the version matters, and include your installed versions. Then run a strict type checker — a function that does not exist has no stub, so the checker flags it.

Are type hints worth the effort on AI-generated Python?

Yes, more than on hand-written code. They convert a class of runtime failure into a static one, catch misspelt attributes and wrong argument types before execution, and give a cheaper model something concrete to iterate against.

Best Model for Python: Everything Passes Until Runtime

Python is the language every model writes well, and that is the problem. It is the most represented language in every training corpus, the default for tutorials, notebooks and interview questions, and the language most benchmark tasks are written in. The result is a field where the differences between models are compressed and the ranking is hard to feel.

So the useful question is not which model writes the best Python. It is which failures Python lets through, and which model choices and workflow choices reduce them.

No compiler means no cheap oracle

In a compiled language, a whole class of model error is caught before you run anything: wrong types, missing methods, misspelt identifiers on a path that never executes. Python catches none of that until the line runs, and often not then.

A generated function with a typo in an error branch will import cleanly, pass the happy-path test, and fail in production three weeks later. A method called on the wrong object type is a runtime AttributeError, not a build failure.

This shifts where verification effort has to go. In TypeScript or Rust the compiler does the first pass for free. In Python that pass has to be bought — with tests, with type checking, or with a human reading carefully. The TypeScript case is the useful contrast, because there the verifier is free and it changes the economics of model choice completely.

Library churn and invented APIs

The most common Python failure from a strong model is not bad logic. It is a call to a function that does not exist, or exists with different arguments than the one the model learned.

The Python ecosystem moves constantly: keyword arguments get renamed, functions get deprecated and removed, the recommended entry point for a library changes between major versions. A model trained before a breaking change will confidently produce code against the old interface, and the code reads perfectly.

The signature of this failure is worth learning. Invented APIs tend to be plausible — the function name is what it should have been called, the arguments are what you would have designed. That plausibility is why review misses them. Why LLMs hallucinate covers the mechanism; the practical mitigation is to paste the relevant documentation section into the prompt for any library where version matters.

Environments are half the failures you will see

A large share of "the model wrote broken code" reports are environment problems. The code is correct for a different version of the library, a different Python minor version, or an environment where an optional dependency is installed.

Models cannot see your virtual environment, your lockfile or your installed versions unless you tell them. They will assume a recent-ish version of everything and a permissive environment.

Fixing this is cheap and almost nobody does it. Put the output of your dependency manifest, or at least the versions of the four or five libraries that matter, into the system prompt. The improvement in first-attempt correctness is larger than what you would get from moving up a model tier.

Type hints as a partial verifier

Python has an optional type system, and it turns out to be a good fit for AI-generated code specifically because it converts a runtime failure class into a static one.

Ask the model to annotate everything it writes, then run a type checker in strict mode. That catches misspelt attributes, wrong argument types, None flowing where it should not, and a share of the invented-API problem — a function that does not exist has no stub, and the checker will say so.

It is not the full compiler you get in a statically typed language, and untyped third-party libraries leave gaps. It is still the highest-return single change available to a Python team generating code at volume, and it makes cheaper models viable by giving them something to iterate against.

Tests are the other half

Where type checking catches shape errors, tests catch logic errors, and Python's lack of a compiler makes both more necessary than in other languages.

The productive pattern is to have the model write the implementation and the tests in separate passes, ideally with the test pass not seeing the implementation. A model that writes tests immediately after writing the code tends to write tests that assert what the code does rather than what it should do.

Then run them and feed failures back. This loop converges, and it is where a cheaper model earns its place — three iterations of DeepSeek V4 Flash can cost less than one shot from a frontier model and reach the same passing state. Generating tests with LLMs covers keeping the two passes honest.

Notebooks are a different task

A large fraction of Python is written in notebooks, and notebook code has different failure modes. Execution order is not source order, state persists across edits, and a cell that works may depend on a variable defined in a cell that has since been changed.

Models are poor at reasoning about this because the notebook file does not record what actually ran. If you are working in notebooks, include the current variable state or the recent execution history in context rather than only the source.

The analytical side of that work has its own separate concerns, covered in choosing a model for data science — correct Python that computes the wrong statistic is a different problem from broken Python.

Recommendation

For everyday application Python with type checking and tests in the loop, a mid-tier model is genuinely sufficient. DeepSeek V4 Pro at roughly $0.44 in and $0.87 out is the sensible default, and DeepSeek V4 Flash is enough for routine work like scripts, glue code and boilerplate.

Step up to GLM-5.2 or Kimi K3 for large multi-file refactors, unfamiliar frameworks, or anything running unattended where per-step reliability compounds.

For self-hosting, Qwen 3.6 27B is the practical single-GPU option, and Python is exactly the kind of well-represented language where a smaller model gives up least.

The decision rule: invest in the verifier before the model. Strict type checking plus a real test suite plus pinned library versions in the prompt will beat a tier upgrade, cost nothing per token, and keep working when you change models. When a cheap model is enough covers where that logic stops applying.