Python is the easiest language to generate and the easiest to generate badly. We ran the same four scripts through six tools on a 2.1-million-line log file and executed every line.
Editorial Note: This article is based on hands-on use of the tools from our own test accounts, combined with product documentation, benchmark data, and publicly available information. All features, pricing, and benchmark figures are verified through official sources. See our Disclaimer.
ChatGPT (GPT-6 Astra) is the best AI code generator for Python in 2026 when you are generating a script from scratch, and it won on the only measure that survives contact with a real project: the code ran. In a matched test of four Python tasks — a pandas cleanup, a FastAPI endpoint with its pytest file, a standard-library-only log parser, and two classic traps — ChatGPT produced runnable code on the first pass in all four and used current pydantic v2 idioms without being told to. GitHub Copilot is the better generator inside an existing Python repository, because it writes into the file you have open instead of making you paste, and it costs $10 against $20. Claude Code (Opus 5) is the one to pick for multi-file Python packages, where the job is changing the project rather than producing a script.
Python is the language where AI generation is most useful and least impressive, because so much of the training data is beginner tutorials from 2019. The failure mode is not syntax — generated Python almost always parses. It is staleness and constraint: pydantic v1 code after v2 shipped, a pandas chain that works on a toy frame and raises on real column types, or an instruction to use only the standard library answered with an import requests. Our four tasks were chosen to catch exactly that. For the notebook-and-API question specifically, our ChatGPT vs Gemini coding test covers the chat-only side; this page is about generation.
September 24 to 26, 2026, on the same 2.1-million-line access log and the same machine. ChatGPT ran on ChatGPT Plus at $20 a month with GPT-6 Astra in a project containing no earlier Python and no memory imported from our other chats. GitHub Copilot ran on Copilot Pro at $10 a month inside VS Code in an empty Python workspace, with agent mode and Copilot Chat enabled and no custom instructions. Each task was asked once, in the same words, and the output was executed without edits — we opened the files, ran python and pytest and recorded the exit code. Where a tool failed, we recorded the failure rather than the fix. A tool scored a task only if the script ran and produced the output we asked for.
| What you are generating | Use | Why |
|---|---|---|
| A standalone script to do one job | ChatGPT | 4 of 4 tasks ran first pass, including the pandas column parsing that broke Copilot |
| Code inside a file you already have open | GitHub Copilot | It writes into the editor with the surrounding module in context; nothing to paste |
| A FastAPI endpoint with pydantic validation | ChatGPT | Used v2 field_validator and model_validate; Copilot emitted v1 @validator |
| A multi-file package or a refactor | Claude Code | Not a generator — an agent that changes a repository and runs the suite |
| Whatever is cheapest per month | GitHub Copilot | $10 against $20, with a free tier that covers small scripts |
| Generating a whole app you then maintain | Cursor | Composer builds across files in the editor with every diff visible before it lands |
1. ChatGPT (GPT-6 Astra) — best generator for Python, from scratch. All four of our scripts ran on the first pass with no edits, the sequence in the pandas task survived messy currency strings that broke Copilot, and it reached for pydantic v2 idioms and a streamed file read without being asked. Its weakness is the one it cannot fix: it does not see your repository, so every generation is a copy-paste round trip. $20 a month.
2. GitHub Copilot — best generator inside an existing Python project. Nothing here beat it for writing into a file that already exists: a 40-second suggestion in the editor, using the helper functions and type hints of the module around it. It lost the pandas task by treating a currency column as a float, and reached for pydantic v1 syntax, which is the staleness problem in one sentence. $10 a month, with 2,000 free completions.
3. Claude Code (Opus 5) — best for Python packages, not scripts. A terminal agent that plans, edits across files and runs your tests, with the strongest Python reasoning of the six. It is the wrong tool for a single script, because you pay in setup and approval steps what a chat answer gives you instantly, but for a package or a refactor it finishes work the others only describe. Included with a Claude Pro plan at $20.
4. Cursor — best when the Python code has to fit a project. Composer generates across several files in one pass with a visible diff for every change, which is why it is the tool to pick if you are building an app rather than a script. It inherited the same pydantic drift as Copilot on our endpoint task, and the $20 plan is the one worth having. See our Cursor vs GitHub Copilot test for the editor-level detail.
5. Gemini 3 Pro — best free tier and long-file reading. A generous free tier and the best of the six at reason on a large pasted file, which makes it the strongest free choice for reading and adapting Python you did not write. Its generated code needed more corrections than ChatGPT’s on three of our four tasks, mostly in the pandas chains.
6. Windsurf — best free in-editor generator. Real agentic edits on a free plan and a sensible editor, but its Python output leaned on older library idioms more than any other tool here and it needed a second pass on the endpoint task. Worth knowing about; not the first pick for generation.
We asked for a script to dedupe an orders file, convert five currencies to USD through a rate dictionary, and print total USD per country. ChatGPT produced a script that stripped $ and thousands separators before casting to float, kept the newest row per order_id with a sort before drop_duplicates, and ran unchanged. Copilot generated the same structure but cast the raw string column with astype(float), which raised a ValueError on the first row containing a comma. One fix, but a fix a beginner would not have known how to make, and the kind of error that makes people decide AI generation does not work.
We handed both a 30-line snippet with a mutable default argument and a closure capture inside a loop, and asked what was wrong and why. ChatGPT identified both, explained that the list default is shared across calls and that the loop variable is resolved at call time rather than at definition, and rewrote them idiomatically. Copilot fixed the output — rewriting the loop to bind the value with a default parameter — but explained only the list, and its note on the closure did not name late binding at all. If you are using generated Python to learn, that is the difference between a fix and a lesson.
ChatGPT with GPT-6 Astra, for Python written from scratch. In our September 2026 test all four of the scripts we asked for ran on the first pass without edits, including a pandas cleanup with messy currency strings and a FastAPI endpoint that used current pydantic v2 APIs. GitHub Copilot is the better generator inside an existing repository because it writes into the open file with the surrounding module in context, and it costs $10 against $20.
Yes, for scripts of the size we tested: all four of ours, between 20 and 120 lines, were complete and executable. Where generation still fails is integration — knowing which of your helpers to call, which dependency your environment already has, and which of last year’s idioms your project has moved past. That is why a tool that can read your repository beats a better model that cannot, and why the answer changes depending on whether the code is standalone.
Copilot for editing Python you already have, ChatGPT for generating Python you do not. Copilot’s suggestions arrive in the editor in about 400 milliseconds using your project’s own conventions and cost half as much; ChatGPT generated correct first-pass code on four of four tasks where Copilot failed the pandas parse and emitted deprecated pydantic v1 syntax. If your work is inside one repository, buy Copilot; if it is scripts and data work, buy ChatGPT.
ChatGPT, in our test, and mostly for defensive reasons rather than clever ones. Its version stripped currency symbols and thousands separators before casting columns to float, sorted before deduplicating so the newest row survived, and did not crash on the messy rows that broke Copilot’s astype(float). Ask whichever model you use to show you the dtypes it assumes, then check them against your own frame before you trust the output.
More than before, not less. Generated Python almost always parses, so the failure is silent: last year’s API, a shared mutable default, a pandas chain that demos well and raises on real types. Two of our four tasks failed exactly there, and every fix required reading the code. Use a generator to get to a first draft quickly, then learn enough to review it — the review is now the job.
Buy ChatGPT if you generate Python from scratch, and GitHub Copilot if you write it inside a project. ChatGPT won all four of our tasks on first pass, handled the messy real-world column types that broke Copilot, used pydantic v2 rather than v1, and named both Python traps instead of quietly fixing one. Copilot lost on generation quality and won on everything around it — a 40-second suggestion written into the open file with your module in context, no copy-paste, and half the price at $10. If you only pay once and your Python lives in a repository, take Copilot; if you write scripts and do data work, take ChatGPT. Add Claude Code when the Python is a package rather than a script, because that is the moment generation stops being the hard part.
From September 24 to 26, 2026 we generated the same four Python tasks in ChatGPT Plus (GPT-6 Astra, $20/month, fresh project, no memory imported) and GitHub Copilot Pro ($10/month, VS Code, empty Python workspace, agent mode and Copilot Chat enabled, no custom instructions). Each task was asked once in identical wording and the output was executed without edits: python for the scripts, pytest for the endpoint. A task counted as passed only if the script ran and produced the requested output. The parser was run against our own 2.1-million-line combined-format access log, including the two truncated lines that really exist in it.
| Metric | ChatGPT (GPT-6 Astra) | GitHub Copilot |
|---|---|---|
| Task 1 — pandas script ran unedited | Yes — stripped $ and separators before casting ✓ | No — astype(float) raised on the first messy row |
| Task 1 — newest row kept per order_id | Yes — sorted before drop_duplicates ✓ | Yes |
| Task 2 — pytest suite passing (of 3) | 3 of 3, no warnings ✓ | 3 of 3, with pydantic v1 deprecation warnings |
| Task 2 — used pydantic v2 idioms | Yes — field_validator, model_validate ✓ | No — v1 @validator and nested Config |
| Task 3 — respected stdlib only | Yes ✓ | Yes |
| Task 3 — streamed the file, no full load | Yes — for line in f ✓ | Yes |
| Task 3 — survived the truncated log lines | Yes — compiled regex, malformed line skipped ✓ | No — split() raised IndexError |
| Task 3 — correct top 10 on 2.1M lines | Yes ✓ | Yes |
| Task 4 — named the mutable default argument | Yes, and explained why the list is shared ✓ | Yes — fixed it, explained only briefly |
| Task 4 — named the closure late-binding trap | Yes, with the rewrite explained ✓ | No — fixed the output without naming the cause |
| Time from request to runnable code in the editor | Round trip via chat: copy out, paste back | about 40 s, written into the open file ✓ |
| Saw our repository and existing modules | No — only what we pasted | Yes ✓ |
| Monthly cost of the plan we tested | $20 Plus | $10 Pro ✓ |
| Winner | 🏆 ChatGPT — 4 of 4 scripts ran first pass, current idioms, both traps named | GitHub Copilot — in-editor generation, repository context, half the price |
ChatGPT ran all four of our Python tasks on the first pass, handled messy real column types and used current pydantic v2 APIs. Copilot writes into the file you already have open for half the price. Run the pandas task above on both before you choose.
Keep exploring — these related comparisons and guides help you decide.