Best AI Models for Python Development 2026: Top 10 Ranked

Updated October 15, 2026. Benchmark figures are quoted from public leaderboards and model cards, and prices from vendor documentation. Each is linked. This site ran none of the benchmarks. Sections marked as recommendation are editorial judgment, not test results.

Quick answer: which AI is best for Python coding?

Python is the language AI coding models are tested on most. SWE-bench, the benchmark behind most "best coding model" claims, is built from 12 Python repositories, so its ranking is a Python ranking. On the official board the leader is Claude 4.5 Opus at 76.8%, with Gemini 3 Flash, MiniMax M2.5 and Claude 4.6 Opus all within 1.2 points.

Those are last winter's models. The ones on sale now are Claude Opus 5.5 ($4 / $20 per million tokens), GPT-6 Astra ($10 / $50), Gemini 4 Argon ($2 / $10 until the end of November 2026) and Gemini 3.8 Flash ($0.75 / $3.75), and none has a leaderboard entry yet. For most Python work the mid-priced pair, Claude Sonnet 5.5 and GPT-6.1 Sol at $2 / $10, is the sensible default.

The best local model for Python with a published score is Qwen3.6 27B: Qwen reports 77.2 on SWE-bench Verified. It is an 18GB download for a 24GB GPU. On 16GB, gpt-oss 20B; on 8GB, Qwen2.5-Coder 7B.

Top 10 AI models for Python, ranked

The order is our judgment. Cloud models are placed first because they lead every public board. Local models follow in order of their published SWE-bench Verified results, then by how much hardware they need.

Percentages are SWE-bench Verified. Official-board figures are from swebench.com, mini-SWE-agent entries dated February 2026. Prices from Anthropic, OpenAI and Google. Read October 15, 2026.

Earlier versions of this page ranked Claude 4 Sonnet, GPT-5, Gemini 2.5 Pro, CodeLlama 70B and DeepSeek Coder 33B. All have been superseded; see older local models for the ones that still run on Ollama.

Python benchmark results: SWE-bench Verified

The original SWE-bench dataset is 2,294 real issues drawn from 12 popular Python repositories (paper). Verified is a 500-issue subset that human annotators checked for clear descriptions and correct tests. A model passes an issue when its patch makes the repository's tests pass.

The rows below use the board's bash-only setting, where every model is driven by the same minimal agent. That makes them comparable with each other.

Source: swebench.com Verified board. Newest entry: February 26, 2026.

How to read it for Python work. The issues come from libraries such as Django, SymPy and scikit-learn, so the benchmark rewards reading an unfamiliar codebase and making a careful fix. It does not test writing a pandas analysis from scratch, building a FastAPI service, or training a model. No public benchmark breaks scores down by framework. Pages that quote a "Django accuracy" or a "pandas accuracy" for a model are not quoting a published test.

Cost matters as much as rank. The Aider polyglot leaderboard, which includes Python and was last updated in November 2025, publishes what each run cost. GPT-5 (high) scored 88.0% for $29.08. DeepSeek V3.2-Exp scored 74.2% for $1.30.

Older models people still search for. Anthropic's October 2024 announcement put the upgraded Claude 3.5 Sonnet at 49.0% on Verified, up from 33.4% for the earlier version. DeepSeek's README reports 12.7 on the original SWE-bench for DeepSeek-Coder-V2 236B and 0.0 for the 16B Lite model. Those numbers show how far the field moved in under two years. The full tables, including SWE-Bench Pro, are on AI coding models ranked, and SWE-bench explained covers how the benchmark is built.

Cloud models for Python: prices and picks

Sources: Anthropic models overview, OpenAI models and pricing, Gemini pricing. Read October 15, 2026. Gemini 3.1 Pro Preview, which an earlier version of this table listed, is now marked as shut down by Google.

The picks below are opinion, based on those prices and on how each vendor describes its model.

• Scripts, automation and small tools. Any model in the table writes a correct 100-line script. Use the cheapest one you already have access to. GPT-6 Luna, Claude Haiku 5.5 and Gemini 3.8 Flash are priced for exactly this.
• Django, Flask and FastAPI applications. Claude Sonnet 5.5, GPT-6.1 Sol or Gemini 4 Argon. Web application work is mostly multi-file edits of moderate difficulty, which is what the mid tier is for.
• A large or unfamiliar codebase. Claude Opus 5.5 or GPT-6 Astra. This is the job SWE-bench measures, and it is what both vendors say these models are for.
• Data work with pandas and NumPy. The model cannot see your DataFrame unless the tool runs code for it. Pick a product that executes Python and paste df.dtypes and df.head() when it cannot. That matters more than which model is behind it.

Which GPT is best for Python?

OpenAI's documentation names GPT-6 Astra as "our most capable model for the most demanding work" and GPT-6.1 Sol as "near-Astra performance for complex work at a lower cost". For Python, Sol is the default and Astra is for the hard, long jobs. GPT-5.5, which earlier versions of this page recommended, is now listed as a previous-generation model at $5 / $30; GPT-4o is still sold at $2.50 / $10 but has no reason to be chosen for new work. More on the older model in our GPT-5.5 page.

Which Claude is best for Python?

Anthropic recommends starting with Claude Opus 5.5 "for most workloads" and reserving Claude Fable 5.1 for "demanding reasoning and long-horizon agentic work". For day-to-day Python, Sonnet 5.5 at half the Opus price is the usual pick; our Claude model guide compares the tiers.

Which Gemini is best for Python?

Google describes Gemini 3.8 Flash as "engineered for long-horizon software engineering" and Gemini 4 Argon as its flagship reasoning model. On the official board the cheaper Gemini 3 Flash (75.8%) outscored Gemini 3 Pro (69.6%), so price and rank do not always move together.

For the chat products rather than the APIs, see ChatGPT vs Claude vs Gemini for coding. To compare subscription cost with running a model yourself, use the local AI vs ChatGPT cost guide.

Best AI by Python framework: Django, Flask, FastAPI, pandas, PyTorch

No benchmark separates these. What follows is recommendation, and the one rule that holds across all of them is that the model needs the versions and the conventions in the prompt.

Django

Any current cloud model; locally, Qwen3.6 27B or Qwen3-Coder 30B. Django code is highly conventional, which helps: models have seen a great deal of it. Tell the model the Django version, whether you use Django REST Framework or Ninja, and paste the relevant models.py. Read migrations and anything touching QuerySet evaluation yourself, since N+1 queries in loops are the most common thing a model writes that works and is still wrong.

Flask

Simple enough that the cheap tier is fine. The things to specify are the application-factory pattern (or not), the extensions in use (Flask-SQLAlchemy, Flask-Login) and the SQLAlchemy version, because 1.x and 2.0 query styles differ and models mix them.

FastAPI

State Pydantic v1 or v2 and whether your SQLAlchemy layer is async. Those two choices change almost every line a model writes for a FastAPI endpoint. Ask for the pytest test alongside the endpoint; the test is what tells you whether the dependency injection was wired correctly.

Data science: pandas, NumPy, scikit-learn

The model's quality matters less than whether it can execute code. A notebook assistant or an agent with a terminal can inspect the DataFrame and correct itself; a chat window is guessing at column names. Paste df.dtypes, df.head() and the shape when the tool cannot run code. See the notebooks section below.

Machine learning: PyTorch and training code

Any current cloud model writes a correct training loop. What they get wrong is the shape bookkeeping between layers, and the only check that works is running one batch through the model. Name the PyTorch version and the hardware (CUDA, ROCm, Apple MPS or CPU), and ask the model to assert tensor shapes at each stage. For diffusion and other generative pipelines, the same rule applies with the library's version pinned in the prompt.

Scripts and automation

The lowest-stakes category and the one where any model, including a 7B local one, does well. For a one-off script, the cheapest tool you have is the right tool.

Best AI by Python task: scripts, debugging, tests, refactoring, learning

Recommendation, not measurement.

Debugging. Paste the whole traceback, not the last line. The useful information is the chain of frames above the error, and a model that sees it usually fixes the bug in one step. Agents that run the code themselves (Claude Code, Codex CLI, aider, Cursor's and Continue's agent modes) close this loop without copy-and-paste.

Generating unit tests for NumPy and pandas code. No public benchmark ranks assistants on this. What decides the result is whether the tool can run pytest and read the failures. An agent that runs the tests will correct a wrong expected value; a chat window will not know it was wrong. Ask for numpy.testing.assert_allclose and pandas.testing.assert_frame_equal in place of plain equality, and give it a small real sample of the data.

Refactoring at scale. This is the job SWE-bench Verified actually measures, so the ranking at the top of the page applies directly. Use an agent, not a chat window, and run the test suite between steps.

Learning Python with an AI. Any current model is a patient tutor, and that is also the trap: it will write the code for you if you let it. Ask it to explain and to review your attempt rather than to produce the answer. Our Python Lab pairs short lessons with practice drills for exactly that reason.

Best local AI models for Python coding

A local model keeps your code and data on your own machine and has no per-token cost. You pay in hardware and in capability.

Sources: Ollama tags pages (sizes re-read October 15, 2026), Hugging Face model cards, swebench.com. "None" means no such number is published; we did not fill the cell.

What hardware each one needs

The download has to fit in GPU memory with room left for the context. As a rule, add a few gigabytes to the download size and round up to the next card.

Two notes on the scores. Qwen's 77.2 for Qwen3.6 27B was measured by Qwen with its own agent. And for the one local model that has both a vendor figure and an official-board figure, Devstral Small 2, the two are 68.0% and 56.4%. Expect a local model to do well on contained tasks, such as a function, a test file or a bug with a clear traceback, and to need more steering on long ones.

Qwen3-Coder-Next, which earlier versions of this page described as running on modest hardware, is a 52GB download at its smallest. It is not a desktop model.

Setup commands

# Install Ollama (Linux; macOS and Windows have installers on ollama.com)
curl -fsSL https://ollama.com/install.sh | sh

# Pull a model that fits your card
ollama pull qwen3.6:27b        # 24GB GPU
ollama pull gpt-oss:20b        # 16GB GPU
ollama pull qwen2.5-coder:7b   # 8GB GPU

# Ask it something
ollama run qwen2.5-coder:7b "Write a Python function that parses ISO 8601 dates"

Ollama's default context is 4K tokens on cards under 24GB. A traceback plus the module it points at can exceed that, and the model then answers without having seen the whole file. Raise it:

OLLAMA_CONTEXT_LENGTH=32000 ollama serve
ollama ps   # confirms the context size in use

More detail: local AI coding models, Qwen3-Coder, 7B vs 14B vs 32B vs 70B for coding, and the 8GB, 12GB, 16GB and 24GB VRAM guides.

Older local models

• Code Llama (codellama:7b 3.8GB, 13b 7.4GB, 34b 19GB, 70b 39GB) and its Python variants (codellama:7b-python through 70b-python, same sizes). Released under Meta's own licence, with training data up to January 2024. The 70B does not support the long context the smaller sizes do. Superseded by everything in the table above; see CodeLlama Python 13B if you still run it.
• DeepSeek Coder 33B (deepseek-coder:33b, 19GB). 16K context, trained on 2 trillion tokens of which 87% was code. Superseded by DeepSeek-Coder-V2 16B, which is itself an older generation now.
• WizardCoder 15B and Phind CodeLlama. Ollama has no 15B WizardCoder tag (the sizes are 7B, 13B, 33B and 34B). Both have been removed from the ranking.

AI in Jupyter, Colab and VS Code notebooks

Notebook work is where "which model" matters least and "can it run the cell" matters most.

• JupyterLab. Jupyter AI is an open-source JupyterLab extension that connects AI agents to notebooks; its documentation lists OpenAI, Anthropic, GitHub Copilot and Mistral among the providers, and says agents "request permission before writing files, running commands, or using MCP tools". Local models reach it through an OpenAI-compatible endpoint such as Ollama's.
• Google Colab. Colab's free GPU tier is a way to run an open model, or a notebook that calls one, when your own machine cannot; see running an LLM on Google Colab free.
• VS Code notebooks. Copilot and Continue.dev both work cell by cell in VS Code's notebook editor, and Continue can point at a local Ollama model.

Whichever you use, the assistant that can see the output of the previous cell beats the one that cannot, so prefer the integrations that execute over pasting cells into a chat.

Using these models in PyCharm, VS Code, Cursor and the terminal

Prices from GitHub's Copilot plans page and cursor.com/pricing, read October 15, 2026. Plans carry monthly request or credit allowances.

PyCharm. Copilot's JetBrains plugin and Continue.dev's JetBrains extension both run in PyCharm, so a PyCharm user has a hosted route and a local route without switching editors. Copilot's code review, included from the Pro plan up, also runs inside JetBrains IDEs. Our Copilot guide covers setup.

Cursor. A separate editor built around agent workflows, with several vendors' models selectable. See the Cursor guide and Cursor vs Copilot vs Claude Code.

Terminal agents. Claude Code, Codex CLI and aider read the repository, edit files and run pytest themselves. Aider's documentation warns that Ollama's default context is too small for agent work and that excess context is "silently" discarded, so set OLLAMA_CONTEXT_LENGTH before pointing any of them at a local model.

Which Python version for AI work?

A recurring question under this page is "best Python version for AI". The honest answer is the newest version your libraries support, which lags the newest release by a few months. The release status on python.org as of October 15, 2026:

Python 3.15.0 shipped on October 9, 2026, and 3.10 reached end of life eight days earlier. Recommendation: for new AI projects, use 3.13 or 3.14 unless every library you depend on already publishes wheels for 3.15; check PyTorch's and your other heavy dependencies' install pages before upgrading, because compiled packages are the ones that lag. Tell the model which version you are on, since syntax such as pattern matching and the newer typing forms depends on it.

Python SDKs and frameworks for building AI features

A separate question hides inside "best Python AI platform": not which model writes your code, but which library you use to call a model from Python.

Sources: ollama-python, anthropic-sdk-python, openai-python, ai.pydantic.dev. Read October 15, 2026.

For a first project, the official client for whichever vendor you use is enough; the agent frameworks earn their place once you have tools, retries and multiple steps to manage. Our Ollama Python API guide covers the local route end to end.

Free AI for Python coding

• Free tiers. GitHub's plan page lists Copilot Free with 2,000 completions a month and a free Student plan; Cursor has a free plan; the model vendors offer free API or chat tiers with changing limits.
• Open-weight models. Every local model in the table above is free to download. Qwen3.6, Qwen3.8, Qwen3-Coder, Devstral Small 2, gpt-oss and Qwen2.5-Coder 32B are confirmed Apache 2.0 on their cards.
• Open-source tools. Continue.dev, aider, Cline and Jupyter AI cost nothing and work with those models. See free local AI models.

Checking AI-written Python

Advice that applies to every model on this page.

• Run it. Most wrong answers fail on first execution. Tests written by the same model share its misunderstandings, so write at least one test yourself.
• Read the imports. Models sometimes import a package that does not exist or call a function that was removed in a recent release. Check each one against your installed version.
• Look for the classic traps. Mutable default arguments (def f(items=[])), bare except: clauses that swallow errors, blocking calls inside async functions, and ORM queries inside loops.
• Type-check it. Ask for type hints and run mypy or pyright. It catches a share of the mistakes before anything runs.
• Read anything involving secrets, SQL, file paths or subprocess yourself.

A good prompt is specific about versions and constraints:

Python 3.14, FastAPI, SQLAlchemy 2.0 async, Pydantic v2.
Write CRUD endpoints for a Project resource.
Include pytest tests. Do not add new dependencies.

Frequently asked questions

What is the best AI for Python coding?

For paid cloud use, Claude Sonnet 5.5 or GPT-6.1 Sol for everyday work and Claude Opus 5.5 or GPT-6 Astra for hard problems in a large codebase. On the official SWE-bench Verified board, which is a Python benchmark, the top score is Claude 4.5 Opus at 76.8%.

What is the best local AI model for Python coding?

Qwen3.6 27B if you have a 24GB GPU; Qwen reports 77.2 on SWE-bench Verified. On a 16GB card, gpt-oss 20B. On 8GB, Qwen2.5-Coder 7B.

Which GPT is best for Python coding?

GPT-6.1 Sol for most work and GPT-6 Astra for the hardest jobs, going by OpenAI's own positioning. GPT-5.5 and GPT-4o are still sold but are previous-generation models.

Is there a free AI for Python?

Every local model on this page is free to download and run, and all but the smaller Qwen2.5-Coder sizes are confirmed Apache 2.0. Copilot Free, Cursor's free plan and the vendors' free tiers cover the hosted side with usage limits.

Which AI is best for learning Python?

Any current cloud model, used as a tutor rather than a code generator: ask it to explain and to review your attempt. For structured practice, the Python Lab has short modules with drills.

Are AI models better at Python than at other languages?

The evidence is better for Python, which is not the same thing. Most benchmarks are Python, so Python is where scores are published. On the multi-language version of SWE-bench the same models score a few points lower. See best AI for JavaScript and TypeScript for that comparison.

Do I need a GPU to run a Python coding model locally?

For the 7B model, no, though it will be slow on a CPU. For anything 20B and larger, yes in practice. Our tutorials cover setup.