Best AI Models for Programming

The best AI to program are always a pair: a model that generates code (Claude from Anthropic, GPT from OpenAI, Gemini from Google, or an open-weight model such as Llama, Mistral or DeepSeek), and a tool that brings it into your work, such as GitHub Copilot, Cursor, Claude Code or Codex. The same model family performs very differently in a browser chat and within an agent that reads the repository, runs tests and fixes its own errors. When choosing, how you work and where your code goes matter more than the monthly ranking.

The rankings also age quickly because the products change name and conditions from one quarter to the next. Codeium has become Windsurf, which today is called Devin Desktop; from June 18, 2026 Google stopped serving Gemini Code Assist to personal accounts and directs to Antigravity products, as explained by the official notice. What you find here is verified on the manufacturers' pages on the update date: before subscribing, double-check.

Chat, extension, editor or agent: four ways to get a model working on the code

The first choice is about how to work, and it comes before the brand.

• Chat in the browser or app. ChatGPT, Claude or Gemini answer a question, explain an error, write a function. The model only sees what you glue and the copy-paste you do. It is the starting point of those who write little code, told in ChatGPT for coding.
• Editor extension. GitHub Copilot or Tabnine work within VS Code, JetBrains and other environments: they complete as you write, respond in a side chat, and, in agent mode, edit project files.
• Editor built around AI. Cursor, based on the code of VS Code, and Devin Desktop start from the idea that the model reads the entire project and proposes changes on multiple files together.
• Terminal or cloud agent. Claude Code, OpenAI Codex, and GitHub's coding agent receive a task, execute commands, launch tests, and deliver changes, even in the form of a pull request. How to govern long sessions and stop rules you find it in Claude Code for long runs.

The more you move towards the agent, the more the model sees and the more it can do on its own. With each step grows what you need to control: an agent running commands must be run with limited permissions and on a separate branch, never on the production copy.

Does your code remain in the company? Local plans, training and installations

Here lies the most concrete risk for an SME, and it has changed recently. A developer using a personal Copilot plan on customer code, from April 24, 2026, makes its interactions (inputs, outputs, code snippets, and context) available to GitHub model training, unless you disable the option. With Copilot Business and Enterprise the option doesn't even appear, because GitHub excludes that data by contract (GitHub documentation).

The same pattern appears elsewhere: Cursor excludes data from training only when Privacy Mode is active, and Anthropic distinguishes consumer accounts from those governed by commercial terms. The most useful internal rule fits in one line: company code only on business plans or with verified settings, never with personal accounts left at their defaults.

When code must not leave at all, there are two paths. The first is a product installed in your infrastructure: Tabnine offers Enterprise customers on-premise and air-gapped installations, meaning isolated from the network, according to its documentation. The second is an open model run locally with Ollama. Beware of project continuity: Continue, an open source extension much used to connect local models to the editor, today has the read-only repository and is no longer actively maintained. Before building a workflow on a tool, check who keeps it.

Hardware requirements and cases where home execution really suits are dealt with in Local AI Models.

Claude, GPT, Gemini or open weights: what changes between families

Quality differences between top models are measured on coding benchmarks, with all their limitations: the method to read them is in best AI model for programming and in SWE-bench and coding benchmarks. The availability and conditions of use are especially important for the practical choice.

Claude comes into the code via chat, API, Claude Code and third-party tools such as Copilot and Cursor; typical uses are described in Claude by code. GPT is used in ChatGPT, via API and Codex. Gemini lives in the Google ecosystem, where the developer offering is reorganizing, as shown by the closure of personal access to Code Assist.

The Open-weight models (Llama, Mistral, DeepSeek and others) can be run at home, with a yield that depends a lot on model size and hardware. For DeepSeek there is an extra focus: if you use the Chinese company's cloud service, evaluate where the data ends compared to the GDPR; with locally executed weights the problem does not arise. The licensing differences between the various models are explained in Open Source AI Models.

How much it really saves: the few measured data

Promises of productivity are everywhere, independent measures much less. In a much-discussed controlled study, METR followed 16 experienced developers on 246 real tasks in their open source projects: with AI tools from early 2025 they took 19% more time. Before they expected an acceleration of 24%, and even after they were convinced that they had been 20% faster (METR, July 2025).

It is not a final verdict. In second experiment, published February 2026, METR called the new estimate unreliable, because many developers now refuse to work without AI and this distorts the sample: the real effect could be higher. The practical lesson does not change. The feeling of going fast is not a measure, and the data that counts is that gathered on your tasks.

Trust, among those who use these tools, remains lukewarm. In Stack Overflow Developer Survey 2025 84% of respondents use or intend to use AI tools, but those who distrust the accuracy of the answers (46%) outperform those who trust them (33%).

Three teams, three different choices

An eight-person software house develops bespoke management software. It works on large repositories, with years of history, and customer code is reserved. Here it makes sense a business plan, Copilot Business or Cursor with Privacy Mode active for everyone, and an agent that only works on separate branches. Every change goes from a human review before the merge; a review prompt that helps find bugs is in Reviewing the Code with AI.

A firm of accountants with no IT department has a different need: Python scripts to clean bank statements or merge spreadsheets. Just a generalist chat with a business plan, as long as you work on test data and never on customer archives. The script tries on a copy of the files and is held in a shared folder, with two lines explaining what it does.

Finally, a provider of banking and insurance software often cannot get a line of code out of their servers due to contractual constraints. The road is an internal installation, with a product providing isolated mode or an open model on a corporate server, accepting a lower yield on difficult tasks. Non-sensitive parties, such as documentation or internal tools, can use cloud services with a business plan.

How to prove it on a real task before paying

1. Choose three tasks that are already closed. A bug, a missing test, a small function: you already know the solution, so you can judge the result.
2. Make them solve with two tools. Same repository, same instructions, same starting branch.
3. Measure to the accepted code. Count the time until the merge after review and testing, not that until the first response.
4. Read the terms of the final plan. Training, data retention, and processing agreement should be verified on the floor you will use in production, not the testing floor.
5. Write the team rule. No code generated goes into production without review, testing, and dependency control.

If the test goes well, the next step is to decide where AI enters the development cycle and where it does not. When you need a management software, integration or app built with this method, you can find our approach on the page on custom software.