Best AI Models for Coding & Software Development

What We Looked At

• HumanEval benchmark
• Context window for large files
• Language breadth
• Tool/function calling
• Price per token

Why context window matters for coding

Here's where Claude pulls ahead on larger projects: its 200K token context window. Paste an entire repository worth of files and Claude can reason across all of it at once. GPT-4o caps at 128K, which covers most individual files and small-to-medium projects just fine, but you'll feel the limit when reviewing a full pull request or refactoring across several interconnected modules. If your day-to-day involves large monorepos or multi-file refactors, that extra headroom isn't just a nice-to-have.

HumanEval: the standard coding benchmark

HumanEval is the go-to coding measure — give a model a function signature and docstring, check if the Python it writes passes tests. Claude Sonnet hits 93.7%, GPT-4o comes in at 90.2%, mini at 87.2%. A 3-point gap won't feel enormous on everyday tasks; both produce solid, runnable code. SWE-bench tells a more interesting story: it tests whether models can actually fix real GitHub issues in real repositories. Claude's advantage is more pronounced there, especially on bugs that require understanding how code across multiple files interacts.

Best AI coding tools built on these models

Most developers don't use the raw API — they use tools built on top of these models. Claude powers Claude Code (the terminal agent), Cursor, and GitHub Copilot via the API. GPT-4o drives GitHub Copilot Chat and most completions. If you're picking a coding workflow, try Cursor or Copilot before reaching for raw API access. The tooling around the model matters just as much as the model itself.