Code Models

Pricing in code generation follows the same per-token model as general text. Flagship code models (GPT-5 Codex, Claude 4.6 Sonnet, Codestral) cost €1-€10 per million input tokens; budget tiers (Codestral Mamba, DeepSeek Coder, Qwen Coder) cost €0.05-€0.50 per million. A single IDE autocomplete request rarely runs more than a few thousand input tokens, so per-call cost is fractions of a cent. The bills grow when you ship agents that re-prompt themselves dozens of times per task.

The trade-off triangle is correctness, speed, and context. Flagships solve harder problems and follow project conventions more reliably but respond at 30-80 tokens/second, which feels slow inside a tight autocomplete loop. Fast budget models (Codestral Mamba, GPT-5 Mini) stream at 200+ tokens/second and feel native in the editor. For batch tasks (refactor a whole repo, generate tests for fifty files), flagship correctness wins. For tight autocomplete loops, fast tier wins.

Watch out for cross-file context: most autocomplete loops only send the current file. For real codebase-aware refactoring, you need a retrieval layer that pulls related files into the prompt. Tools like Cursor and Continue do this automatically; if you're rolling your own, embed the codebase first and retrieve the top 5-10 most relevant files per request.

Watch out for license contamination: a few open-weights code models were trained on permissively licensed code only; others swept GPL code with unclear redistribution terms. If you're shipping generated code in a closed-source product, prefer commercial models with explicit code-license guarantees.

Top picks above cover the most correct flagship, the cheapest workhorse, the longest-context model, and the fastest autocomplete option.