Frequently asked questions
Which LLM is best for coding in 2026?
Right now Claude Opus 5.5, GPT-6 Astra and Claude Fable 5 lead (graded on SWE-bench Verified, Arena Elo (WebDev) and LiveCodeBench plus an overall task grade). Best open-weight: DeepSeek V4-Flash. Best budget pick graded A or better: Qwen3.7 Flash ($0.03/$0.13 per 1M tokens).
What is SWE-bench Verified and why does it matter?
SWE-bench Verified is a set of 500 human-validated GitHub issues from real Python projects. A model has to read the repository, write a patch and pass the project's own tests. It is the closest public benchmark to day-to-day software work, which is why it carries the most weight in our coding grade.
What is the best free or open-source LLM for coding?
Among open-weight models, DeepSeek V4-Flash, Kimi K2.6 and DeepSeek V4-Pro rank highest for coding right now. Whether you can run one locally depends on your VRAM — check the model in the Can I Run LLM calculator before downloading.
Which coding LLM gives the best value for money?
The cheapest model we grade A or better for coding is Qwen3.7 Flash ($0.03 in / $0.13 out per 1M tokens). Sort the table by price, or switch on the value score, to see the cost of every model next to its scores.
Does a bigger context window make a model better at coding?
Only up to a point. A large window lets the model see more of a repository at once, but accuracy usually drops as the prompt grows. For multi-file work, a model that scores well on SWE-bench Verified matters more than raw context size.