The best AI for coding

For most coding work, Claude Opus 5 is the strongest all-round pick: it is built for agentic coding and can read a large codebase in one go. For the hardest problems in big codebases, Claude Fable 5.1 goes further. For code alone at a lower price, GPT-5.3 Codex is purpose-built.

Our picks

  1. Best overall

    Claude Opus 5

    Built for agentic coding and complex reasoning, with room to read a whole service and its tests at once.

    LiteAnthropicTextReads up to 1M tokens
    Details
  2. For the hardest problems

    Claude Fable 5.1

    Anthropic’s most capable model, made for complex codebases and problems that need long, careful reasoning.

    ProAnthropicTextReads up to 1M tokens
    Details
  3. Built for code

    GPT-5.3 Codex

    OpenAI’s coding model, made for complex code generation and refactors that span many files.

    LiteOpenAITextReads up to 400K tokens
    Details
  4. Best free pick

    Gemini 3.8 Flash

    Strong at coding with a very large context window, at Flash speed and a low price.

    FreeGoogleTextReads up to 1.0M tokens
    Details
  5. For quick reviews

    Grok Build

    xAI’s coding model for generation, review and refactoring, and the lowest-priced of these picks.

    FreexAITextReads up to 256K tokens
    Details

Side by side

Prices are each provider’s list price.

The picks for coding, side by side
ModelPlanInput priceOutput priceReads up toSpeed
Claude Opus 5Lite$5 per 1M tokens$25 per 1M tokens1M tokensPowerful
Claude Fable 5.1Pro$10 per 1M tokens$50 per 1M tokens1M tokensPowerful
GPT-5.3 CodexLite$1.75 per 1M tokens$14 per 1M tokens400K tokensPowerful
Gemini 3.8 FlashFree$0.75 per 1M tokens$3.75 per 1M tokens1.0M tokensBalanced
Grok BuildFree$1 per 1M tokens$2 per 1M tokens256K tokensBalanced

What matters for coding

Three things separate a good coding model from a merely clever one. The first is how much code it can see at once. A model with a large context window can hold a whole service and its tests, so its changes fit the code around them instead of guessing at it.

The second is reasoning. Debugging is detective work, and models built for agentic coding plan a change across files before they write it, rather than patching the line the error points at.

The third is cost per attempt. Coding is a loop of try, test and fix, so a cheaper model you can run ten times often beats an expensive one you run once.

Tips for better results

  1. Paste the error together with the file it points at, not the error on its own.
  2. Ask for tests alongside the fix, and run them before you accept the change.
  3. For a refactor, describe the end state first and ask for a plan before any code.

Questions

Which AI is best for learning to code?

A fast, low-cost model such as Gemini 3.8 Flash is a good start, because you can ask why as often as you like. Ask it to explain each line, not just to write the code.

Can I use a different model for each task?

Yes. In Araveil you can switch models in the same conversation, for example GPT-5.3 Codex for a refactor and Claude Opus 5 to review it.

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