> ## Documentation Index
> Fetch the complete documentation index at: https://docs.kong.fyi/llms.txt
> Use this file to discover all available pages before exploring further.

# Supported Architectures

> Architecture and language support matrix with confidence levels

## Confidence matrix

Kong works with most Ghidra-decompilable binaries. Confidence varies by architecture and source language:

|                  | C      | C++    | Go     | Rust   |
| ---------------- | ------ | ------ | ------ | ------ |
| **x86**          | High   | High   | Medium | Medium |
| **x86-64**       | High   | High   | Medium | Medium |
| **ARM (32-bit)** | High   | High   | Medium | Low    |
| **AArch64**      | High   | High   | Medium | Low    |
| **MIPS**         | Medium | Medium | Low    | Low    |
| **PowerPC**      | Medium | Medium | Low    | Low    |

## Confidence definitions

**High** — Kong reliably decompiles, deobfuscates, and recovers names, types, and structure. Expect 80%+ of functions named with high confidence.

**Medium** — Decompilation is usable but noisier. Expect partial recovery and lower confidence scores. Go and Rust binaries have more complex calling conventions and runtime patterns that reduce accuracy.

**Low** — Decompilation has significant gaps. Results will be incomplete, noisy, or partially unreadable. Kong will still produce output, but expect many low-confidence results.

## Scaling considerations

Binary size correlates with:

* **Function count** — more functions = longer analysis
* **LLM cost** — more functions = more API calls
* **Time to completion** — roughly linear with function count

However, larger binaries tend to produce **lower average confidence** — there are more utility functions, compiler-generated code, and edge cases that are harder to name accurately.

See [LLM Models & Pricing](/reference/llm-models-pricing) for cost estimates by binary size.