# Solving Hack The Box Challenges with GPT-5.6 Luna Pro

> Source: <https://theaq.blog/2026/08/10/solving-htb-challenges-with-openai-gpt-5.6-luna-pro.html>
> Published: 2026-08-10 16:51:46+00:00

# Solving Hack The Box Challenges with GPT-5.6 Luna Pro

What the heck is **GPT-5.6 Luna Pro**, I hear you asking. That’s a very good question! To answer it, let me quote its [description on OpenRouter.ai](https://openrouter.ai/openai/gpt-5.6-luna-pro): “* GPT-5.6 Luna Pro is the same underlying model as GPT-5.6 Luna, served with *”

`reasoning.mode`

set to `pro`

for higher-quality responses on complex tasks.Okay, so how much better, and how much more expensive, is it compared with **GPT-5.6 Luna**, I hear you asking now. And that’s exactly what I can tell you.

This blog post is part of a series of tests for the HTB-Challenger Benchmark. See the

[benchmark results page]for all results and the[benchmark methodology]to learn how the benchmark is calculated.

I tested **GPT-5.6 Luna** in my [previous post](/2026/08/10/solving-htb-challenges-with-openai-gpt-5.6-luna.html), so I have the numbers ready. In a nutshell:

**Luna Pro** is approximately five times more expensive than**Luna**. It may sound dramatic, but** Luna**is such a cheap model that** Luna Pro**is still very cheap.- In this benchmark run,
**Luna Pro** performed much better than**Luna**. If you look below at the results for the more difficult challenges, you can see that** Luna Pro**solved some Medium and Hard challenges, while** Luna**solved only one Medium challenge and no Hard challenges.

I believe the main use case for **Luna Pro** is if you are tied to the OpenAI ecosystem and need a model for offensive-security or CTF-style tasks. In my tests, **GPT-5.6 Terra** and **GPT-5.6 Sol** refused to respond to most offensive-security requests, so **GPT-5.6 Luna Pro** was the most useful OpenAI option for this kind of work.

## Overall benchmark results

**Number of challenges:** 16**Number of solved challenges:** 11**Number of false positives:** 0**Runs where the model gave up:** 4**Runs that reached the step or cost limit:** 1**Runs where the model got stuck:** 0**Benchmark score:** 55.0%

| Metric | Per challenge (median) | Total |
|---|---|---|
| Model steps | 21.5 | 554 |
| Model cost | $0.10 | $3.05 |
| Duration | 00:08:55 | 03:48:35 |
| Number of input tokens | 1.50M | 42.93M |
| Number of output tokens | 0.07M | 1.63M |
Number of `read_file` tool calls |
4.0 | 186 |
Number of `write_file` tool calls |
0.0 | 18 |
Number of `execute_command` tool calls |
36.5 | 865 |
Number of `web_search` tool calls |
0.0 | 3 |

## Results by challenge difficulty

### Very Easy challenges

**Number of challenges:** 4**Number of solved challenges:** 4**Number of false positives:** 0**Runs where the model gave up:** 0**Runs that reached the step or cost limit:** 0**Runs where the model got stuck:** 0**Benchmark score:** 100.0%

| Metric | Per challenge (median) | Total |
|---|---|---|
| Model steps | 6 | 38 |
| Model cost | $0.01 | $0.14 |
| Duration | 00:01:43 | 00:12:53 |
| Number of input tokens | 0.16M | 1.89M |
| Number of output tokens | 0.01M | 0.08M |
Number of `read_file` tool calls |
0.5 | 7 |
Number of `write_file` tool calls |
0.0 | 1 |
Number of `execute_command` tool calls |
6.0 | 73 |
Number of `web_search` tool calls |
0.0 | 0 |

### Easy challenges

**Number of challenges:** 4**Number of solved challenges:** 4**Number of false positives:** 0**Runs where the model gave up:** 0**Runs that reached the step or cost limit:** 0**Runs where the model got stuck:** 0**Benchmark score:** 100.0%

| Metric | Per challenge (median) | Total |
|---|---|---|
| Model steps | 21.5 | 80 |
| Model cost | $0.09 | $0.34 |
| Duration | 00:08:46 | 00:37:25 |
| Number of input tokens | 1.38M | 4.94M |
| Number of output tokens | 0.06M | 0.22M |
Number of `read_file` tool calls |
1.5 | 12 |
Number of `write_file` tool calls |
0.0 | 0 |
Number of `execute_command` tool calls |
26.5 | 99 |
Number of `web_search` tool calls |
0.5 | 3 |

### Medium challenges

**Number of challenges:** 4**Number of solved challenges:** 2**Number of false positives:** 0**Runs where the model gave up:** 2**Runs that reached the step or cost limit:** 0**Runs where the model got stuck:** 0**Benchmark score:** 50.0%

| Metric | Per challenge (median) | Total |
|---|---|---|
| Model steps | 33 | 175 |
| Model cost | $0.16 | $1.02 |
| Duration | 00:13:37 | 01:10:21 |
| Number of input tokens | 2.30M | 14.23M |
| Number of output tokens | 0.10M | 0.54M |
Number of `read_file` tool calls |
8.5 | 75 |
Number of `write_file` tool calls |
0.5 | 3 |
Number of `execute_command` tool calls |
47.5 | 265 |
Number of `web_search` tool calls |
0.0 | 0 |

### Hard challenges

**Number of challenges:** 4**Number of solved challenges:** 1**Number of false positives:** 0**Runs where the model gave up:** 2**Runs that reached the step or cost limit:** 1**Runs where the model got stuck:** 0**Benchmark score:** 25.0%

| Metric | Per challenge (median) | Total |
|---|---|---|
| Model steps | 73.5 | 261 |
| Model cost | $0.45 | $1.55 |
| Duration | 00:28:35 | 01:47:55 |
| Number of input tokens | 6.36M | 21.87M |
| Number of output tokens | 0.22M | 0.79M |
Number of `read_file` tool calls |
16.5 | 92 |
Number of `write_file` tool calls |
2.5 | 14 |
Number of `execute_command` tool calls |
121.0 | 428 |
Number of `web_search` tool calls |
0.0 | 0 |