Solving Hack The Box Challenges with GPT-5.6 Luna Pro

OpenAI's GPT-5.6 Luna Pro solved 11 of 16 Hack The Box challenges, scoring 55.0% on the HTB-Challenger Benchmark, compared with GPT-5.6 Luna which solved only one Medium and no Hard challenges. The model, served via OpenRouter.ai with reasoning.mode set to 'pro', is approximately five times more expensive than Luna but remains cheap, with a median cost of $0.10 per challenge and total cost of $3.05. It solved all Very Easy and Easy challenges, 2 of 4 Medium, and 1 of 4 Hard, with no false positives.

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 |