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: “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.


HTB-Challenger Benchmark

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, 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.


HTB-Challenger Benchmark LLM model card

Overall benchmark results

  • Number of challenges: 16
  • Number of solved challenges: 11
  • Incorrect flag submissions: 0
  • Runs where the model gave up: 4
  • Runs that reached the step or cost limit: 1
  • 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
  • Incorrect flag submissions: 0
  • Runs where the model gave up: 0
  • Runs that reached the step or cost limit: 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
  • Incorrect flag submissions: 0
  • Runs where the model gave up: 0
  • Runs that reached the step or cost limit: 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
  • Incorrect flag submissions: 0
  • Runs where the model gave up: 2
  • Runs that reached the step or cost limit: 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
  • Incorrect flag submissions: 0
  • Runs where the model gave up: 2
  • Runs that reached the step or cost limit: 1
  • 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