DeepSeek V4 Pro was the best (and most expensive) LLM I tested with Strix a few months ago. After the heartbreaking results of its smaller sibling, DeepSeek V4 Flash 0731, in my HTB-Challenger tests, I was curious to see how the Pro version would handle the new challenges.


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.


The results of DeepSeek V4 Pro in my testing were, well, not great and not terrible. On the one hand, it didn’t have the basic flaws I observed when testing DeepSeek V4 Flash 0731: it didn’t submit any incorrect flags, and it used the available tools without any issues. On the other hand, its final score was comparable to that of GPT-5.6 Luna, whose median cost per test was 17 times lower.

The performance gap between DeepSeek V4 Pro and the leaders of my benchmark was also quite large. I’m afraid that’s a sign of just how quickly things have progressed over the last few months. DeepSeek V4 Pro was released four months ago, which feels like an eternity now that we’re getting better and better models almost every month.


HTB-Challenger Benchmark LLM model card

Overall benchmark results

  • Number of challenges: 16
  • Number of solved challenges: 8
  • Number of false positives: 0
  • Runs where the model gave up: 0
  • Runs that reached the step or cost limit: 8
  • Runs where the model got stuck: 0
  • Benchmark score: 37.5%
Metric Per challenge (median) Total
Model steps 62.5 938
Model cost $0.35 $11.84
Duration 00:06:19 03:34:35
Number of input tokens 1.93M 39.64M
Number of output tokens 0.01M 0.35M
Number of read_file tool calls 1.5 55
Number of write_file tool calls 1.0 92
Number of execute_command tool calls 26.5 821
Number of web_search tool calls 0.0 102

Results by challenge difficulty

Very Easy challenges

  • Number of challenges: 4
  • Number of solved challenges: 3
  • Number of false positives: 0
  • Runs where the model gave up: 0
  • Runs that reached the step or cost limit: 1
  • Runs where the model got stuck: 0
  • Benchmark score: 75.0%
Metric Per challenge (median) Total
Model steps 24.5 158
Model cost $0.11 $1.19
Duration 00:03:51 00:27:35
Number of input tokens 0.28M 4.28M
Number of output tokens 0.01M 0.05M
Number of read_file tool calls 1.5 8
Number of write_file tool calls 0.0 7
Number of execute_command tool calls 15.5 149
Number of web_search tool calls 2.0 10

Easy challenges

  • Number of challenges: 4
  • Number of solved challenges: 3
  • Number of false positives: 0
  • Runs where the model gave up: 0
  • Runs that reached the step or cost limit: 1
  • Runs where the model got stuck: 0
  • Benchmark score: 75.0%
Metric Per challenge (median) Total
Model steps 21.5 155
Model cost $0.09 $2.09
Duration 00:04:05 00:49:04
Number of input tokens 0.22M 5.02M
Number of output tokens 0.01M 0.07M
Number of read_file tool calls 2.0 10
Number of write_file tool calls 2.0 41
Number of execute_command tool calls 15.5 79
Number of web_search tool calls 4.5 32

Medium challenges

  • Number of challenges: 4
  • Number of solved challenges: 2
  • Number of false positives: 0
  • Runs where the model gave up: 0
  • Runs that reached the step or cost limit: 2
  • Runs where the model got stuck: 0
  • Benchmark score: 50.0%
Metric Per challenge (median) Total
Model steps 56.5 225
Model cost $0.61 $3.78
Duration 00:09:55 01:11:26
Number of input tokens 2.44M 10.07M
Number of output tokens 0.02M 0.11M
Number of read_file tool calls 0.5 17
Number of write_file tool calls 0.5 5
Number of execute_command tool calls 52.5 231
Number of web_search tool calls 0.0 8

Hard challenges

  • Number of challenges: 4
  • Number of solved challenges: 0
  • Number of false positives: 0
  • Runs where the model gave up: 0
  • Runs that reached the step or cost limit: 4
  • Runs where the model got stuck: 0
  • Benchmark score: 0.0%
Metric Per challenge (median) Total
Model steps 100 400
Model cost $1.27 $4.78
Duration 00:16:12 01:06:30
Number of input tokens 5.25M 20.27M
Number of output tokens 0.02M 0.10M
Number of read_file tool calls 1.0 20
Number of write_file tool calls 4.0 39
Number of execute_command tool calls 93.5 362
Number of web_search tool calls 0.0 52