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Code enhancing benchmarks for OpenAI’s “1106” fashions

Code enhancing benchmarks for OpenAI’s “1106” fashions

2023-11-07 17:14:09

benchmark results

OpenAI just released new versions of GPT-3.5 and GPT-4,
and there’s rather a lot
of curiosity about their potential to code in comparison with the earlier variations.
With that in thoughts, I’ve been benchmarking the brand new fashions.

Aider is an open supply command line chat software that allows you to work with GPT to edit
code in your native git repo.
To do that, aider wants to have the ability to reliably acknowledge when GPT desires to edit
your supply code,
decide which information it desires to change
and precisely apply the modifications it’s attempting to make.
Doing an excellent job on this “code enhancing” job requires an excellent LLM, good prompting and
an excellent software driving the interactions with the LLM.

Aider depends on a
code editing benchmark
to quantitatively consider
each time one in every of these items modifications.
For instance,
each time I alter aider’s prompting or the backend which drives LLM conversations,
I run the benchmark to verify these modifications produce enhancements (not regressions).

The benchmark makes use of aider to try to full
133 Exercism Python coding exercises.
For every train, Exercism supplies a beginning python file with stubs for the wanted features,
a pure language description of the issue to resolve
and a take a look at suite to judge whether or not the coder has accurately solved the issue.

The benchmark offers aider two tries to finish the duty:

  1. On the primary strive, aider offers GPT the stub code file to edit and the pure language directions that describe the issue. This displays the way you code with aider. You add your supply code information to the chat and ask for modifications, that are routinely utilized.
  2. If the take a look at suite fails after the primary strive, aider offers GPT the take a look at error output and asks it to repair the code. Aider helps this kind of interplay utilizing a command like /run pytest to run and share pytest leads to the chat with GPT. You may /run no matter checks/linters/and so forth make sense to your language/framework/scenario.

Benchmark outcomes


For now, I’ve solely benchmarked the GPT-4 fashions utilizing the diff edit technique.
That is the edit format that aider makes use of by default with gpt-4.

  • The brand new gpt-4-1106-preview mannequin appears a lot sooner than the sooner GPT-4 fashions. I gained’t have the ability to correctly quantify this till the speed limits loosen up.
  • It appears higher at producing right code on the primary strive. It will get
    ~57% of the coding workout routines right, while not having to see errors from the take a look at suite. Earlier fashions solely get 46-47% of the workout routines right on the primary strive.
  • The brand new mannequin appears to carry out comparable
    (~66%) to the previous fashions (63-64%) after being given a second probability to right bugs by reviewing take a look at suite error output.

These are preliminary outcomes.
OpenAI is implementing very low
price limits on the brand new GPT-4 mannequin. The boundaries are so low, that
I’ve solely been capable of try
out of the 133 Exercism issues.
The issues are chosen in random order, so outcomes ought to be roughly
indicative of the total benchmark.


I benchmarked the GPT-3.5 fashions with each the entire and diff edit format.
Not one of the gpt-3.5 fashions appear capable of successfully use the diff edit format, together with the latest November (1106) mannequin.

See Also

The feedback under solely give attention to evaluating the entire edit format outcomes:

  • The brand new gpt-3.5-turbo-1106 mannequin is finishing the benchmark 3-4X sooner than the sooner GPT-3.5 fashions.
  • The success price after the primary strive of 42% is corresponding to the earlier June (0613) mannequin. The brand new November and former June fashions are each worse than the unique March (0301) mannequin’s 50% consequence on the primary strive.
  • The brand new mannequin’s 56% success price after the second strive appears corresponding to the unique March mannequin, and considerably higher than the June mannequin’s 50% rating.


I’ll replace the outcomes on this web page as rapidly as my price restrict permits.

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