aider/docs/leaderboards/index.md

# Aider's LLM leaderboards

Aider works best with LLMs which are good at *editing* code, not just good at writing
code.
Aider uses the system prompt to tell the LLM how to make edits to the existing code
in your local git repo.
Some LLMs are better than others at consistently following these instructions
to successfully edit code.

Aider uses two benchmarks
to measure an LLM's code editing ability:

- [Aider's code editing benchmark](/docs/benchmarks.html#the-benchmark) asks the LLM to edit python source files to complete 133 small coding exercises. This benchmark measures the LLM's coding ability, but also whether it can consistently emit code edits in the format specified in the system prompt.
- [Aider's refactoring benchmark](https://github.com/paul-gauthier/refactor-benchmark) asks the LLM to refactor 89 large methods from large python classes. This is a more challenging benchmark, which tests the model's ability to output long chunks of code without skipping sections or making mistakes. It was developed to provoke and measure [GPT-4 Turbo's "lazy coding" habit](/2023/12/21/unified-diffs.html).

The leaderboards below report the results from a number of popular LLMs,
to help users select which models to use with aider.
While [aider can connect to almost any LLM](/docs/llms.html)
it will work best with models that score well on the benchmarks.

## Code editing leaderboard

<table style="width: 90%; max-width: 800px; margin: auto; border-collapse: collapse; box-shadow: 0 2px 4px rgba(0,0,0,0.1); font-size: 14px;">
  <thead style="background-color: #f2f2f2;">
    <tr>
      <th style="padding: 8px; text-align: left;">Model</th>
      <th style="padding: 8px; text-align: center;">Percent correct</th>
      <th style="padding: 8px; text-align: left;">Command</th>
      <th style="padding: 8px; text-align: center;">Edit format</th>
    </tr>
  </thead>
  <tbody>
    {% assign edit_sorted = site.data.edit_leaderboard | sort: 'second' | reverse %}
    {% for row in edit_sorted %}
      <tr style="border-bottom: 1px solid #ddd;">
        <td style="padding: 8px;">{{ row.model }}</td>
        <td style="padding: 8px; text-align: center;">{{ row.second }}%</td>
        <td style="padding: 8px;"><code>{{ row.command }}</code></td>
        <td style="padding: 8px; text-align: center;">{{ row.format }}</td>
      </tr>
    {% endfor %}
  </tbody>
</table>

<canvas id="editChart" width="800" height="450" style="margin-top: 20px"></canvas>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<script>
  document.addEventListener('DOMContentLoaded', function () {
    var ctx = document.getElementById('editChart').getContext('2d');
    var leaderboardData = {
      labels: [],
      datasets: [{
        label: 'Percent correct on code editing tasks',
        data: [],
        backgroundColor: 'rgba(54, 162, 235, 0.2)',
        borderColor: 'rgba(54, 162, 235, 1)',
        borderWidth: 1
      }]
    };

    {% for row in edit_sorted %}
      leaderboardData.labels.push('{{ row.model }}');
      leaderboardData.datasets[0].data.push({{ row.second }});
    {% endfor %}

    var leaderboardChart = new Chart(ctx, {
      type: 'bar',
      data: leaderboardData,
      options: {
        scales: {
          yAxes: [{
            scaleLabel: {
              display: true,
            },
            ticks: {
              beginAtZero: true
            }
          }]
        }
      }
    });
  });
</script>

## Code refactoring leaderboard

The refactoring benchmark requires a large context window to
work with large source files.
Therefore, results are available for fewer models.

<table style="width: 90%; max-width: 800px; margin: auto; border-collapse: collapse; box-shadow: 0 2px 4px rgba(0,0,0,0.1); font-size: 14px;">
  <thead style="background-color: #f2f2f2;">
    <tr>
      <th style="padding: 8px; text-align: left;">Model</th>
      <th style="padding: 8px; text-align: center;">Percent correct</th>
      <th style="padding: 8px; text-align: left;">Command</th>
      <th style="padding: 8px; text-align: center;">Edit format</th>
    </tr>
  </thead>
  <tbody>
    {% assign refac_sorted = site.data.refactor_leaderboard | sort: 'first' | reverse %}
    {% for row in refac_sorted %}
      <tr style="border-bottom: 1px solid #ddd;">
        <td style="padding: 8px;">{{ row.model }}</td>
        <td style="padding: 8px; text-align: center;">{{ row.first }}%</td>
        <td style="padding: 8px;"><code>{{ row.command }}</code></td>
        <td style="padding: 8px; text-align: center;">{{ row.format }}</td>
      </tr>
    {% endfor %}
  </tbody>
</table>

<canvas id="refacChart" width="800" height="450" style="margin-top: 20px"></canvas>
<script src="https://cdn.jsdelivr.net/npm/chart.js"></script>
<script>
  document.addEventListener('DOMContentLoaded', function () {
    var ctx = document.getElementById('refacChart').getContext('2d');
    var leaderboardData = {
      labels: [],
      datasets: [{
        label: 'Percent correct on code refactoring tasks',
        data: [],
        backgroundColor: 'rgba(54, 162, 235, 0.2)',
        borderColor: 'rgba(54, 162, 235, 1)',
        borderWidth: 1
      }]
    };

    {% for row in refac_sorted %}
      leaderboardData.labels.push('{{ row.model }}');
      leaderboardData.datasets[0].data.push({{ row.first }});
    {% endfor %}

    var leaderboardChart = new Chart(ctx, {
      type: 'bar',
      data: leaderboardData,
      options: {
        scales: {
          yAxes: [{
            scaleLabel: {
              display: true,
            },
            ticks: {
              beginAtZero: true
            }
          }]
        }
      }
    });
  });
</script>



## Notes on the edit format

Aider uses different "edit formats" to collect code edits from different LLMs.
The "whole" format is the easiest for an LLM to use, but it uses a lot of tokens
and may limit how large a file can be edited.
Models which can use one of the diff formats are much more efficient,
using far fewer tokens.
Models that use a diff-like format are able to 
edit larger files with less cost and without hitting token limits.

Aider is configured to use the best edit format for the popular OpenAI and Anthropic models
and the [other models recommended on the LLM page](/docs/llms.html).
For lesser known models aider will default to using the "whole" editing format
since it is the easiest format for an LLM to use.

## Contributing benchmark results

Contributions of benchmark results are welcome!
See the
[benchmark README](https://github.com/paul-gauthier/aider/blob/main/benchmark/README.md)
for information on running aider's code editing benchmark.
Submit results by opening a PR with edits to the
[benchmark results CSV data files](https://github.com/paul-gauthier/aider/blob/main/_data/).