Skip to the content.

Aider LLM Leaderboards

Aider works best with LLMs which are good at editing code, not just good at writing code. To evaluate an LLM’s editing skill, aider uses a pair of benchmarks that assess a model’s ability to consistently follow the system prompt to successfully edit code.

The leaderboards below report the results from a number of popular LLMs. While aider can connect to almost any LLM, it works best with models that score well on the benchmarks.

GPT-4o takes the #1 & #2 spots

GPT-4o tops the aider LLM code editing leaderboard at 72.9%, versus 68.4% for Opus. GPT-4o takes second on aider’s refactoring leaderboard with 62.9%, versus Opus at 72.3%.

GPT-4o did much better than the 4-turbo models, and seems much less lazy.

GPT-4o is also able to use aider’s established “diff” edit format that uses SEARCH/REPLACE blocks. This diff format is used by all the other capable models, including Opus and the original GPT-4 models The GPT-4 Turbo models have all required the “udiff” edit format, due to their tendancy to lazy coding.

Code editing leaderboard

Aider’s code editing 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.

Model Percent completed correctly Percent using correct edit format Command Edit format
gpt-4o 72.9% 96.2% aider diff
claude-3-opus-20240229 68.4% 100.0% aider --opus diff
gpt-4-0613 67.7% 100.0% aider -4 diff
gpt-4-0314 66.2% 93.2% aider --model gpt-4-0314 diff
gpt-4-0125-preview 66.2% 97.7% aider --model gpt-4-0125-preview udiff
gpt-4-1106-preview 65.4% 92.5% aider --model gpt-4-1106-preview udiff
gpt-4-turbo-2024-04-09 (udiff) 63.9% 97.0% aider --gpt-4-turbo udiff
deepseek-chat v2 (diff) 60.9% 97.0% aider --model deepseek/deepseek-chat diff
deepseek-chat v2 (whole) 60.2% 100.0% aider --model deepseek/deepseek-chat --edit-format whole whole
gpt-3.5-turbo-0301 57.9% 100.0% aider --model gpt-3.5-turbo-0301 whole
gpt-4-turbo-2024-04-09 (diff) 57.6% 100.0% aider --model gpt-4-turbo-2024-04-09 diff
gemini-1.5-pro-latest 57.1% 87.2% aider --model gemini/gemini-1.5-pro-latest diff-fenced
gpt-3.5-turbo-1106 56.1% 100.0% aider --model gpt-3.5-turbo-1106 whole
claude-3-sonnet-20240229 54.9% 100.0% aider --sonnet whole
deepseek-coder 54.5% 100.0% aider --model deepseek/deepseek-coder whole
gpt-3.5-turbo-0613 50.4% 100.0% aider --model gpt-3.5-turbo-0613 whole
gpt-3.5-turbo-0125 50.4% 100.0% aider -3 whole
llama3-70b-8192 49.2% 73.5% aider --model groq/llama3-70b-8192 diff
WizardLM-2 8x22B 44.4% 100.0% aider --model openrouter/microsoft/wizardlm-2-8x22b whole
qwen1.5-110b-chat 37.6% 100.0% aider --model together_ai/qwen/qwen1.5-110b-chat whole
command-r-plus 31.6% 100.0% aider --model command-r-plus whole

Code refactoring leaderboard

Aider’s refactoring 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.

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

Model Percent completed correctly Percent using correct edit format Command Edit format
claude-3-opus-20240229 72.3% 79.5% aider --opus diff
gpt-4o 62.9% 53.9% aider diff
gpt-4-1106-preview 50.6% 39.3% aider --model gpt-4-1106-preview udiff
gemini/gemini-1.5-pro-latest 49.4% 7.9% aider --model gemini/gemini-1.5-pro-latest diff-fenced
gpt-4-turbo-2024-04-09 (udiff) 34.1% 30.7% aider --gpt-4-turbo udiff
gpt-4-0125-preview 33.7% 47.2% aider --model gpt-4-0125-preview udiff
gpt-4-turbo-2024-04-09 (diff) 21.4% 6.8% aider --model gpt-4-turbo-2024-04-09 diff

LLM code editing skill by model release date

connecting to many LLMs

Notes on benchmarking results

The key benchmarking results are:

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. 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 for information on running aider’s code editing benchmarks. Submit results by opening a PR with edits to the benchmark results data files.