Code Interpreter Benchmark Math
Models use a Python interpreter to solve math problems. The score is execution accuracy on the Math task set.
One track of Code Interpreter Benchmark. The Code Interpreter Benchmark record keeps lab scores that combine tracks.
- Made by
- Qwen
- Released
- September 2023
- Kind
- Function calling
- Level
- School
- Scored
- Checked automatically
- Answers
- Agent task
- Tasks
- All public
- Strongest model tested
-
Qwen-14B #213 on the capability index
- Last in a launch post
- February 2024, dropped before it saturated
- Last model added
- February 2024
- Scores refreshed
- November 12th, 2026
Qwen1.5-72B leads with 73.1%
Best score per model as labs report them in their own cards. Labs pick their own settings, so these compare less cleanly than independent runs.
Qwen-14B leads with 58.4% across all independent runs
Best score per model across the official leaderboard, Epoch AI’s own runs and any board Epoch AI copied. Each bar and row names its source.
Qwen1.5-72B leads with 73.1%, claims included
Best score per model from every source, including labs’ own numbers and claims Epoch AI collected from reports and papers. Labs pick their own settings, so a claim can beat an independent run of the same model.
- 1
Qwen1.5-72B 73.1%
- 2
Qwen-14B 58.4%
Source: Labs’ model cards
- 1
Qwen-14B version 20231206 58.4%
- 2 ChatGLM3-6B version 20231206 54.2%
Source: Official Code Interpreter Benchmark Math leaderboard
- 1
Qwen1.5-72B 73.1%
- 2
Qwen-14B version 20231206 58.4%
- 3 ChatGLM3-6B version 20231206 54.2%
Sources: Labs’ model cards and official Code Interpreter Benchmark Math leaderboard
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 73.1% | Feb 2024 | Lab model card | |
| 2 | | 58.4% | Sep 2023 | Lab model card |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 58.4% | version 20231206 | Sep 2023 | Official leaderboard |
| 2 | ChatGLM3-6B | 54.2% | version 20231206 | Oct 2023 | Official leaderboard |
| # | Model | Score | Setting | Date | Source |
|---|---|---|---|---|---|
| 1 | | 73.1% | Feb 2024 | Lab model card | |
| 2 | | 58.4% | version 20231206 | Sep 2023 | Official leaderboard |
| 3 | ChatGLM3-6B | 54.2% | version 20231206 | Oct 2023 | Official leaderboard |
The benchmark's own leaderboard has 10 entries, 2 of them models we track. Switch the scores above to rank them together.
Scores over time
All the scores we have, one dot each, so a model tested at several settings appears more than once.
Sources: Official Code Interpreter Benchmark Math leaderboard and labs’ model cards
No launch post has listed it since February 2024
Launch posts from every lab that has listed it, one tick per post. Tall colored ticks include it, and short gray ones leave it out.
| Date | Launch post | Score | As the post writes it |
|---|---|---|---|
| Feb 5, 2024 | 73.1 | Code Interpreter Benchmark (Accuracy of Code Execution Results, Math) | |
| Sep 25, 2023 | 58.4 | Code Interpreter benchmark, Accuracy of Code Execution Results (Math) |
One lab claim next to the leaderboard
Each score a lab reported for its own model, beside the same model's best score on the leaderboard. Gaps mostly come from different harnesses, effort levels and task subsets. 1 of them match, which usually means one side copied the other. Every benchmark.
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