GraphCanon updated Oct 8, 2026 · GitHub synced Oct 8, 2026
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Decision brief
APPS offers a benchmark to evaluate the competence of large language models on coding challenges using its datasets.
Good fit when
- When you need benchmarking datasets specifically tailored for assessing the performance of your AI in solving programming tasks
- For training and evaluating the efficacy of large language models like GPT-2 or GPT-Neo on code generation
Avoid when
- If you solely require general datasets without a focus on coding challenges
- When your use case does not involve using Python-based tools for developing machine learning applications that include program synthesis and code generation
Observed Jul 17, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Dormant (840d since push)
- As of Oct 8, 2026
- Provenance
- Not a fork · Personal account
- As of Oct 8, 2026
- Security (OSV)
- 77 low (77 low)
- As of Jul 11, 2026
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Install
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Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A benchmark for measuring coding challenge competence with datasets and code for training and evaluation using large language models.
Capability facts
- Languages
- python
Source: github.language · Oct 8, 2026
Categories
Tags
README
Measuring Coding Challenge Competence With APPS This is the repository for Measuring Coding Challenge Competence With APPS by Dan Hendrycks\ , Steven Basart\ , Saurav Kadavath, Mantas Mazeika, Akul Arora, Ethan Guo, Collin Burns, Samir Puranik, Horace He, Dawn Song, and Jacob Ste...
For agents
This page has a .md twin and JSON over the API.