Instruct HumanEval

Instruction-based variant of HumanEval benchmark for evaluating large language models' code generation capabilities with functional correctness using pass@k metric on programming problems

Llama 3.1 Nemotron 70B Instruct from NVIDIA currently leads the Instruct HumanEval leaderboard with a score of 0.738 across 1 evaluated AI models.

Paper

Progress Over Time

Interactive timeline showing model performance evolution on Instruct HumanEval

State-of-the-art frontier
Open
Proprietary

Instruct HumanEval Leaderboard

1 models
ContextCostLicense
170B
Notice missing or incorrect data?

FAQ

Common questions about Instruct HumanEval.

What is the Instruct HumanEval benchmark?

Instruction-based variant of HumanEval benchmark for evaluating large language models' code generation capabilities with functional correctness using pass@k metric on programming problems

What is the Instruct HumanEval leaderboard?

The Instruct HumanEval leaderboard ranks 1 AI models based on their performance on this benchmark. Currently, Llama 3.1 Nemotron 70B Instruct by NVIDIA leads with a score of 0.738. The average score across all models is 0.738.

What is the highest Instruct HumanEval score?

The highest Instruct HumanEval score is 0.738, achieved by Llama 3.1 Nemotron 70B Instruct from NVIDIA.

How many models are evaluated on Instruct HumanEval?

1 models have been evaluated on the Instruct HumanEval benchmark, with 0 verified results and 1 self-reported results.

Where can I find the Instruct HumanEval paper?

The Instruct HumanEval paper is available at https://arxiv.org/abs/2107.03374. The paper details the methodology, dataset construction, and evaluation criteria.

What categories does Instruct HumanEval cover?

Instruct HumanEval is categorized under general. The benchmark evaluates text models.

More evaluations to explore

Related benchmarks in the same category

View all general
GPQA

A challenging dataset of 448 multiple-choice questions written by domain experts in biology, physics, and chemistry. Questions are Google-proof and extremely difficult, with PhD experts reaching 65% accuracy.

general
214 models
MMLU-Pro

A more robust and challenging multi-task language understanding benchmark that extends MMLU by expanding multiple-choice options from 4 to 10, eliminating trivial questions, and focusing on reasoning-intensive tasks. Features over 12,000 curated questions across 14 domains and causes a 16-33% accuracy drop compared to original MMLU.

general
119 models
MMLU

Massive Multitask Language Understanding benchmark testing knowledge across 57 diverse subjects including STEM, humanities, social sciences, and professional domains

general
99 models
LiveCodeBench

LiveCodeBench is a holistic and contamination-free evaluation benchmark for large language models for code. It continuously collects new problems from programming contests (LeetCode, AtCoder, CodeForces) and evaluates four different scenarios: code generation, self-repair, code execution, and test output prediction. Problems are annotated with release dates to enable evaluation on unseen problems released after a model's training cutoff.

general
71 models
IFEval

Instruction-Following Evaluation (IFEval) benchmark for large language models, focusing on verifiable instructions with 25 types of instructions and around 500 prompts containing one or more verifiable constraints

general
63 models
MMMU

MMMU (Massive Multi-discipline Multimodal Understanding) is a benchmark designed to evaluate multimodal models on college-level subject knowledge and deliberate reasoning. Contains 11.5K meticulously collected multimodal questions from college exams, quizzes, and textbooks, covering six core disciplines: Art & Design, Business, Science, Health & Medicine, Humanities & Social Science, and Tech & Engineering across 30 subjects and 183 subfields.

generalmultimodal
62 models