BigCodeBench

BigCodeBench

Quick answer: BigCodeBench (Zhuo et al., June 2024) challenges LLMs to generate correct Python solutions for 1,140 fine-grained programming tasks that require invoking multiple functions from 139 real-world libraries across 7 domains. Its two evaluation variants — Complete (code completion from docstrings) and Instruct (natural language instructions) — measure both completion ability and instruction-following quality, making it one of the most practically-oriented coding benchmarks.

At a Glance

What it tests: Multi-library Python code generation covering data processing, numerical computing, web APIs, OS operations, cryptography, NLP, and visualization tasks.

Why it matters: Unlike HumanEval or MBPP which use toy problems, BigCodeBench requires composing real library calls in realistic scenarios, exposing weaknesses in models that excel on simple algorithmic tasks but struggle with practical software engineering.

Known limitations: Evaluation requires executing the generated code in a sandboxed environment, which can be slow; also, the leaderboard has limited coverage of post-2025 frontier models.

What BigCodeBench Measures

BigCodeBench constructs each task around a practical programming scenario that requires calling at least one function from a real library (e.g., pandas, numpy, requests, cryptography). Tasks are drawn from 7 domains: data processing, numerical computing, network/web, OS operations, cryptography, NLP, and data visualization. For each task, a model must generate code that passes a suite of unit tests — not just produce syntactically valid output.

The benchmark has two variants: Complete evaluates code completion from structured docstrings (testing if models are good at coding given full specifications), while Instruct evaluates code generation from brief natural language instructions (a "vibe check" for whether models can translate human intent into working code). The Instruct variant is generally considered more representative of real-world use.

A subset of approximately 150 "Hard Set" tasks represents the most challenging and user-facing problems; models score significantly lower on this subset than on the full 1,140-task set.

Benchmark Specifications

How BigCodeBench Is Scored

BigCodeBench uses calibrated Pass@1 — the fraction of tasks where the model's greedy-decoded output passes all unit tests. A "calibrated" score accounts for the proportion of trivially-correct solutions to remove luck-based passes. Scores are expressed as percentages or fractions (0–1). The typical range for frontier models is 25–36% on the Instruct variant, highlighting the genuine difficulty of multi-library programming tasks.

State-of-the-Art Results

FAQ

What is BigCodeBench? BigCodeBench is a coding benchmark with 1,140 Python programming tasks that require multi-library function calls from 139 real-world libraries across 7 domains, evaluating practical code generation beyond simple algorithmic problems.

What is the difference between BigCodeBench Complete and Instruct? Complete evaluates code completion given structured long docstrings; Instruct evaluates code generation from brief natural language descriptions. The Instruct variant is generally harder and more representative of real-world coding assistant use cases.

What is a good BigCodeBench score? Current frontier models score 30–36% on the Instruct variant, reflecting the genuine difficulty of multi-library programming. The hard subset is even more challenging with typical scores 10–20% lower.