BigCodeBench Leaderboard

What this benchmark measures

• practical code generation
• library and function-call usage
• intent understanding

Relevant to:

• coding assistant selection
• realistic programming task screening

Failure modes it exercises:

• failure on practical multi-library programming tasks

Method and its limits

1,140 practical programming tasks, with a roughly 150-task hard subset, evaluated in a Complete variant from a structured docstring and an Instruct variant from brief natural-language instructions, ranked by calibrated Pass@1 under greedy decoding.

• Complete and Instruct measure different things and are not interchangeable: one tests coding from a specification, the other tests understanding intent.
• The official page does not state how many libraries or function calls are covered, so the breadth claim is not established from it.

Dataset

Dataset

BigCodeBench

Type

practical programming tasks with executable tests

Freshness

aging

Benchmark release

2024-06-22

1,140 tasks in the full set and roughly 150 in the hard set, as registered on the official leaderboard. The official page carries an ICLR'25 listing but states no dataset release date; the freshness date recorded here is the primary paper's publication date. The primary paper's abstract establishes the composition: 1,140 fine-grained tasks drawing on 139 libraries across 7 domains, with 5.6 test cases per task.

How to read this score

The hard subset separates models far better than the full set, and Complete and Instruct results must be read separately rather than averaged.

Similarity to real tasks: High — Established from the official source: BigCodeBench evaluates models on practical programming tasks across a 1,140-task full set and a roughly 150-task hard set described as more user-facing, with a Complete variant driven by a structured docstring and an Instruct variant driven by brief natural-language instructions. Writing code from a short instruction against real libraries is close to assistant work. It remains task-scoped rather than repository-scoped.

Data contamination risk: Medium — Tasks and tests are public. The benchmark is newer than the saturated function-level sets, so exposure is lower, but no run-specific overlap audit is registered.

Benchmark gaming risk: Medium — A prominent public leaderboard creates optimisation pressure, and calibrated Pass@1 under greedy decoding is sensitive to prefilling and chat-formatting choices, which the leaderboard marks per entry.

What you still need to test yourself

• Test against your own dependency set and runtime.
• Decide whether Complete or Instruct matches how your users actually prompt.

This benchmark supports decisions about:

• Compare assistants on practical tasks rather than puzzle-style synthesis.
• Use the hard subset where the full set has compressed.

Limitations

• Task-scoped, not repository-scoped.
• Complete and Instruct figures are not interchangeable.

Official leaderboard reviewed 2026-10-12. The leaderboard is continuously updated and states no dataset release date; the freshness date recorded here is the publication date of the primary paper attached 2026-10-25.

What this evidence does not cover

• Only one model is currently scored on this page, so no comparison is supported.
• The average number of function calls a task requires is not established; the primary paper states test cases per task rather than call depth.
• The registered score is the Full subset of the Instruct split; the Hard subset of the same split is 27.0 for that model and is not registered here.

Sources

• BigCodeBench leaderboard BigCode · accessed 2026-10-12Supports the 1,140-task full set, the roughly 150-task hard set, the Complete and Instruct variants, and calibrated Pass@1 under greedy decoding as the published ranking; this is a continuously updated leaderboard reviewed at the access date, it does not state library coverage, and it does not support any model score.
• BigCodeBench: Benchmarking Code Generation with Diverse Function Calls and Complex Instructions Zhuo et al. · accessed 2026-10-25The primary methodology paper, establishing the benchmark's task construction and release date. It does not establish any model's score.

How this benchmark is scored

Primary source: https://bigcode-bench.github.io/

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