BigCodeBench is a 1,140-task Python benchmark built to stress multi-library, multi-function code generation instead of the single-function toy problems HumanEval made famous — and as of late 2026, independent trackers can’t even agree on who’s winning it.
What BigCodeBench actually tests
Released by a BigCode-affiliated academic team (University of Illinois Urbana-Champaign and collaborators), BigCodeBench was designed as a direct response to a known weakness in HumanEval and MBPP: both test short, self-contained functions that rarely resemble how professional engineers actually write code. BigCodeBench instead asks models to compose solutions that call multiple standard and third-party Python libraries per task, drawn from a pool of 139 libraries spanning data analysis, visualization, networking, cryptography, and system operations.
Each of the 1,140 tasks comes with a natural-language instruction, a reference solution, and a battery of unit tests (branch coverage averaging well above what HumanEval’s simpler checks require). The benchmark ships two evaluation modes: Complete, where the model finishes a partially written function given full docstring context, and Instruct, where it must write the function from a natural-language instruction alone — a harder, more realistic setting that strips away the scaffolding Complete mode provides. A Hard subset of roughly 148 tasks, selected by the benchmark authors for structural complexity, longer function signatures, and denser library usage, is reported separately because full-set scores for frontier models have started to cluster near the top, reducing the full set’s ability to discriminate between strong models.
This two-mode, two-split design is itself a response to a pattern seen across the benchmark landscape: once a benchmark saturates (see our coverage of MMLU’s saturation and replacement by MMLU-Pro), the community either retires it or carves out a harder subset to keep discriminating between top models. BigCodeBench’s Hard split is the coding-benchmark version of that pattern.
The leaderboard disagreement problem
This is where BigCodeBench becomes a useful case study in benchmark reporting, not just model capability. Pulling scores from multiple third-party trackers in late 2026 produces results that don’t reconcile with one another:
- LM Market Cap lists Claude Opus 4.6 at 72.1% on the Hard subset, aggregating from HuggingFace- and LMArena-linked sources, with no clear date stamp on the page itself.
- BenchLM (updated July 29, 2026) lists only two scored models, with DeepSeek V4 Pro Base leading at 59.2% and DeepSeek V4 Flash Base at 56.8% — both base-model, not instruction-tuned, scores.
- AnotherWrapper (dated August 30, 2026) names Gemini Diffusion as the top model at 45.4%, again with only two models scored on the page.
- Older benchmarklist.com imports, dated May 5, 2026, show top Hard-subset scores around 32% for models several generations behind today’s frontier.
None of these trackers agree on the leading model, and the gap between the lowest (32%) and highest (72.1%) reported “top score” is wide enough that the numbers are clearly not measuring the same thing. The likely explanation is a mix of split (full set vs. Hard subset), mode (Complete vs. Instruct), and model variant (base vs. instruction-tuned) — and most third-party trackers don’t disclose which combination they ran, or when their underlying scrape was last refreshed. That disagreement is itself the finding: a benchmark name alone, without split, mode, and date attached, tells you almost nothing reliable about relative model capability.
We could not locate an actively maintained, officially-hosted leaderboard update from the BigCode team itself newer than the benchmark’s original 2024 publication and subsequent incremental updates, which compounds the problem: third-party aggregators are filling a gap the benchmark’s own maintainers haven’t kept current, and each aggregator fills it differently.
Why BigCodeBench matters anyway
Despite the reporting mess, BigCodeBench’s underlying design addressed a real gap in how coding capability gets measured. HumanEval’s 164 hand-written problems became so heavily represented in training corpora (see our HumanEval contamination case study) that near-saturation scores stopped being informative about anything except how well a model had memorized the test set. BigCodeBench’s broader library coverage and longer, compositional tasks make rote memorization considerably less effective, and its calling-convention diversity — multiple imports, multiple function calls, real argument passing across libraries — is closer to how code actually gets written in production than a single self-contained function body.
That said, BigCodeBench is not immune to the same long-run saturation risk every static benchmark eventually faces. A fixed set of 1,140 tasks, published openly with reference solutions, is exactly the kind of artifact that ends up scraped into future pretraining corpora. That’s why cross-referencing it against rolling, contamination-resistant evaluations like LiveCodeBench, which continuously adds problems dated after each model’s training cutoff, and community-run sets like Aider Polyglot, which vendors have less incentive and ability to directly optimize against, remains the more reliable way to track real coding capability over time. For a broader framework on how much weight to put on any single benchmark claim, see our guide on how to evaluate LLMs.
It’s also worth distinguishing what BigCodeBench does and doesn’t tell you. It measures function-level code generation correctness against unit tests — not whether a model can navigate an existing multi-file codebase, resolve a real GitHub issue, or operate inside an agentic loop with tools. For that kind of evaluation, benchmarks like SWE-bench and the broader agentic coding benchmark landscape are the more relevant comparison points.
Adoption-wise, BigCodeBench has become a standard secondary citation in model release reports and technical cards alongside HumanEval and MBPP, even without a single canonical, continuously-updated public leaderboard. That pattern — a benchmark cited constantly in papers and model cards, but tracked inconsistently by the third-party sites most practitioners actually check for comparisons — is common enough in the coding-benchmark space that it is worth treating as a standing caveat rather than a one-off problem specific to this benchmark.
Reported scores by tracker (use with caution)
| Tracker | Reported leader | Score | Split / mode | As of |
|---|---|---|---|---|
| LM Market Cap | Claude Opus 4.6 | 72.1% | Hard subset | undated |
| BenchLM | DeepSeek V4 Pro Base | 59.2% | Full set, base model | Jul 29, 2026 |
| AnotherWrapper | Gemini Diffusion | 45.4% | Unspecified | Aug 30, 2026 |
| benchmarklist.com (import) | Earlier-generation model | ~32% | Hard subset | Imported May 5, 2026 |
FAQ
Is BigCodeBench harder than HumanEval?
Yes, by design. HumanEval’s 164 tasks are short, single-function problems with minimal library usage; BigCodeBench’s 1,140 tasks require composing multiple library calls per solution across 139 libraries, and its Hard subset specifically selects for longer, more structurally complex instructions.
Why do BigCodeBench leaderboards disagree so much?
Third-party trackers rarely specify whether they ran the full set or Hard subset, Complete or Instruct mode, and base or instruction-tuned model variants. Mixing these produces scores that aren’t comparable, even when every tracker cites the same benchmark name and claims to be current.
Should I trust a single BigCodeBench score when comparing models?
Not on its own. Check the split, mode, and date behind the number, and cross-reference against at least one other coding benchmark — LiveCodeBench for contamination resistance, or Aider Polyglot for a community-run alternative — before drawing conclusions about relative capability.
Last updated 2026-11-13. This page is refreshed as benchmarks and scores move.