Comparison
bigcode-evaluation-harness vs HLCE
Verdict
Pick bigcode-evaluation-harness if bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments; pick HLCE if hLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes.
Markdown twin · bigcode-evaluation-harness alternatives · HLCE alternatives
GraphCanon updated 2w
vs
Trust & integrity
| Signal | bigcode-evaluation-harness | HLCE |
|---|---|---|
| Maintenance | Dormant (378d since push) As of 2w · github_public_v1 | Slowing (352d since push) As of 2w · github_public_v1 |
| Provenance | Not a fork · Organization account As of 2w · github_public_v1 | Not a fork · Organization account As of 2w · github_public_v1 |
| OSV dependency advisories | Published findings As of 1mo · osv@v1 | Published findings As of 1mo · osv@v1 |
| deps.dev advisories | Not queried deps.dev@v1 | Not queried deps.dev@v1 |
| OpenSSF Scorecard | Not queried openssf-scorecard@v1 | Not queried openssf-scorecard@v1 |
Tagline
- bigcode-evaluation-harness
- A framework for evaluating autoregressive code generation language models.
- HLCE
- Source Evaluation scripts for Humanity's Last Code Exam
Stars
- bigcode-evaluation-harness
- 1.1k
- HLCE
- 96
Forks
- bigcode-evaluation-harness
- 261
- HLCE
- 8
Open issues
- bigcode-evaluation-harness
- 96
- HLCE
- 1
Language
- bigcode-evaluation-harness
- Python
- HLCE
- Python
Adopt for
- bigcode-evaluation-harness
- bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments.
- HLCE
- HLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes.
Persona
- bigcode-evaluation-harness
- -
- HLCE
- -
Runtime
- bigcode-evaluation-harness
- -
- HLCE
- -
License
- bigcode-evaluation-harness
- bigcode-evaluation-harness is distributed under the Apache-2.0 license.
- HLCE
- -
Last pushed
- bigcode-evaluation-harness
- Jul 22, 2025
- HLCE
- Aug 21, 2025
Categories
- bigcode-evaluation-harness
- Evaluation & Observability
- HLCE
- Evaluation & Observability, LLM Frameworks
Trust and health
Maintenance
- bigcode-evaluation-harness
- Dormant (18%)
- HLCE
- Slowing (36%)
Days since push
- bigcode-evaluation-harness
- 378d
- HLCE
- 352d
Open issues (now)
- bigcode-evaluation-harness
- 96
- HLCE
- 1
Full report
- bigcode-evaluation-harness
- Trust report
- HLCE
- Trust report
Choose bigcode-evaluation-harness if…
- Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation..
- Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python.
- bigcode-evaluation-harness ships Docker support for self-hosted deployment.
- When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments.
When NOT to use bigcode-evaluation-harness
- When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker.
- If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity.
Choose HLCE if…
- Tags unique to HLCE: benchmark, codegen, codellm, llm-evaluation.
- Also covers LLM Frameworks.
- When you are researching the capabilities of language models in generating code and need benchmarking tools that focus on this aspect exclusively.
When NOT to use HLCE
- If you require tools that cater to general-purpose evaluation beyond the scope of LLM code generation in a research context.
- When proprietary or non-research licenses are necessary, since HLCE does not detail its licensing beyond being for research purposes only.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (bigcode-project/bigcode-evaluation-harness) · observed Aug 5, 2026
- GitHub forks (bigcode-project/bigcode-evaluation-harness) · observed Aug 5, 2026
- Last push (bigcode-project/bigcode-evaluation-harness) · observed Jul 22, 2025
- License file (Apache-2.0) · observed Aug 5, 2026
- Decision facts (enrichment) · observed Jul 17, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (Humanity-s-Last-Code-Exam/HLCE) · observed Aug 8, 2026
- GitHub forks (Humanity-s-Last-Code-Exam/HLCE) · observed Aug 8, 2026
- Last push (Humanity-s-Last-Code-Exam/HLCE) · observed Aug 21, 2025
- License file (unknown) · observed Aug 8, 2026
- Decision facts (enrichment) · observed Jul 16, 2026
- Trust scan (lockfile / OSV) · observed Jul 15, 2026
GitHub stars on cards: bigcode-evaluation-harness 1.1k · HLCE 96 (synced Aug 5, 2026).
Common questions
- What is the difference between bigcode-evaluation-harness and HLCE?
- bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. HLCE: Source Evaluation scripts for Humanity's Last Code Exam. See the comparison table for live GitHub stats and shared categories.
- When should I choose bigcode-evaluation-harness over HLCE?
- Choose bigcode-evaluation-harness over HLCE when Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.; Tags unique to bigcode-evaluation-harness: autoregressive models, code generation, docker, python; bigcode-evaluation-harness ships Docker support for self-hosted deployment; When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments.
- When should I choose HLCE over bigcode-evaluation-harness?
- Choose HLCE over bigcode-evaluation-harness when Tags unique to HLCE: benchmark, codegen, codellm, llm-evaluation; Also covers LLM Frameworks; When you are researching the capabilities of language models in generating code and need benchmarking tools that focus on this aspect exclusively.
- When should I avoid bigcode-evaluation-harness?
- When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity.
- When should I avoid HLCE?
- If you require tools that cater to general-purpose evaluation beyond the scope of LLM code generation in a research context. When proprietary or non-research licenses are necessary, since HLCE does not detail its licensing beyond being for research purposes only.
- Is bigcode-evaluation-harness or HLCE more popular on GitHub?
- bigcode-evaluation-harness has more GitHub stars (1,055 vs 96). Stars measure visibility, not whether either tool fits your constraints.
- Are bigcode-evaluation-harness and HLCE open source?
- Yes - both are open-source projects on GitHub.
- Where can I find alternatives to bigcode-evaluation-harness or HLCE?
- GraphCanon lists graph-backed alternatives at bigcode-evaluation-harness alternatives and HLCE alternatives (bigcode-evaluation-harness markdown twin, HLCE markdown twin), ranked by typed relationship edges rather than popularity votes.
- Is there a machine-readable version of this comparison?
- Yes. The markdown twin at this comparison mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers.
- Which is better maintained, bigcode-evaluation-harness or HLCE?
- bigcode-evaluation-harness: Dormant. HLCE: Slowing. Compare maintenance labels, days since push, and release cadence in the trust section below - stars alone do not measure maintenance.
- Where are the full trust reports for bigcode-evaluation-harness and HLCE?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: bigcode-evaluation-harness trust report; HLCE trust report.