--- title: "instruct-eval vs HLCE" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/declare-lab-instruct-eval-vs-humanity-s-last-code-exam-hlce" tools: ["declare-lab-instruct-eval", "humanity-s-last-code-exam-hlce"] --- # instruct-eval vs HLCE *GraphCanon updated Aug 8, 2026* ## Verdict Pick instruct-eval if key facts about instruct-eval; pick HLCE if hLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. [instruct-eval](https://declare-lab.github.io/instruct-eval/) reports 552 GitHub stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. [HLCE](https://humanity-s-last-code-exam.github.io/website/) has 96 stars, 8 forks, and 1 open issues, last pushed Aug 21, 2025. Figures are from public GitHub metadata via [instruct-eval's repository](https://github.com/declare-lab/instruct-eval) and [HLCE's repository](https://github.com/Humanity-s-Last-Code-Exam/HLCE). | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [HLCE](/tools/humanity-s-last-code-exam-hlce.md) | | --- | --- | --- | | Tagline | Quantitative evaluation for instruction-tuned language models | Source Evaluation scripts for Humanity's Last Code Exam | | Stars | 552 | 96 | | Forks | 45 | 8 | | Open issues | 24 | 1 | | Language | Python | Python | | Adopt for | Key facts about instruct-eval | HLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. | | Persona | - | - | | Runtime | - | - | | License | The tool is distributed under Apache-2.0 license | - | | Categories | Evaluation & Observability | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [HLCE](/tools/humanity-s-last-code-exam-hlce.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 879d | 352d | | Open issues (now) | 24 | 1 | | Full report | [trust report](/tools/declare-lab-instruct-eval/trust.md) | [trust report](/tools/humanity-s-last-code-exam-hlce/trust.md) | ## Decision facts: instruct-eval - **Requirements:** Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation. - **Adopt for:** Key facts about instruct-eval - **License detail:** The tool is distributed under Apache-2.0 license ## Decision facts: HLCE - **Adopt for:** HLCE offers evaluation scripts to assess code generation using LLMs, specifically for research purposes. ## Choose when ### Choose instruct-eval if… - Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.. - Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm. - When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ### 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 instruct-eval - When primarily interested in general model evaluation without a focus on instruction-tuned LMs. - If your primary interest lies in qualitative assessment rather than quantitative metrics. - If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ## 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. ## Common questions ### What is the difference between instruct-eval and HLCE? instruct-eval: Quantitative evaluation for instruction-tuned 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 instruct-eval over HLCE? Choose instruct-eval over HLCE when Requirements: Min 8 GB RAM; Requires Python environment setup and specific dependencies as outlined in the repository's documentation.; Tags unique to instruct-eval: benchmarking, evaluation, instruct-tuning, llm; When you need to quantitatively evaluate the performance of instruction-tuned large language models such as Alpaca and Flan-T5 on held-out tasks. ### When should I choose HLCE over instruct-eval? Choose HLCE over instruct-eval 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 instruct-eval? When primarily interested in general model evaluation without a focus on instruction-tuned LMs. If your primary interest lies in qualitative assessment rather than quantitative metrics. If you need support for non-HuggingFace Transformer models, as instruct-eval mainly supports models from the HuggingFace ecosystem. ### 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 instruct-eval or HLCE more popular on GitHub? instruct-eval has more GitHub stars (552 vs 96). Stars measure visibility, not whether either tool fits your constraints. ### Are instruct-eval and HLCE open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to instruct-eval or HLCE? GraphCanon lists graph-backed alternatives at [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) and [HLCE alternatives](/tools/humanity-s-last-code-exam-hlce/alternatives) ([instruct-eval markdown twin](/tools/declare-lab-instruct-eval/alternatives.md), [HLCE markdown twin](/tools/humanity-s-last-code-exam-hlce/alternatives.md)), 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](/compare/declare-lab-instruct-eval-vs-humanity-s-last-code-exam-hlce.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, instruct-eval or HLCE? instruct-eval: 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 instruct-eval and HLCE? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust); [HLCE trust report](/tools/humanity-s-last-code-exam-hlce/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=declare-lab-instruct-eval`](/api/graphcanon/graph?tool=declare-lab-instruct-eval) - LLM index: [/llms.txt](/llms.txt) - Full corpus: [/llms-full.txt](/llms-full.txt) _GraphCanon - The knowledge graph for AI development. https://www.graphcanon.com/_