--- title: "instruct-eval vs hallucination-index" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/declare-lab-instruct-eval-vs-rungalileo-hallucination-index" tools: ["declare-lab-instruct-eval", "rungalileo-hallucination-index"] --- # instruct-eval vs hallucination-index *GraphCanon updated Aug 7, 2026* ## Verdict Pick instruct-eval if key facts about instruct-eval; pick hallucination-index if hallucination-Index helps users identify LLMs with the lowest propensity for factual errors across varying context lengths and source types. [instruct-eval](https://declare-lab.github.io/instruct-eval/) reports 552 GitHub stars, 45 forks, and 24 open issues, last pushed Mar 10, 2024. [hallucination-index](https://www.rungalileo.io/hallucinationindex) has 116 stars, 8 forks, and 1 open issues, last pushed Jul 28, 2025. Figures are from public GitHub metadata via [instruct-eval's repository](https://github.com/declare-lab/instruct-eval) and [hallucination-index's repository](https://github.com/rungalileo/hallucination-index). | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [hallucination-index](/tools/rungalileo-hallucination-index.md) | | --- | --- | --- | | Tagline | Quantitative evaluation for instruction-tuned language models | Initiative to evaluate and rank popular LLMs based on hallucination propensity | | Stars | 552 | 116 | | Forks | 45 | 8 | | Open issues | 24 | 1 | | Language | Python | - | | Adopt for | Key facts about instruct-eval | Hallucination-Index helps users identify LLMs with the lowest propensity for factual errors across varying context lengths and source types. | | Persona | - | - | | Runtime | - | - | | License | The tool is distributed under Apache-2.0 license | - | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [instruct-eval](/tools/declare-lab-instruct-eval.md) | [hallucination-index](/tools/rungalileo-hallucination-index.md) | | --- | --- | --- | | Days since push | 879d | 365d | | Open issues (now) | 24 | 1 | | Full report | [trust report](/tools/declare-lab-instruct-eval/trust.md) | [trust report](/tools/rungalileo-hallucination-index/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: hallucination-index - **Adopt for:** Hallucination-Index helps users identify LLMs with the lowest propensity for factual errors across varying context lengths and source types. ## 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 hallucination-index if… - Tags unique to hallucination-index: hallucinations, large language models, llm-evaluation, openai. - Use when you need to ensure accuracy in short-context tasks, as it tests models like Chain-of-Note prompting techniques specifically for such scenarios. - More recently updated (last pushed Jul 28, 2025). ## 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 hallucination-index - Avoid using Hallucination-Index when your application requires real-time evaluation of hallucinations, as it focuses on predefined tests rather than live model performance. - Do not rely solely on this index if your primary concern is the latest updates to LLM models; its data might not reflect recent improvements in models or the introduction of new ones. ## Common questions ### What is the difference between instruct-eval and hallucination-index? instruct-eval: Quantitative evaluation for instruction-tuned language models. hallucination-index: Initiative to evaluate and rank popular LLMs based on hallucination propensity. See the comparison table for live GitHub stats and shared categories. ### When should I choose instruct-eval over hallucination-index? Choose instruct-eval over hallucination-index 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 hallucination-index over instruct-eval? Choose hallucination-index over instruct-eval when Tags unique to hallucination-index: hallucinations, large language models, llm-evaluation, openai; Use when you need to ensure accuracy in short-context tasks, as it tests models like Chain-of-Note prompting techniques specifically for such scenarios; More recently updated (last pushed Jul 28, 2025). ### 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 hallucination-index? Avoid using Hallucination-Index when your application requires real-time evaluation of hallucinations, as it focuses on predefined tests rather than live model performance. Do not rely solely on this index if your primary concern is the latest updates to LLM models; its data might not reflect recent improvements in models or the introduction of new ones. ### Is instruct-eval or hallucination-index more popular on GitHub? instruct-eval has more GitHub stars (552 vs 116). Stars measure visibility, not whether either tool fits your constraints. ### Are instruct-eval and hallucination-index open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to instruct-eval or hallucination-index? GraphCanon lists graph-backed alternatives at [instruct-eval alternatives](/tools/declare-lab-instruct-eval/alternatives) and [hallucination-index alternatives](/tools/rungalileo-hallucination-index/alternatives) ([instruct-eval markdown twin](/tools/declare-lab-instruct-eval/alternatives.md), [hallucination-index markdown twin](/tools/rungalileo-hallucination-index/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-rungalileo-hallucination-index.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, instruct-eval or hallucination-index? instruct-eval: Dormant. hallucination-index: Dormant. 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 hallucination-index? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [instruct-eval trust report](/tools/declare-lab-instruct-eval/trust); [hallucination-index trust report](/tools/rungalileo-hallucination-index/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/_