--- title: "deepeval vs Open-LLM-Leaderboard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/confident-ai-deepeval-vs-vila-lab-open-llm-leaderboard" tools: ["confident-ai-deepeval", "vila-lab-open-llm-leaderboard"] --- # deepeval vs Open-LLM-Leaderboard *GraphCanon updated Sep 20, 2026* ## Verdict Pick deepeval if deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies; pick Open-LLM-Leaderboard if open-LLM-Leaderboard evaluates large language models on open-style questions using a GPT-4-based evaluator and aggregates results in an accessible leaderboard format. [deepeval](https://deepeval.com) reports 18k GitHub stars, 2.0k forks, and 624 open issues, last pushed Sep 18, 2026. [Open-LLM-Leaderboard](https://huggingface.co/spaces/Open-Style/OSQ-Leaderboard) has 53 stars, 7 forks, and 1 open issues, last pushed Jun 27, 2024. Figures are from public GitHub metadata via [deepeval's repository](https://github.com/confident-ai/deepeval) and [Open-LLM-Leaderboard's repository](https://github.com/VILA-Lab/Open-LLM-Leaderboard). | | [deepeval](/tools/confident-ai-deepeval.md) | [Open-LLM-Leaderboard](/tools/vila-lab-open-llm-leaderboard.md) | | --- | --- | --- | | Tagline | LLM Evaluation Framework. | Tracks LLM performance on open-style questions | | Stars | 18,342 | 53 | | Forks | 1,953 | 7 | | Open issues | 624 | 1 | | Language | Python | Python | | Adopt for | Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. | Open-LLM-Leaderboard evaluates large language models on open-style questions using a GPT-4-based evaluator and aggregates results in an accessible leaderboard format. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 License | CC-BY-4.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [deepeval](/tools/confident-ai-deepeval.md) | [Open-LLM-Leaderboard](/tools/vila-lab-open-llm-leaderboard.md) | | --- | --- | --- | | Maintenance | Very active (96%) | Dormant (18%) | | Days since push | 1d | 804d | | Open issues (now) | 624 | 1 | | Stars delta | +1.1k (30d) | 0 (30d) | | Open issues delta | +220 (30d) | 0 (30d) | | Full report | [trust report](/tools/confident-ai-deepeval/trust.md) | [trust report](/tools/vila-lab-open-llm-leaderboard/trust.md) | ## Shared compatibility - **Python**: [deepeval](/tools/confident-ai-deepeval.md) - Python runtime; [Open-LLM-Leaderboard](/tools/vila-lab-open-llm-leaderboard.md) - Python runtime ## Decision facts: deepeval - **Requirements:** Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities. - **Adopt for:** Deepeval is a Python-based framework designed for evaluating large language models with an array of metrics and evaluation methodologies. - **License detail:** Apache-2.0 License ## Decision facts: Open-LLM-Leaderboard - **Adopt for:** Open-LLM-Leaderboard evaluates large language models on open-style questions using a GPT-4-based evaluator and aggregates results in an accessible leaderboard format. ## Choose when ### Choose deepeval if… - License: deepeval is Apache-2.0, Open-LLM-Leaderboard is CC-BY-4.0. - Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.. - Tags unique to deepeval: evaluation, metrics. - When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### Choose Open-LLM-Leaderboard if… - License: Open-LLM-Leaderboard is CC-BY-4.0, deepeval is Apache-2.0. - Tags unique to Open-LLM-Leaderboard: leaderboard, model-performance-tracking, open-style-questions. - You need to evaluate your LLM's performance on open-ended, human-like question formats across multiple datasets without setting up the evaluation process yourself. ## When NOT to use deepeval - For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. - In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ## When NOT to use Open-LLM-Leaderboard - You are seeking evaluations solely based on closed-response or multiple-choice questions where specific answers can be easily verified by non-LLM means. - Your project has constraints against using commercial LLMs like GPT-4 for evaluation due to cost, licensing issues, or the need for open-source alternatives. ## Common questions ### What is the difference between deepeval and Open-LLM-Leaderboard? deepeval: LLM Evaluation Framework.. Open-LLM-Leaderboard: Tracks LLM performance on open-style questions. See the comparison table for live GitHub stats and shared categories. ### When should I choose deepeval over Open-LLM-Leaderboard? Choose deepeval over Open-LLM-Leaderboard when License: deepeval is Apache-2.0, Open-LLM-Leaderboard is CC-BY-4.0; Requirements: Requires Python environment and familiarity with large language models to effectively utilize Deepeval's capabilities.; Tags unique to deepeval: evaluation, metrics; When developing large language models and you need a comprehensive evaluation framework to measure their performance across various metrics. ### When should I choose Open-LLM-Leaderboard over deepeval? Choose Open-LLM-Leaderboard over deepeval when License: Open-LLM-Leaderboard is CC-BY-4.0, deepeval is Apache-2.0; Tags unique to Open-LLM-Leaderboard: leaderboard, model-performance-tracking, open-style-questions; You need to evaluate your LLM's performance on open-ended, human-like question formats across multiple datasets without setting up the evaluation process yourself. ### When should I avoid deepeval? For small-scale applications that do not require the depth of metrics and evaluations offered by Deepeval, as it might be overkill. In situations where there is a need for real-time performance monitoring, since Deepeval focuses more on post-development evaluation rather than continuous runtime analysis. ### When should I avoid Open-LLM-Leaderboard? You are seeking evaluations solely based on closed-response or multiple-choice questions where specific answers can be easily verified by non-LLM means. Your project has constraints against using commercial LLMs like GPT-4 for evaluation due to cost, licensing issues, or the need for open-source alternatives. ### Is deepeval or Open-LLM-Leaderboard more popular on GitHub? deepeval has more GitHub stars (18,342 vs 53). Stars measure visibility, not whether either tool fits your constraints. ### Are deepeval and Open-LLM-Leaderboard open source? Yes - both are open-source projects on GitHub (deepeval: Apache-2.0, Open-LLM-Leaderboard: CC-BY-4.0). ### Where can I find alternatives to deepeval or Open-LLM-Leaderboard? GraphCanon lists graph-backed alternatives at [deepeval alternatives](/tools/confident-ai-deepeval/alternatives) and [Open-LLM-Leaderboard alternatives](/tools/vila-lab-open-llm-leaderboard/alternatives) ([deepeval markdown twin](/tools/confident-ai-deepeval/alternatives.md), [Open-LLM-Leaderboard markdown twin](/tools/vila-lab-open-llm-leaderboard/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/confident-ai-deepeval-vs-vila-lab-open-llm-leaderboard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, deepeval or Open-LLM-Leaderboard? deepeval: Very active. Open-LLM-Leaderboard: 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 deepeval and Open-LLM-Leaderboard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [deepeval trust report](/tools/confident-ai-deepeval/trust); [Open-LLM-Leaderboard trust report](/tools/vila-lab-open-llm-leaderboard/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=confident-ai-deepeval`](/api/graphcanon/graph?tool=confident-ai-deepeval) - 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/_