--- title: "awesome-evals vs llm-leaderboard" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/benchflow-ai-awesome-evals-vs-jonathanchaveztamales-llm-leaderboard" tools: ["benchflow-ai-awesome-evals", "jonathanchaveztamales-llm-leaderboard"] --- # awesome-evals vs llm-leaderboard *GraphCanon updated Jul 28, 2026* ## Verdict Pick awesome-evals if curated resources for AI agent evaluation with BenchFlow backing its maintenance; pick llm-leaderboard if llm-leaderboard provides deprecated benchmark data for large language models alongside service provider pricing information. [awesome-evals](https://github.com/benchflow-ai/awesome-evals) reports 761 GitHub stars, 71 forks, and 21 open issues, last pushed Jul 1, 2026. [llm-leaderboard](https://llm-stats.com) has 359 stars, 40 forks, and 14 open issues, last pushed Oct 24, 2025. Figures are from public GitHub metadata via [awesome-evals's repository](https://github.com/benchflow-ai/awesome-evals) and [llm-leaderboard's repository](https://github.com/JonathanChavezTamales/llm-leaderboard). | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [llm-leaderboard](/tools/jonathanchaveztamales-llm-leaderboard.md) | | --- | --- | --- | | Tagline | A curated library of resources for building and evaluating AI agents | Comprehensive LLM benchmark scores and provider prices | | Stars | 761 | 359 | | Forks | 71 | 40 | | Open issues | 21 | 14 | | Language | - | JavaScript | | Adopt for | Curated resources for AI agent evaluation with BenchFlow backing its maintenance | llm-leaderboard provides deprecated benchmark data for large language models alongside service provider pricing information. | | Persona | - | - | | Runtime | - | - | | License | Other | Other | | Categories | AI Agents, Evaluation & Observability | Evaluation & Observability, LLM Frameworks | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [awesome-evals](/tools/benchflow-ai-awesome-evals.md) | [llm-leaderboard](/tools/jonathanchaveztamales-llm-leaderboard.md) | | --- | --- | --- | | Maintenance | Active (82%) | Slowing (36%) | | Days since push | 26d | 277d | | Open issues (now) | 21 | 14 | | Owner type | Organization | User | | Full report | [trust report](/tools/benchflow-ai-awesome-evals/trust.md) | [trust report](/tools/jonathanchaveztamales-llm-leaderboard/trust.md) | ## Decision facts: awesome-evals - **Adopt for:** Curated resources for AI agent evaluation with BenchFlow backing its maintenance ## Decision facts: llm-leaderboard - **Adopt for:** llm-leaderboard provides deprecated benchmark data for large language models alongside service provider pricing information. ## Choose when ### Choose awesome-evals if… - Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks. - Also covers AI Agents. - Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation ### Choose llm-leaderboard if… - Tags unique to llm-leaderboard: llm, llm-agents, llmops, llms-benchmarking. - Also covers LLM Frameworks. - When you need to compare historical performance and service costs of different LLMs within the constraints of outdated data. ## When NOT to use awesome-evals - Require real-time interactive support or direct tool integrations not covered by a static resource list - Seeking proprietary tools from specific vendors rather than open resources and community content ## When NOT to use llm-leaderboard - If timely or updated benchmarking data is a requirement, as llm-leaderboard's repository has been deprecated. - For real-time evaluations, as this tool does not provide current or recent performance metrics and pricing details. ## Common questions ### What is the difference between awesome-evals and llm-leaderboard? awesome-evals: A curated library of resources for building and evaluating AI agents. llm-leaderboard: Comprehensive LLM benchmark scores and provider prices. See the comparison table for live GitHub stats and shared categories. ### When should I choose awesome-evals over llm-leaderboard? Choose awesome-evals over llm-leaderboard when Tags unique to awesome-evals: agent-evaluation, ai-agents, awesome-list, benchmarks; Also covers AI Agents; Need diverse resources encompassing papers, blogs, talks, tools, and benchmarks specifically curated for AI agent evaluation. ### When should I choose llm-leaderboard over awesome-evals? Choose llm-leaderboard over awesome-evals when Tags unique to llm-leaderboard: llm, llm-agents, llmops, llms-benchmarking; Also covers LLM Frameworks; When you need to compare historical performance and service costs of different LLMs within the constraints of outdated data. ### When should I avoid awesome-evals? Require real-time interactive support or direct tool integrations not covered by a static resource list Seeking proprietary tools from specific vendors rather than open resources and community content ### When should I avoid llm-leaderboard? If timely or updated benchmarking data is a requirement, as llm-leaderboard's repository has been deprecated. For real-time evaluations, as this tool does not provide current or recent performance metrics and pricing details. ### Is awesome-evals or llm-leaderboard more popular on GitHub? awesome-evals has more GitHub stars (761 vs 359). Stars measure visibility, not whether either tool fits your constraints. ### Are awesome-evals and llm-leaderboard open source? Yes - both are open-source projects on GitHub (awesome-evals: Other, llm-leaderboard: Other). ### Where can I find alternatives to awesome-evals or llm-leaderboard? GraphCanon lists graph-backed alternatives at [awesome-evals alternatives](/tools/benchflow-ai-awesome-evals/alternatives) and [llm-leaderboard alternatives](/tools/jonathanchaveztamales-llm-leaderboard/alternatives) ([awesome-evals markdown twin](/tools/benchflow-ai-awesome-evals/alternatives.md), [llm-leaderboard markdown twin](/tools/jonathanchaveztamales-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/benchflow-ai-awesome-evals-vs-jonathanchaveztamales-llm-leaderboard.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, awesome-evals or llm-leaderboard? awesome-evals: Active. llm-leaderboard: 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 awesome-evals and llm-leaderboard? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [awesome-evals trust report](/tools/benchflow-ai-awesome-evals/trust); [llm-leaderboard trust report](/tools/jonathanchaveztamales-llm-leaderboard/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=benchflow-ai-awesome-evals`](/api/graphcanon/graph?tool=benchflow-ai-awesome-evals) - 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/_