--- title: "code-eval vs autoarena" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/abacaj-code-eval-vs-kolenaio-autoarena" tools: ["abacaj-code-eval", "kolenaio-autoarena"] --- # code-eval vs autoarena *GraphCanon updated Aug 5, 2026* ## Verdict Pick code-eval if code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability; pick autoarena if autoarena automates evaluations for LLMs and RAG systems through a user-friendly interface where projects are created and judged without manual intervention by the users. [code-eval](https://github.com/abacaj/code-eval) reports 431 GitHub stars, 37 forks, and 5 open issues, last pushed Sep 12, 2023. [autoarena](https://www.kolena.com/autoarena/) has 108 stars, 9 forks, and 4 open issues, last pushed Dec 16, 2024. Figures are from public GitHub metadata via [code-eval's repository](https://github.com/abacaj/code-eval) and [autoarena's repository](https://github.com/kolenaIO/autoarena). | | [code-eval](/tools/abacaj-code-eval.md) | [autoarena](/tools/kolenaio-autoarena.md) | | --- | --- | --- | | Tagline | Run evaluation on LLMs using human-eval benchmark. | Automated evaluation of LLMs and RAG systems | | Stars | 431 | 108 | | Forks | 37 | 9 | | Open issues | 5 | 4 | | Language | Python | TypeScript | | Adopt for | code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability. | autoarena automates evaluations for LLMs and RAG systems through a user-friendly interface where projects are created and judged without manual intervention by the users. | | Persona | - | - | | Runtime | - | - | | License | MIT | Apache-2.0 license | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [code-eval](/tools/abacaj-code-eval.md) | [autoarena](/tools/kolenaio-autoarena.md) | | --- | --- | --- | | Days since push | 1058d | 589d | | Open issues (now) | 5 | 4 | | Owner type | User | Organization | | Full report | [trust report](/tools/abacaj-code-eval/trust.md) | [trust report](/tools/kolenaio-autoarena/trust.md) | ## Shared compatibility - **Python**: [code-eval](/tools/abacaj-code-eval.md) - Python runtime; [autoarena](/tools/kolenaio-autoarena.md) - Python runtime ## Decision facts: code-eval - **Adopt for:** code-eval assesses large language models with the human-eval benchmark to provide insights into code generation reliability. ## Decision facts: autoarena - **Hosting:** self hosted - **Requirements:** Python environment and internet access are needed for PyPI installation via pip. - **Adopt for:** autoarena automates evaluations for LLMs and RAG systems through a user-friendly interface where projects are created and judged without manual intervention by the users. - **License detail:** Apache-2.0 license ## Choose when ### Choose code-eval if… - code-eval is primarily Python; autoarena is TypeScript. - License: code-eval is MIT, autoarena is Apache-2.0. - Tags unique to code-eval: humaneval, wizardcoder. - When you need clear comparisons of pass rates for different LLMs using standardized tests ### Choose autoarena if… - autoarena is primarily TypeScript; code-eval is Python. - License: autoarena is Apache-2.0, code-eval is MIT. - Requirements: Python environment and internet access are needed for PyPI installation via pip.. - Tags unique to autoarena: ai, evaluation, llm-evaluation, rag. - When you need a TypeScript-based tool to rank LLMs and RAG systems via automated head-to-head comparisons, and a web UI is preferable. ## When NOT to use code-eval - If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences - For real-time or dynamic evaluations as this repo offers pre-computed static results only ## When NOT to use autoarena - If your environment lacks the necessary Python packages or you cannot install from PyPI due to restrictions. - When real-time evaluation needs surpass capabilities, such as requiring immediate feedback beyond autoarena's batch-processing approach. ## Common questions ### What is the difference between code-eval and autoarena? code-eval: Run evaluation on LLMs using human-eval benchmark.. autoarena: Automated evaluation of LLMs and RAG systems. See the comparison table for live GitHub stats and shared categories. ### When should I choose code-eval over autoarena? Choose code-eval over autoarena when code-eval is primarily Python; autoarena is TypeScript; License: code-eval is MIT, autoarena is Apache-2.0; Tags unique to code-eval: humaneval, wizardcoder; When you need clear comparisons of pass rates for different LLMs using standardized tests. ### When should I choose autoarena over code-eval? Choose autoarena over code-eval when autoarena is primarily TypeScript; code-eval is Python; License: autoarena is Apache-2.0, code-eval is MIT; Requirements: Python environment and internet access are needed for PyPI installation via pip.; Tags unique to autoarena: ai, evaluation, llm-evaluation, rag; When you need a TypeScript-based tool to rank LLMs and RAG systems via automated head-to-head comparisons, and a web UI is preferable. ### When should I avoid code-eval? If the tool's results do not correlate well with the official published benchmarks due to unknown prompt differences For real-time or dynamic evaluations as this repo offers pre-computed static results only ### When should I avoid autoarena? If your environment lacks the necessary Python packages or you cannot install from PyPI due to restrictions. When real-time evaluation needs surpass capabilities, such as requiring immediate feedback beyond autoarena's batch-processing approach. ### Is code-eval or autoarena more popular on GitHub? code-eval has more GitHub stars (431 vs 108). Stars measure visibility, not whether either tool fits your constraints. ### Are code-eval and autoarena open source? Yes - both are open-source projects on GitHub (code-eval: MIT, autoarena: Apache-2.0). ### Where can I find alternatives to code-eval or autoarena? GraphCanon lists graph-backed alternatives at [code-eval alternatives](/tools/abacaj-code-eval/alternatives) and [autoarena alternatives](/tools/kolenaio-autoarena/alternatives) ([code-eval markdown twin](/tools/abacaj-code-eval/alternatives.md), [autoarena markdown twin](/tools/kolenaio-autoarena/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/abacaj-code-eval-vs-kolenaio-autoarena.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, code-eval or autoarena? code-eval: Dormant. autoarena: 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 code-eval and autoarena? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [code-eval trust report](/tools/abacaj-code-eval/trust); [autoarena trust report](/tools/kolenaio-autoarena/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=abacaj-code-eval`](/api/graphcanon/graph?tool=abacaj-code-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/_