--- title: "bigcode-evaluation-harness vs qa_metrics" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-zli12321-qa-metrics" tools: ["bigcode-project-bigcode-evaluation-harness", "zli12321-qa-metrics"] --- # bigcode-evaluation-harness vs qa_metrics *GraphCanon updated Sep 20, 2026* ## Verdict Pick bigcode-evaluation-harness if bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments; pick qa_metrics if qa_metrics is a Python library for evaluating LLMs using standardized QA and semantic metrics, including support for Black-box and open-source models along with APIs from OpenAI and Anthropic. [bigcode-evaluation-harness](https://github.com/bigcode-project/bigcode-evaluation-harness) reports 1.1k GitHub stars, 259 forks, and 96 open issues, last pushed Jul 22, 2025. [qa_metrics](https://github.com/zli12321/qa_metrics) has 64 stars, 6 forks, and 0 open issues, last pushed Jul 18, 2025. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [qa_metrics's repository](https://github.com/zli12321/qa_metrics). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [qa_metrics](/tools/zli12321-qa-metrics.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | A Python package for basic QA evaluations of large language models. | | Stars | 1,058 | 64 | | Forks | 259 | 6 | | Open issues | 96 | 0 | | Language | Python | Python | | Adopt for | bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. | qa_metrics is a Python library for evaluating LLMs using standardized QA and semantic metrics, including support for Black-box and open-source models along with APIs from OpenAI and Anthropic. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | MIT License allows for free use and distribution with attribution required by retaining the copyright notice and license text in any redistribution. | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [qa_metrics](/tools/zli12321-qa-metrics.md) | | --- | --- | --- | | Days since push | 409d | 417d | | Open issues (now) | 96 | 0 | | Stars delta | +3 (30d) | +2 (30d) | | Owner type | Organization | User | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/zli12321-qa-metrics/trust.md) | ## Decision facts: bigcode-evaluation-harness - **Requirements:** Users must have Docker installed to leverage the isolated execution environments for model output evaluation. - **Adopt for:** bigcode-evaluation-harness is tailored towards evaluating autoregressive code generation models via Python and Docker containers for secure and reproducible execution environments. - **License detail:** bigcode-evaluation-harness is distributed under the Apache-2.0 license. ## Decision facts: qa_metrics - **Adopt for:** qa_metrics is a Python library for evaluating LLMs using standardized QA and semantic metrics, including support for Black-box and open-source models along with APIs from OpenAI and Anthropic. - **License detail:** MIT License allows for free use and distribution with attribution required by retaining the copyright notice and license text in any redistribution. ## Choose when ### Choose bigcode-evaluation-harness if… - License: bigcode-evaluation-harness is Apache-2.0, qa_metrics is MIT. - Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.. - Tags unique to bigcode-evaluation-harness: autoregressive-models, code-generation, docker, python. - bigcode-evaluation-harness ships Docker support for self-hosted deployment. - When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments. ### Choose qa_metrics if… - License: qa_metrics is MIT, bigcode-evaluation-harness is Apache-2.0. - Tags unique to qa_metrics: exact-matching, llm-evaluation, qa-automation-test. - When you need to evaluate the performance of large language models with built-in standardized metrics like exact match and F1 Score. ## When NOT to use bigcode-evaluation-harness - When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. - If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity. ## When NOT to use qa_metrics - Avoid if you seek advanced customization or fine-tuning options not present in qa_metrics for metric calculation methods beyond its provided set. - Not ideal when needing specific evaluation tools that are not Black-box or open-source models, as the package focuses on these types of evaluations primarily. ## Common questions ### What is the difference between bigcode-evaluation-harness and qa_metrics? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. qa_metrics: A Python package for basic QA evaluations of large language models.. See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over qa_metrics? Choose bigcode-evaluation-harness over qa_metrics when License: bigcode-evaluation-harness is Apache-2.0, qa_metrics is MIT; Requirements: Users must have Docker installed to leverage the isolated execution environments for model output evaluation.; Tags unique to bigcode-evaluation-harness: autoregressive-models, code-generation, docker, python; bigcode-evaluation-harness ships Docker support for self-hosted deployment; When you need to isolate the evaluation environment from your local development setup, ensuring that no external variables affect the outcomes of model performance assessments. ### When should I choose qa_metrics over bigcode-evaluation-harness? Choose qa_metrics over bigcode-evaluation-harness when License: qa_metrics is MIT, bigcode-evaluation-harness is Apache-2.0; Tags unique to qa_metrics: exact-matching, llm-evaluation, qa-automation-test; When you need to evaluate the performance of large language models with built-in standardized metrics like exact match and F1 Score. ### When should I avoid bigcode-evaluation-harness? When you require real-time evaluation without the overhead of generating outputs locally and then evaluating them within isolated environments via Docker. If your model's evaluation process does not necessitate autoregressive setup or the security features provided by Docker, using bigcode-evaluation-harness might introduce unnecessary complexity. ### When should I avoid qa_metrics? Avoid if you seek advanced customization or fine-tuning options not present in qa_metrics for metric calculation methods beyond its provided set. Not ideal when needing specific evaluation tools that are not Black-box or open-source models, as the package focuses on these types of evaluations primarily. ### Is bigcode-evaluation-harness or qa_metrics more popular on GitHub? bigcode-evaluation-harness has more GitHub stars (1,058 vs 64). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and qa_metrics open source? Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, qa_metrics: MIT). ### Where can I find alternatives to bigcode-evaluation-harness or qa_metrics? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [qa_metrics alternatives](/tools/zli12321-qa-metrics/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [qa_metrics markdown twin](/tools/zli12321-qa-metrics/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/bigcode-project-bigcode-evaluation-harness-vs-zli12321-qa-metrics.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, bigcode-evaluation-harness or qa_metrics? bigcode-evaluation-harness: Dormant. qa_metrics: 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 bigcode-evaluation-harness and qa_metrics? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [bigcode-evaluation-harness trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust); [qa_metrics trust report](/tools/zli12321-qa-metrics/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness`](/api/graphcanon/graph?tool=bigcode-project-bigcode-evaluation-harness) - 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/_