--- title: "bigcode-evaluation-harness vs simple-evals" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/bigcode-project-bigcode-evaluation-harness-vs-openai-simple-evals" tools: ["bigcode-project-bigcode-evaluation-harness", "openai-simple-evals"] --- # bigcode-evaluation-harness vs simple-evals *GraphCanon updated Aug 7, 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 simple-evals if simple-evals provides lightweight tools for evaluating language models using reference implementations from HealthBench, BrowseComp, SimpleQA. Last updates July 2025. [bigcode-evaluation-harness](https://github.com/bigcode-project/bigcode-evaluation-harness) reports 1.1k GitHub stars, 261 forks, and 96 open issues, last pushed Jul 22, 2025. [simple-evals](https://github.com/openai/simple-evals) has 4.6k stars, 501 forks, and 56 open issues, last pushed Apr 22, 2026. Figures are from public GitHub metadata via [bigcode-evaluation-harness's repository](https://github.com/bigcode-project/bigcode-evaluation-harness) and [simple-evals's repository](https://github.com/openai/simple-evals). | | [bigcode-evaluation-harness](/tools/bigcode-project-bigcode-evaluation-harness.md) | [simple-evals](/tools/openai-simple-evals.md) | | --- | --- | --- | | Tagline | A framework for evaluating autoregressive code generation language models. | A lightweight library for evaluating language models. | | Stars | 1,055 | 4,595 | | Forks | 261 | 501 | | Open issues | 96 | 56 | | 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. | simple-evals provides lightweight tools for evaluating language models using reference implementations from HealthBench, BrowseComp, SimpleQA. Last updates July 2025. | | Persona | - | - | | Runtime | - | - | | License | bigcode-evaluation-harness is distributed under the Apache-2.0 license. | MIT licensed Python library for transparent language model evaluations with specific benchmark support until July 2025. | | 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) | [simple-evals](/tools/openai-simple-evals.md) | | --- | --- | --- | | Maintenance | Dormant (18%) | Slowing (36%) | | Days since push | 378d | 106d | | Open issues (now) | 96 | 56 | | Full report | [trust report](/tools/bigcode-project-bigcode-evaluation-harness/trust.md) | [trust report](/tools/openai-simple-evals/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: simple-evals - **Adopt for:** simple-evals provides lightweight tools for evaluating language models using reference implementations from HealthBench, BrowseComp, SimpleQA. Last updates July 2025. - **License detail:** MIT licensed Python library for transparent language model evaluations with specific benchmark support until July 2025. ## Choose when ### Choose bigcode-evaluation-harness if… - License: bigcode-evaluation-harness is Apache-2.0, simple-evals 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 simple-evals if… - License: simple-evals is MIT, bigcode-evaluation-harness is Apache-2.0. - Tags unique to simple-evals: benchmark, depreciation notice, evaluation, language-models. - When you need a stable baseline to evaluate model performance with specific benchmarks like MMLU, HumanEval, and DROP that won't change after July 2025 ## 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 simple-evals - For evaluating models released or significantly updated after July 2025, as this tool does not include future benchmarks - When you need a tool that will adapt and expand its benchmark set with emerging model releases and evaluation tasks beyond 2025 ## Common questions ### What is the difference between bigcode-evaluation-harness and simple-evals? bigcode-evaluation-harness: A framework for evaluating autoregressive code generation language models.. simple-evals: A lightweight library for evaluating language models.. See the comparison table for live GitHub stats and shared categories. ### When should I choose bigcode-evaluation-harness over simple-evals? Choose bigcode-evaluation-harness over simple-evals when License: bigcode-evaluation-harness is Apache-2.0, simple-evals 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 simple-evals over bigcode-evaluation-harness? Choose simple-evals over bigcode-evaluation-harness when License: simple-evals is MIT, bigcode-evaluation-harness is Apache-2.0; Tags unique to simple-evals: benchmark, depreciation notice, evaluation, language-models; When you need a stable baseline to evaluate model performance with specific benchmarks like MMLU, HumanEval, and DROP that won't change after July 2025. ### 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 simple-evals? For evaluating models released or significantly updated after July 2025, as this tool does not include future benchmarks When you need a tool that will adapt and expand its benchmark set with emerging model releases and evaluation tasks beyond 2025 ### Is bigcode-evaluation-harness or simple-evals more popular on GitHub? simple-evals has more GitHub stars (4,595 vs 1,055). Stars measure visibility, not whether either tool fits your constraints. ### Are bigcode-evaluation-harness and simple-evals open source? Yes - both are open-source projects on GitHub (bigcode-evaluation-harness: Apache-2.0, simple-evals: MIT). ### Where can I find alternatives to bigcode-evaluation-harness or simple-evals? GraphCanon lists graph-backed alternatives at [bigcode-evaluation-harness alternatives](/tools/bigcode-project-bigcode-evaluation-harness/alternatives) and [simple-evals alternatives](/tools/openai-simple-evals/alternatives) ([bigcode-evaluation-harness markdown twin](/tools/bigcode-project-bigcode-evaluation-harness/alternatives.md), [simple-evals markdown twin](/tools/openai-simple-evals/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-openai-simple-evals.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 simple-evals? bigcode-evaluation-harness: Dormant. simple-evals: 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 bigcode-evaluation-harness and simple-evals? 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); [simple-evals trust report](/tools/openai-simple-evals/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/_