--- title: "MixEval vs DevEval" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/jinjieni-mixeval-vs-open-compass-deveval" tools: ["jinjieni-mixeval", "open-compass-deveval"] --- # MixEval vs DevEval *GraphCanon updated Oct 8, 2026* ## Verdict Pick MixEval if mixEval offers a comprehensive evaluation suite and dynamic data release tailored for large language models (LLMs) and multimodal systems, supporting a variety of benchmarking needs; pick DevEval if devEval suits organizations requiring Python-centric software development benchmarks and practices evaluation. [MixEval](https://mixeval.github.io/) reports 254 GitHub stars, 40 forks, and 7 open issues, last pushed Nov 10, 2024. [DevEval](https://github.com/open-compass/DevEval) has 138 stars, 13 forks, and 0 open issues, last pushed May 30, 2024. Figures are from public GitHub metadata via [MixEval's repository](https://github.com/JinjieNi/MixEval) and [DevEval's repository](https://github.com/open-compass/DevEval). | | [MixEval](/tools/jinjieni-mixeval.md) | [DevEval](/tools/open-compass-deveval.md) | | --- | --- | --- | | Tagline | Evaluation suite and dynamic data release for MixEval | A Comprehensive Benchmark for Software Development | | Stars | 254 | 138 | | Forks | 40 | 13 | | Open issues | 7 | 0 | | Language | Python | Python | | Adopt for | MixEval offers a comprehensive evaluation suite and dynamic data release tailored for large language models (LLMs) and multimodal systems, supporting a variety of benchmarking needs. | DevEval suits organizations requiring Python-centric software development benchmarks and practices evaluation. | | Persona | - | - | | Runtime | - | - | | License | - | Apache-2.0 | | Categories | Evaluation & Observability | Evaluation & Observability | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [MixEval](/tools/jinjieni-mixeval.md) | [DevEval](/tools/open-compass-deveval.md) | | --- | --- | --- | | Days since push | 679d | 860d | | Open issues (now) | 7 | 0 | | Owner type | User | Organization | | Full report | [trust report](/tools/jinjieni-mixeval/trust.md) | [trust report](/tools/open-compass-deveval/trust.md) | ## Decision facts: MixEval - **Requirements:** Min 8 GB RAM; Python environment setup is required. Ensure Python version 3.11 is used, as specified in the README excerpt.; A conda environment named 'MixEval' must be created and activated. - **Adopt for:** MixEval offers a comprehensive evaluation suite and dynamic data release tailored for large language models (LLMs) and multimodal systems, supporting a variety of benchmarking needs. ## Decision facts: DevEval - **Adopt for:** DevEval suits organizations requiring Python-centric software development benchmarks and practices evaluation. ## Choose when ### Choose MixEval if… - Requirements: Min 8 GB RAM; Python environment setup is required. Ensure Python version 3.11 is used, as specified in the README excerpt.; A conda environment named 'MixEval' must be created and activated.. - Tags unique to MixEval: evaluation-framework, foundation models, large-language-models, large-multimodal-models. - You need to evaluate LLMs and multimodal models within the same framework, as MixEval is designed with support for both types of models. ### Choose DevEval if… - Tags unique to DevEval: docker-supported, python, software-development. - Choose DevEval when you require comprehensive benchmarking specifically for software development using Python. - Leaner open-issue backlog (0). ## When NOT to use MixEval - You are looking for a lightweight solution since MixEval focuses on providing exhaustive evaluation with extensive benchmarking possibilities which may increase complexity. - Your primary focus is on models outside the scope of LLMs or multimodal systems, as MixEval primarily targets these specific types of AI architectures. ## When NOT to use DevEval - Avoid DevEval if your software projects heavily rely on languages other than Python. - Do not use it when a non-Dockerized evaluation tool is preferred due to organizational constraints or preferences. ## Common questions ### What is the difference between MixEval and DevEval? MixEval: Evaluation suite and dynamic data release for MixEval. DevEval: A Comprehensive Benchmark for Software Development. See the comparison table for live GitHub stats and shared categories. ### When should I choose MixEval over DevEval? Choose MixEval over DevEval when Requirements: Min 8 GB RAM; Python environment setup is required. Ensure Python version 3.11 is used, as specified in the README excerpt.; A conda environment named 'MixEval' must be created and activated.; Tags unique to MixEval: evaluation-framework, foundation models, large-language-models, large-multimodal-models; You need to evaluate LLMs and multimodal models within the same framework, as MixEval is designed with support for both types of models. ### When should I choose DevEval over MixEval? Choose DevEval over MixEval when Tags unique to DevEval: docker-supported, python, software-development; Choose DevEval when you require comprehensive benchmarking specifically for software development using Python; Leaner open-issue backlog (0). ### When should I avoid MixEval? You are looking for a lightweight solution since MixEval focuses on providing exhaustive evaluation with extensive benchmarking possibilities which may increase complexity. Your primary focus is on models outside the scope of LLMs or multimodal systems, as MixEval primarily targets these specific types of AI architectures. ### When should I avoid DevEval? Avoid DevEval if your software projects heavily rely on languages other than Python. Do not use it when a non-Dockerized evaluation tool is preferred due to organizational constraints or preferences. ### Is MixEval or DevEval more popular on GitHub? MixEval has more GitHub stars (254 vs 138). Stars measure visibility, not whether either tool fits your constraints. ### Are MixEval and DevEval open source? Yes - both are open-source projects on GitHub. ### Where can I find alternatives to MixEval or DevEval? GraphCanon lists graph-backed alternatives at [MixEval alternatives](/tools/jinjieni-mixeval/alternatives) and [DevEval alternatives](/tools/open-compass-deveval/alternatives) ([MixEval markdown twin](/tools/jinjieni-mixeval/alternatives.md), [DevEval markdown twin](/tools/open-compass-deveval/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/jinjieni-mixeval-vs-open-compass-deveval.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, MixEval or DevEval? MixEval: Dormant. DevEval: 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 MixEval and DevEval? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [MixEval trust report](/tools/jinjieni-mixeval/trust); [DevEval trust report](/tools/open-compass-deveval/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=jinjieni-mixeval`](/api/graphcanon/graph?tool=jinjieni-mixeval) - 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/_