--- title: "mxnet vs mindspore" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/apache-mxnet-vs-mindspore-ai-mindspore" tools: ["apache-mxnet", "mindspore-ai-mindspore"] --- # mxnet vs mindspore *GraphCanon updated Aug 3, 2026* ## Verdict Pick mxnet if apache MXNet is a deep learning framework that prioritizes efficiency and flexibility, allowing for the mix of symbolic and imperative programming techniques; pick mindspore if mindSpore's core strengths lie in its flexibility across Ascend910, GPU CUDA 10.1, and CPU setups on multiple OSes; it excels in mobile, edge, and cloud scenarios. [mxnet](https://mxnet.apache.org) reports 21k GitHub stars, 6.7k forks, and 2.0k open issues, last pushed Oct 25, 2023. [mindspore](https://gitee.com/mindspore/mindspore) has 4.7k stars, 751 forks, and 250 open issues, last pushed Jul 29, 2024. Figures are from public GitHub metadata via [mxnet's repository](https://github.com/apache/mxnet) and [mindspore's repository](https://github.com/mindspore-ai/mindspore). | | [mxnet](/tools/apache-mxnet.md) | [mindspore](/tools/mindspore-ai-mindspore.md) | | --- | --- | --- | | Tagline | Lightweight, Portable, Flexible Distributed/Mobile Deep Learning Framework | An open-source deep learning framework for mobile, edge and cloud scenarios. | | Stars | 20,817 | 4,699 | | Forks | 6,690 | 751 | | Open issues | 2,007 | 250 | | Language | C++ | C++ | | Adopt for | Apache MXNet is a deep learning framework that prioritizes efficiency and flexibility, allowing for the mix of symbolic and imperative programming techniques. | MindSpore's core strengths lie in its flexibility across Ascend910, GPU CUDA 10.1, and CPU setups on multiple OSes; it excels in mobile, edge, and cloud scenarios. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | Apache-2.0 | | Categories | Model Training | Inference & Serving, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [mxnet](/tools/apache-mxnet.md) | [mindspore](/tools/mindspore-ai-mindspore.md) | | --- | --- | --- | | Maintenance | Archived (8%) | Dormant (18%) | | Days since push | 1012d | 735d | | Archived on GitHub | Yes | No | | Open issues (now) | 2.0k | 250 | | Full report | [trust report](/tools/apache-mxnet/trust.md) | [trust report](/tools/mindspore-ai-mindspore/trust.md) | ## Shared compatibility - **Python**: [mxnet](/tools/apache-mxnet.md) - Python runtime; [mindspore](/tools/mindspore-ai-mindspore.md) - Python runtime ## Decision facts: mxnet - **Pricing:** freemium - Open-source, open-access framework with advanced services potentially requiring proprietary add-ons or cloud service costs. - **Requirements:** MXNet is known for its lightweight nature and efficient memory management, making it suitable for deployment on various hardware configurations. - **Adopt for:** Apache MXNet is a deep learning framework that prioritizes efficiency and flexibility, allowing for the mix of symbolic and imperative programming techniques. ## Decision facts: mindspore - **Adopt for:** MindSpore's core strengths lie in its flexibility across Ascend910, GPU CUDA 10.1, and CPU setups on multiple OSes; it excels in mobile, edge, and cloud scenarios. ## Choose when ### Choose mxnet if… - Pricing: Open-source, open-access framework with advanced services potentially requiring proprietary add-ons or cloud service costs.. - Requirements: MXNet is known for its lightweight nature and efficient memory management, making it suitable for deployment on various hardware configurations.. - Tags unique to mxnet: auto hybridization, distributed-computing, flexible, lightweight. - You prefer to mix symbolic and imperative programming styles in your deep learning projects for maximum productivity and performance. ### Choose mindspore if… - Tags unique to mindspore: ascend910, cpu-support, gpu-support, inference framework. - Also covers Inference & Serving. - When working with Huawei's Ascend hardware like Ascend910 ## When NOT to use mxnet - If you require a framework with more out-of-the-box models and easier-to-use libraries, since MXNet focuses on flexibility and efficiency over convenience in pre-built functionalities. - You are focusing exclusively on one particular programming language (other than Python), as while MXNet supports multiple languages, most community support and updates center around its Python API. ## When NOT to use mindspore - Avoid if only NVIDIA GPUs without CUDA 10.1 support are available - Not ideal for users requiring non-LINUX (excluding Windows) environments beyond specified Ubuntu/CentOS/x86 versions - If development primarily targets hardware not covered by MindSpore's Ascend, CUDA, or CPU setups ## Common questions ### What is the difference between mxnet and mindspore? mxnet: Lightweight, Portable, Flexible Distributed/Mobile Deep Learning Framework. mindspore: An open-source deep learning framework for mobile, edge and cloud scenarios.. See the comparison table for live GitHub stats and shared categories. ### When should I choose mxnet over mindspore? Choose mxnet over mindspore when Pricing: Open-source, open-access framework with advanced services potentially requiring proprietary add-ons or cloud service costs.; Requirements: MXNet is known for its lightweight nature and efficient memory management, making it suitable for deployment on various hardware configurations.; Tags unique to mxnet: auto hybridization, distributed-computing, flexible, lightweight; You prefer to mix symbolic and imperative programming styles in your deep learning projects for maximum productivity and performance. ### When should I choose mindspore over mxnet? Choose mindspore over mxnet when Tags unique to mindspore: ascend910, cpu-support, gpu-support, inference framework; Also covers Inference & Serving; When working with Huawei's Ascend hardware like Ascend910. ### When should I avoid mxnet? If you require a framework with more out-of-the-box models and easier-to-use libraries, since MXNet focuses on flexibility and efficiency over convenience in pre-built functionalities. You are focusing exclusively on one particular programming language (other than Python), as while MXNet supports multiple languages, most community support and updates center around its Python API. ### When should I avoid mindspore? Avoid if only NVIDIA GPUs without CUDA 10.1 support are available Not ideal for users requiring non-LINUX (excluding Windows) environments beyond specified Ubuntu/CentOS/x86 versions If development primarily targets hardware not covered by MindSpore's Ascend, CUDA, or CPU setups ### Is mxnet or mindspore more popular on GitHub? mxnet has more GitHub stars (20,817 vs 4,699). Stars measure visibility, not whether either tool fits your constraints. ### Are mxnet and mindspore open source? Yes - both are open-source projects on GitHub (mxnet: Apache-2.0, mindspore: Apache-2.0). ### Where can I find alternatives to mxnet or mindspore? GraphCanon lists graph-backed alternatives at [mxnet alternatives](/tools/apache-mxnet/alternatives) and [mindspore alternatives](/tools/mindspore-ai-mindspore/alternatives) ([mxnet markdown twin](/tools/apache-mxnet/alternatives.md), [mindspore markdown twin](/tools/mindspore-ai-mindspore/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/apache-mxnet-vs-mindspore-ai-mindspore.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, mxnet or mindspore? mxnet: Archived. mindspore: 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 mxnet and mindspore? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [mxnet trust report](/tools/apache-mxnet/trust); [mindspore trust report](/tools/mindspore-ai-mindspore/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=apache-mxnet`](/api/graphcanon/graph?tool=apache-mxnet) - 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/_