--- title: "DeepSeek-R1 vs MiniMax-01" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/deepseek-ai-deepseek-r1-vs-minimax-ai-minimax-01" tools: ["deepseek-ai-deepseek-r1", "minimax-ai-minimax-01"] --- # DeepSeek-R1 vs MiniMax-01 *GraphCanon updated Aug 18, 2026* ## Verdict Pick DeepSeek-R1 if deepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use; pick MiniMax-01 if miniMax-01 optimizes Linear Attention for large-language and vision-language models. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) reports 92k GitHub stars, 12k forks, and 38 open issues, last pushed Jun 27, 2025. [MiniMax-01](https://www.minimax.io/) has 3.5k stars, 332 forks, and 8 open issues, last pushed Jul 7, 2025. Figures are from public GitHub metadata via [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1) and [MiniMax-01's repository](https://github.com/MiniMax-AI/MiniMax-01). | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [MiniMax-01](/tools/minimax-ai-minimax-01.md) | | --- | --- | --- | | Tagline | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention | | Stars | 91,982 | 3,463 | | Forks | 11,706 | 332 | | Open issues | 38 | 8 | | Language | - | Python | | Adopt for | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | MiniMax-01 optimizes Linear Attention for large-language and vision-language models. | | Persona | - | - | | Runtime | - | - | | License | MIT | MIT | | Categories | LLM Frameworks, Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | [MiniMax-01](/tools/minimax-ai-minimax-01.md) | | --- | --- | --- | | Days since push | 405d | 406d | | Open issues (now) | 38 | 8 | | Stars delta | Unknown | +17 (30d) | | Open issues delta | Unknown | 0 (30d) | | Full report | [trust report](/tools/deepseek-ai-deepseek-r1/trust.md) | [trust report](/tools/minimax-ai-minimax-01/trust.md) | ## Decision facts: DeepSeek-R1 - **Pricing:** freemium - The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository. - **Requirements:** Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs. - **Adopt for:** DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. ## Decision facts: MiniMax-01 - **Adopt for:** MiniMax-01 optimizes Linear Attention for large-language and vision-language models. ## Choose when ### Choose DeepSeek-R1 if… - Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.. - Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.. - Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit-license. - When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics. ### Choose MiniMax-01 if… - Tags unique to MiniMax-01: large language models, llm, vision-language-model, vlm. - When high throughput performance is required for model serving - More recently updated (last pushed Jul 7, 2025). ## When NOT to use DeepSeek-R1 - Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. - If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series. ## When NOT to use MiniMax-01 - If deep customization of attention mechanisms aside from Linear Attention is needed - In favor of frameworks lacking vLLM's optimization features, when efficiency or memory use are secondary to flexibility ## Common questions ### What is the difference between DeepSeek-R1 and MiniMax-01? DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. MiniMax-01: Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention. See the comparison table for live GitHub stats and shared categories. ### When should I choose DeepSeek-R1 over MiniMax-01? Choose DeepSeek-R1 over MiniMax-01 when Pricing: The repository allows for commercial use under the MIT License or respective original licenses with no explicit monetary costs outlined in the repository.; Requirements: Min 4 GB RAM; This is a rough estimate based on common model requirements. Specific models within DeepSeek-R1 may have different resource needs.; Tags unique to DeepSeek-R1: commercial use, derived models, distilled models, mit-license; When you need to work with pre-trained models derived specifically from the Qwen-2.5 and Llama3.x series, benefiting from their unique characteristics. ### When should I choose MiniMax-01 over DeepSeek-R1? Choose MiniMax-01 over DeepSeek-R1 when Tags unique to MiniMax-01: large language models, llm, vision-language-model, vlm; When high throughput performance is required for model serving; More recently updated (last pushed Jul 7, 2025). ### When should I avoid DeepSeek-R1? Avoid if you need foundational models rather than distilled versions, as DeepSeek-R1 specializes in providing smaller, more efficient models suitable for resource-constrained environments. If your project is tightly regulated or requires models from a different lineage, as DeepSeek-R1 exclusively provides derivatives of Qwen and LLaMA series. ### When should I avoid MiniMax-01? If deep customization of attention mechanisms aside from Linear Attention is needed In favor of frameworks lacking vLLM's optimization features, when efficiency or memory use are secondary to flexibility ### Is DeepSeek-R1 or MiniMax-01 more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,982 vs 3,463). Stars measure visibility, not whether either tool fits your constraints. ### Are DeepSeek-R1 and MiniMax-01 open source? Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, MiniMax-01: MIT). ### Where can I find alternatives to DeepSeek-R1 or MiniMax-01? GraphCanon lists graph-backed alternatives at [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) and [MiniMax-01 alternatives](/tools/minimax-ai-minimax-01/alternatives) ([DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/alternatives.md), [MiniMax-01 markdown twin](/tools/minimax-ai-minimax-01/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/deepseek-ai-deepseek-r1-vs-minimax-ai-minimax-01.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, DeepSeek-R1 or MiniMax-01? DeepSeek-R1: Dormant. MiniMax-01: 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 DeepSeek-R1 and MiniMax-01? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust); [MiniMax-01 trust report](/tools/minimax-ai-minimax-01/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=deepseek-ai-deepseek-r1`](/api/graphcanon/graph?tool=deepseek-ai-deepseek-r1) - 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/_