--- title: "MARS vs DeepSeek-R1" type: "comparison" canonical_url: "https://www.graphcanon.com/compare/agi-arena-mars-vs-deepseek-ai-deepseek-r1" tools: ["agi-arena-mars", "deepseek-ai-deepseek-r1"] --- # MARS vs DeepSeek-R1 *GraphCanon updated Jul 12, 2026* ## Verdict Pick MARS when license: MARS is Apache-2.0, DeepSeek-R1 is MIT; pick DeepSeek-R1 when license: DeepSeek-R1 is MIT, MARS is Apache-2.0. [MARS](https://github.com/AGI-Arena/MARS) reports 723 GitHub stars, 49 forks, and 6 open issues, last pushed Mar 26, 2026. [DeepSeek-R1](https://github.com/deepseek-ai/DeepSeek-R1) has 92k stars, 12k forks, and 45 open issues, last pushed Jun 27, 2025. Figures are from public GitHub metadata via [MARS's repository](https://github.com/AGI-Arena/MARS) and [DeepSeek-R1's repository](https://github.com/deepseek-ai/DeepSeek-R1). | | [MARS](/tools/agi-arena-mars.md) | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | | --- | --- | --- | | Tagline | The official implementation of MARS: Unleashing the Power of Variance Reduction for Training Large Models | Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses. | | Stars | 723 | 91,991 | | Forks | 49 | 11,711 | | Open issues | 6 | 45 | | Language | Python | - | | Adopt for | - | DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use. | | Persona | - | - | | Runtime | - | - | | License | Apache-2.0 | MIT | | Categories | Model Training | LLM Frameworks, Model Training | ## Trust and health _Sourced signals - not a safety guarantee. No winner column._ | | [MARS](/tools/agi-arena-mars.md) | [DeepSeek-R1](/tools/deepseek-ai-deepseek-r1.md) | | --- | --- | --- | | Maintenance | Slowing (36%) | Dormant (18%) | | Days since push | 107d | 379d | | Open issues (now) | 6 | 45 | | Full report | [trust report](/tools/agi-arena-mars/trust.md) | [trust report](/tools/deepseek-ai-deepseek-r1/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. ## Choose when ### Choose MARS if… - License: MARS is Apache-2.0, DeepSeek-R1 is MIT. - Tags unique to MARS: optimizer, fine-tuning, optimization-algorithms, python. - More recently updated (last pushed Mar 26, 2026). ### Choose DeepSeek-R1 if… - License: DeepSeek-R1 is MIT, MARS is Apache-2.0. - 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: derived models, mit license, distilled models, commercial use. - Also covers LLM Frameworks. - 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 NOT to use MARS - Last GitHub push was 108 days ago (slowing maintenance, Mar 26, 2026). Validate activity before betting a new project on MARS. - Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ## 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. ## Common questions ### What is the difference between MARS and DeepSeek-R1? MARS: The official implementation of MARS: Unleashing the Power of Variance Reduction for Training Large Models. DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. See the comparison table for live GitHub stats and shared categories. ### When should I choose MARS over DeepSeek-R1? Choose MARS over DeepSeek-R1 when License: MARS is Apache-2.0, DeepSeek-R1 is MIT; Tags unique to MARS: optimizer, fine-tuning, optimization-algorithms, python; More recently updated (last pushed Mar 26, 2026). ### When should I choose DeepSeek-R1 over MARS? Choose DeepSeek-R1 over MARS when License: DeepSeek-R1 is MIT, MARS is Apache-2.0; 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: derived models, mit license, distilled models, commercial use; Also covers LLM Frameworks; 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 avoid MARS? Last GitHub push was 108 days ago (slowing maintenance, Mar 26, 2026). Validate activity before betting a new project on MARS. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge. ### 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. ### Is MARS or DeepSeek-R1 more popular on GitHub? DeepSeek-R1 has more GitHub stars (91,991 vs 723). Stars measure visibility, not whether either tool fits your constraints. ### Are MARS and DeepSeek-R1 open source? Yes - both are open-source projects on GitHub (MARS: Apache-2.0, DeepSeek-R1: MIT). ### Where can I find alternatives to MARS or DeepSeek-R1? GraphCanon lists graph-backed alternatives at [MARS alternatives](/tools/agi-arena-mars/alternatives) and [DeepSeek-R1 alternatives](/tools/deepseek-ai-deepseek-r1/alternatives) ([MARS markdown twin](/tools/agi-arena-mars/alternatives.md), [DeepSeek-R1 markdown twin](/tools/deepseek-ai-deepseek-r1/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/agi-arena-mars-vs-deepseek-ai-deepseek-r1.md) mirrors this page for agents and LLM crawlers, with the same stats table and FAQ answers. ### Which is better maintained, MARS or DeepSeek-R1? MARS: Slowing. DeepSeek-R1: 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 MARS and DeepSeek-R1? GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: [MARS trust report](/tools/agi-arena-mars/trust); [DeepSeek-R1 trust report](/tools/deepseek-ai-deepseek-r1/trust). --- **Machine-readable endpoints** - JSON: [`/api/graphcanon/graph?tool=agi-arena-mars`](/api/graphcanon/graph?tool=agi-arena-mars) - 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/_