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Home/Compare/DeepSeek-R1 vs MiniMax-01

Comparison

DeepSeek-R1 vs MiniMax-01

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.

Markdown twin · DeepSeek-R1 alternatives · MiniMax-01 alternatives

GraphCanon updated 3d

DeepSeek-R1 logo

DeepSeek-R1

deepseek-ai/DeepSeek-R1

★ 92kpushed Jun 27, 2025
vs
MiniMax-01 logo

MiniMax-01

MiniMax-AI/MiniMax-01

★ 3.5kpushed Jul 7, 2025

Trust & integrity

SignalDeepSeek-R1MiniMax-01
Maintenance
Dormant (405d since push)
As of 2w · github_public_v1
Dormant (406d since push)
As of 3d · github_public_v1
Provenance
Not a fork · Organization account
As of 2w · github_public_v1
Not a fork · Organization account
As of 3d · github_public_v1
OSV dependency advisories
No lockfile (source not queried)
As of 1mo · osv@v1
No lockfile (source not queried)
As of 1mo · osv@v1
deps.dev advisories
Not queried
deps.dev@v1
Not queried
deps.dev@v1
OpenSSF Scorecard
Not queried
openssf-scorecard@v1
Not queried
openssf-scorecard@v1
DeepSeek-R1 trust reportMiniMax-01 trust reportTrust methodology

Tagline

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

Stars

DeepSeek-R1
92k
MiniMax-01
3.5k

Forks

DeepSeek-R1
12k
MiniMax-01
332

Open issues

DeepSeek-R1
38
MiniMax-01
8

Language

DeepSeek-R1
-
MiniMax-01
Python

Adopt for

DeepSeek-R1
DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use.
MiniMax-01
MiniMax-01 optimizes Linear Attention for large-language and vision-language models.

Persona

DeepSeek-R1
-
MiniMax-01
-

Runtime

DeepSeek-R1
-
MiniMax-01
-

License

DeepSeek-R1
MIT
MiniMax-01
MIT

Last pushed

DeepSeek-R1
Jun 27, 2025
MiniMax-01
Jul 7, 2025

Categories

DeepSeek-R1
LLM Frameworks, Model Training
MiniMax-01
LLM Frameworks, Model Training

Trust and health

Days since push

DeepSeek-R1
405d
MiniMax-01
406d

Open issues (now)

DeepSeek-R1
38
MiniMax-01
8

Stars delta

DeepSeek-R1
Unknown
MiniMax-01
+17 (30d)

Open issues delta

DeepSeek-R1
Unknown
MiniMax-01
0 (30d)

Full report

DeepSeek-R1
Trust report
MiniMax-01
Trust report

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.

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.
GitHubDetails

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 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
GitHubDetails

Explore

DeepSeek-R1 trust report →MiniMax-01 trust report →LLM Frameworks category →Model Training category →All comparisonsStack workflowsTrending tools

Sources

Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.

GitHub stars on cards: DeepSeek-R1 92k · MiniMax-01 3.5k (synced Aug 6, 2026).

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 and MiniMax-01 alternatives (DeepSeek-R1 markdown twin, MiniMax-01 markdown twin), 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 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; MiniMax-01 trust report.

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