Comparison
MiniMax-01 vs MiniMax-M1
Verdict
Coexists - While MiniMax-M1 may be older, both models can coexist in different applications depending on specific needs.
Markdown twin · MiniMax-01 alternatives · MiniMax-M1 alternatives
GraphCanon updated 3d
vs
Trust & integrity
| Signal | MiniMax-01 | MiniMax-M1 |
|---|---|---|
| Maintenance | Dormant (406d since push) As of 3d · github_public_v1 | Dormant (406d since push) As of 3d · github_public_v1 |
| Provenance | Not a fork · Organization account As of 3d · 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 |
Tagline
- MiniMax-01
- Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention
- MiniMax-M1
- Open-weight large-scale hybrid-attention reasoning model
Stars
- MiniMax-01
- 3.5k
- MiniMax-M1
- 3.2k
Forks
- MiniMax-01
- 332
- MiniMax-M1
- 283
Open issues
- MiniMax-01
- 8
- MiniMax-M1
- 31
Language
- MiniMax-01
- Python
- MiniMax-M1
- Python
Adopt for
- MiniMax-01
- MiniMax-01 optimizes Linear Attention for large-language and vision-language models.
- MiniMax-M1
- MiniMax-M1 stands out for its open-access nature and hybrid-attention mechanisms that promise efficient inference capabilities.
Persona
- MiniMax-01
- -
- MiniMax-M1
- -
Runtime
- MiniMax-01
- -
- MiniMax-M1
- -
License
- MiniMax-01
- MIT
- MiniMax-M1
- Apache-2.0
Last pushed
- MiniMax-01
- Jul 7, 2025
- MiniMax-M1
- Jul 7, 2025
Categories
- MiniMax-01
- LLM Frameworks, Model Training
- MiniMax-M1
- Inference & Serving, LLM Frameworks
Trust and health
Open issues (now)
- MiniMax-01
- 8
- MiniMax-M1
- 31
Stars delta
- MiniMax-01
- +17 (30d)
- MiniMax-M1
- +12 (30d)
Full report
- MiniMax-01
- Trust report
- MiniMax-M1
- Trust report
Typed relationship
MiniMax-01 successor MiniMax-M1MiniMax-M1 seems to be a predecessor model based on the naming and description, indicating that MiniMax-Text-01 and MiniMax-VL-01 are newer evolutions, possibly using similar or improved technology.Coexists - While MiniMax-M1 may be older, both models can coexist in different applications depending on specific needs.
Choose MiniMax-01 if…
- License: MiniMax-01 is MIT, MiniMax-M1 is Apache-2.0.
- MiniMax-M1 seems to be a predecessor model based on the naming and description, indicating that MiniMax-Text-01 and MiniMax-VL-01 are newer evolutions, possibly using similar or improved technology.
- Tags unique to MiniMax-01: vision-language-model, vlm.
- Also covers Model Training.
- When high throughput performance is required for model serving
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
Choose MiniMax-M1 if…
- License: MiniMax-M1 is Apache-2.0, MiniMax-01 is MIT.
- Pricing: Free to use under Apache-2.0 license, cost considerations will mainly stem from computing resources when deploying..
- Requirements: Min 64 GB RAM; Requires Docker; Deployment is recommended using vLLM for optimal performance and efficient processing.; Transformers can also be used directly for deployment, offering an alternative way to integrate MiniMax-M1..
- MiniMax-M1 seems to be a predecessor model based on the naming and description, indicating that MiniMax-Text-01 and MiniMax-VL-01 are newer evolutions, possibly using similar or improved technology.
- Tags unique to MiniMax-M1: minimax-m1, reasoning-models.
- Also covers Inference & Serving.
- When your project requires an open-weight model with flexible access to weights, allowing you to customize the model without any restrictions.
When NOT to use MiniMax-M1
- In scenarios where strict proprietary controls over model weights are necessary, as MiniMax-M1's open-access nature might not comply with such stringent requirements.
- If your project focuses on lightweight inference without the need for large-scale hybrid-attention mechanisms; smaller models might offer more efficient deployment options.
Explore
Sources
Every stat on this page traces to a dated GitHub sync, license file, enrichment field, or trust scan.
- GitHub stars (MiniMax-AI/MiniMax-01) · observed Aug 18, 2026
- GitHub forks (MiniMax-AI/MiniMax-01) · observed Aug 18, 2026
- Last push (MiniMax-AI/MiniMax-01) · observed Jul 7, 2025
- License file (MIT) · observed Aug 18, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (MiniMax-AI/MiniMax-M1) · observed Aug 18, 2026
- GitHub forks (MiniMax-AI/MiniMax-M1) · observed Aug 18, 2026
- Last push (MiniMax-AI/MiniMax-M1) · observed Jul 7, 2025
- License file (Apache-2.0) · observed Aug 18, 2026
- Decision facts (enrichment) · observed Jul 14, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: MiniMax-01 3.5k · MiniMax-M1 3.2k (synced Aug 18, 2026).
Common questions
- What is the difference between MiniMax-01 and MiniMax-M1?
- MiniMax-01: Repository for MiniMax-Text-01 and MiniMax-VL-01 models based on Linear Attention. MiniMax-M1: Open-weight large-scale hybrid-attention reasoning model. See the comparison table for live GitHub stats and shared categories.
- When should I choose MiniMax-01 over MiniMax-M1?
- Choose MiniMax-01 over MiniMax-M1 when License: MiniMax-01 is MIT, MiniMax-M1 is Apache-2.0; MiniMax-M1 seems to be a predecessor model based on the naming and description, indicating that MiniMax-Text-01 and MiniMax-VL-01 are newer evolutions, possibly using similar or improved technology; Tags unique to MiniMax-01: vision-language-model, vlm; Also covers Model Training; When high throughput performance is required for model serving.
- When should I choose MiniMax-M1 over MiniMax-01?
- Choose MiniMax-M1 over MiniMax-01 when License: MiniMax-M1 is Apache-2.0, MiniMax-01 is MIT; Pricing: Free to use under Apache-2.0 license, cost considerations will mainly stem from computing resources when deploying.; Requirements: Min 64 GB RAM; Requires Docker; Deployment is recommended using vLLM for optimal performance and efficient processing.; Transformers can also be used directly for deployment, offering an alternative way to integrate MiniMax-M1.; MiniMax-M1 seems to be a predecessor model based on the naming and description, indicating that MiniMax-Text-01 and MiniMax-VL-01 are newer evolutions, possibly using similar or improved technology; Tags unique to MiniMax-M1: minimax-m1, reasoning-models; Also covers Inference & Serving; When your project requires an open-weight model with flexible access to weights, allowing you to customize the model without any restrictions.
- 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
- When should I avoid MiniMax-M1?
- In scenarios where strict proprietary controls over model weights are necessary, as MiniMax-M1's open-access nature might not comply with such stringent requirements. If your project focuses on lightweight inference without the need for large-scale hybrid-attention mechanisms; smaller models might offer more efficient deployment options.
- Is MiniMax-01 or MiniMax-M1 more popular on GitHub?
- MiniMax-01 has more GitHub stars (3,463 vs 3,172). Stars measure visibility, not whether either tool fits your constraints.
- Are MiniMax-01 and MiniMax-M1 open source?
- Yes - both are open-source projects on GitHub (MiniMax-01: MIT, MiniMax-M1: Apache-2.0).
- Where can I find alternatives to MiniMax-01 or MiniMax-M1?
- GraphCanon lists graph-backed alternatives at MiniMax-01 alternatives and MiniMax-M1 alternatives (MiniMax-01 markdown twin, MiniMax-M1 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, MiniMax-01 or MiniMax-M1?
- MiniMax-01: Dormant. MiniMax-M1: 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 MiniMax-01 and MiniMax-M1?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: MiniMax-01 trust report; MiniMax-M1 trust report.