Home/Compare/DeepSeek-R1 vs BMTrain

Comparison

DeepSeek-R1 vs BMTrain

Verdict

Pick DeepSeek-R1 when license: DeepSeek-R1 is MIT, BMTrain is Apache-2.0; pick BMTrain when license: BMTrain is Apache-2.0, DeepSeek-R1 is MIT.

Markdown twin · DeepSeek-R1 alternatives · BMTrain alternatives

GraphCanon updated today

DeepSeek-R1 logo

DeepSeek-R1

deepseek-ai/DeepSeek-R1

92kpushed Jun 27, 2025
vs
BMTrain logo

BMTrain

OpenBMB/BMTrain

624pushed Jul 7, 2026

Trust & integrity

SignalDeepSeek-R1BMTrain
Maintenance
Dormant (379d since push)
As of today · github_public_v1
Very active (3d since push)
As of today · github_public_v1
Provenance
Not a fork · Organization account
As of today · github_public_v1
Not a fork · Organization account
As of today · github_public_v1
Security (OSV)
No lockfile
As of today · none
No lockfile
As of today · none

Tagline

DeepSeek-R1
Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.
BMTrain
Efficient Training (including pre-training and fine-tuning) for Big Models

Stars

DeepSeek-R1
92k
BMTrain
624

Forks

DeepSeek-R1
12k
BMTrain
88

Open issues

DeepSeek-R1
45
BMTrain
10

Language

DeepSeek-R1
-
BMTrain
Python

Adopt for

DeepSeek-R1
DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use.
BMTrain
-

Persona

DeepSeek-R1
-
BMTrain
-

Runtime

DeepSeek-R1
-
BMTrain
-

License

DeepSeek-R1
MIT
BMTrain
Apache-2.0

Last pushed

DeepSeek-R1
Jun 27, 2025
BMTrain
Jul 7, 2026

Categories

DeepSeek-R1
Model Training, LLM Frameworks
BMTrain
Model Training

Trust and health

Maintenance

DeepSeek-R1
Dormant (18%)
BMTrain
Very active (96%)

Days since push

DeepSeek-R1
379d
BMTrain
3d

Open issues (now)

DeepSeek-R1
45
BMTrain
10

Full report

DeepSeek-R1
Trust report

Choose DeepSeek-R1 if…

  • License: DeepSeek-R1 is MIT, BMTrain 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 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.

Choose BMTrain if…

  • License: BMTrain is Apache-2.0, DeepSeek-R1 is MIT.
  • Tags unique to BMTrain: python.
  • BMTrain ships Docker support for self-hosted deployment.

When NOT to use BMTrain

  • Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.

Explore

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 · BMTrain 624 (synced Jul 12, 2026).

Common questions

What is the difference between DeepSeek-R1 and BMTrain?
DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. BMTrain: Efficient Training (including pre-training and fine-tuning) for Big Models. See the comparison table for live GitHub stats and shared categories.
When should I choose DeepSeek-R1 over BMTrain?
Choose DeepSeek-R1 over BMTrain when License: DeepSeek-R1 is MIT, BMTrain 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 choose BMTrain over DeepSeek-R1?
Choose BMTrain over DeepSeek-R1 when License: BMTrain is Apache-2.0, DeepSeek-R1 is MIT; Tags unique to BMTrain: python; BMTrain ships Docker support for self-hosted deployment.
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 BMTrain?
Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
Is DeepSeek-R1 or BMTrain more popular on GitHub?
DeepSeek-R1 has more GitHub stars (91,991 vs 624). Stars measure visibility, not whether either tool fits your constraints.
Are DeepSeek-R1 and BMTrain open source?
Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, BMTrain: Apache-2.0).
Where can I find alternatives to DeepSeek-R1 or BMTrain?
GraphCanon lists graph-backed alternatives at DeepSeek-R1 alternatives and BMTrain alternatives (DeepSeek-R1 markdown twin, BMTrain 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 BMTrain?
DeepSeek-R1: Dormant. BMTrain: Very active. 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 BMTrain?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSeek-R1 trust report; BMTrain trust report.