Comparison
DeepSeek-R1 vs aideml
Verdict
Pick DeepSeek-R1 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.; pick aideml when tags unique to aideml: data-science, llm, ai, autoresearch.
Markdown twin · DeepSeek-R1 alternatives · aideml alternatives
GraphCanon updated today
vs
Trust & integrity
| Signal | DeepSeek-R1 | aideml |
|---|---|---|
| Maintenance | Dormant (379d since push) As of today · github_public_v1 | Steady (70d 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 | 1 low (1 low) As of today · osv@v1 |
Tagline
- DeepSeek-R1
- Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.
- aideml
- AIDE: AI-Driven Exploration in the Space of Code. The machine Learning engineering agent that automates AI R&D.
Stars
- DeepSeek-R1
- 92k
- aideml
- 1.3k
Forks
- DeepSeek-R1
- 12k
- aideml
- 197
Open issues
- DeepSeek-R1
- 45
- aideml
- 0
Language
- DeepSeek-R1
- -
- aideml
- Python
Adopt for
- DeepSeek-R1
- DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use.
- aideml
- -
Persona
- DeepSeek-R1
- -
- aideml
- -
Runtime
- DeepSeek-R1
- -
- aideml
- -
License
- DeepSeek-R1
- MIT
- aideml
- MIT
Last pushed
- DeepSeek-R1
- Jun 27, 2025
- aideml
- May 2, 2026
Categories
- DeepSeek-R1
- LLM Frameworks, Model Training
- aideml
- AI Agents, LLM Frameworks, Model Training
Trust and health
Maintenance
- DeepSeek-R1
- Dormant (18%)
- aideml
- Steady (60%)
Days since push
- DeepSeek-R1
- 379d
- aideml
- 70d
Open issues (now)
- DeepSeek-R1
- 45
- aideml
- 0
Security scan
- DeepSeek-R1
- No lockfile
- aideml
- 1 low (1 low)
Full report
- DeepSeek-R1
- Trust report
- aideml
- 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: derived models, mit license, distilled models, commercial use.
- 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 aideml if…
- Tags unique to aideml: data-science, llm, ai, autoresearch.
- Also covers AI Agents.
- aideml ships Docker support for self-hosted deployment.
When NOT to use aideml
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism.
- LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves.
- 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 (deepseek-ai/DeepSeek-R1) · observed Jul 12, 2026
- GitHub forks (deepseek-ai/DeepSeek-R1) · observed Jul 12, 2026
- Last push (deepseek-ai/DeepSeek-R1) · observed Jun 27, 2025
- License file (MIT) · observed Jul 12, 2026
- Decision facts (enrichment) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
- GitHub stars (WecoAI/aideml) · observed Jul 11, 2026
- GitHub forks (WecoAI/aideml) · observed Jul 11, 2026
- Last push (WecoAI/aideml) · observed May 2, 2026
- License file (MIT) · observed Jul 11, 2026
- Trust scan (lockfile / OSV) · observed Jul 11, 2026
GitHub stars on cards: DeepSeek-R1 92k · aideml 1.3k (synced Jul 12, 2026).
Common questions
- What is the difference between DeepSeek-R1 and aideml?
- DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. aideml: AIDE: AI-Driven Exploration in the Space of Code. The machine Learning engineering agent that automates AI R&D.. See the comparison table for live GitHub stats and shared categories.
- When should I choose DeepSeek-R1 over aideml?
- Choose DeepSeek-R1 over aideml 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: derived models, mit license, distilled models, commercial use; 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 aideml over DeepSeek-R1?
- Choose aideml over DeepSeek-R1 when Tags unique to aideml: data-science, llm, ai, autoresearch; Also covers AI Agents; aideml 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 aideml?
- AI Agents: Don't use an agent loop when a deterministic workflow would do; agents add latency, cost, and non-determinism. LLM Frameworks: Avoid a framework for a single prompt-and-retrieve call; the abstraction can cost more than it saves. Model Training: Try prompting and RAG first; fine-tuning is the answer to style/format, not missing knowledge.
- Is DeepSeek-R1 or aideml more popular on GitHub?
- DeepSeek-R1 has more GitHub stars (91,991 vs 1,347). Stars measure visibility, not whether either tool fits your constraints.
- Are DeepSeek-R1 and aideml open source?
- Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, aideml: MIT).
- Where can I find alternatives to DeepSeek-R1 or aideml?
- GraphCanon lists graph-backed alternatives at DeepSeek-R1 alternatives and aideml alternatives (DeepSeek-R1 markdown twin, aideml 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 aideml?
- DeepSeek-R1: Dormant. aideml: Steady. 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 aideml?
- GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSeek-R1 trust report; aideml trust report.