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Comparison

DeepSeek-R1 vs CodeGen

Verdict

Pick DeepSeek-R1 if deepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use; pick CodeGen if codeGen is a series of open-source large language models designed for program synthesis. Trained on TPUs, CodeGen offers several versions with varying capabilities from basic code generation to advanced infill sampling.

Markdown twin · DeepSeek-R1 alternatives · CodeGen alternatives

GraphCanon updated today

DeepSeek-R1 logo

DeepSeek-R1

deepseek-ai/DeepSeek-R1

★ 92kpushed Jun 27, 2025
vs
CodeGen logo

CodeGen

salesforce/CodeGen

★ 5.2kpushed Jun 2, 2026

Trust & integrity

SignalDeepSeek-R1CodeGen
Maintenance
Dormant (379d since push)
As of today · github_public_v1
Steady (39d 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 1d · none
No lockfile
As of today · none
DeepSeek-R1 trust reportCodeGen trust reportTrust methodology

Tagline

DeepSeek-R1
Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.
CodeGen
Family of open-source models for program synthesis.

Stars

DeepSeek-R1
92k
CodeGen
5.2k

Forks

DeepSeek-R1
12k
CodeGen
423

Open issues

DeepSeek-R1
45
CodeGen
48

Language

DeepSeek-R1
-
CodeGen
Python

Adopt for

DeepSeek-R1
DeepSeek-R1 provides a set of distilled LLMs from Qwen and LLaMA series that support commercial use.
CodeGen
CodeGen is a series of open-source large language models designed for program synthesis. Trained on TPUs, CodeGen offers several versions with varying capabilities from basic code generation to advanced infill sampling.

Persona

DeepSeek-R1
-
CodeGen
-

Runtime

DeepSeek-R1
-
CodeGen
-

License

DeepSeek-R1
MIT
CodeGen
Apache-2.0

Last pushed

DeepSeek-R1
Jun 27, 2025
CodeGen
Jun 2, 2026

Categories

DeepSeek-R1
LLM Frameworks, Model Training
CodeGen
LLM Frameworks, Model Training

Trust and health

Maintenance

DeepSeek-R1
Dormant (18%)
CodeGen
Steady (60%)

Days since push

DeepSeek-R1
379d
CodeGen
39d

Open issues (now)

DeepSeek-R1
45
CodeGen
48

Full report

DeepSeek-R1
Trust report
CodeGen
Trust report

Choose DeepSeek-R1 if…

  • License: DeepSeek-R1 is MIT, CodeGen 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: 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 CodeGen if…

  • License: CodeGen is Apache-2.0, DeepSeek-R1 is MIT.
  • Tags unique to CodeGen: codex, generativemodel, languagemodel, llm.
  • When you require high-performance model training and code generation that matches or exceeds the performance of OpenAI Codex on specific tasks

When NOT to use CodeGen

  • In scenarios where the model's primary use is not centered around code generation or program synthesis, as its specialized training may limit its effectiveness for other types of generative tasks
  • If your project strictly requires a smaller memory footprint or simpler deployment because advanced models like CodeGen2.5 require significant computational resources and setup
GitHubDetails

Explore

DeepSeek-R1 trust report →CodeGen 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 · CodeGen 5.2k (synced Jul 12, 2026).

Common questions

What is the difference between DeepSeek-R1 and CodeGen?
DeepSeek-R1: Repository contains distilled LLM models derived from Qwen and LLaMA series for various commercial uses.. CodeGen: Family of open-source models for program synthesis.. See the comparison table for live GitHub stats and shared categories.
When should I choose DeepSeek-R1 over CodeGen?
Choose DeepSeek-R1 over CodeGen when License: DeepSeek-R1 is MIT, CodeGen 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: 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 CodeGen over DeepSeek-R1?
Choose CodeGen over DeepSeek-R1 when License: CodeGen is Apache-2.0, DeepSeek-R1 is MIT; Tags unique to CodeGen: codex, generativemodel, languagemodel, llm; When you require high-performance model training and code generation that matches or exceeds the performance of OpenAI Codex on specific tasks.
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 CodeGen?
In scenarios where the model's primary use is not centered around code generation or program synthesis, as its specialized training may limit its effectiveness for other types of generative tasks If your project strictly requires a smaller memory footprint or simpler deployment because advanced models like CodeGen2.5 require significant computational resources and setup
Is DeepSeek-R1 or CodeGen more popular on GitHub?
DeepSeek-R1 has more GitHub stars (91,991 vs 5,177). Stars measure visibility, not whether either tool fits your constraints.
Are DeepSeek-R1 and CodeGen open source?
Yes - both are open-source projects on GitHub (DeepSeek-R1: MIT, CodeGen: Apache-2.0).
Where can I find alternatives to DeepSeek-R1 or CodeGen?
GraphCanon lists graph-backed alternatives at DeepSeek-R1 alternatives and CodeGen alternatives (DeepSeek-R1 markdown twin, CodeGen 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 CodeGen?
DeepSeek-R1: Dormant. CodeGen: 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 CodeGen?
GraphCanon publishes per-repo trust reports with dated maintenance, provenance, and scan summaries: DeepSeek-R1 trust report; CodeGen trust report.