Model
DeepSeek-R1
Publisher
DeepSeek (China)
You substantially own self-hosted DeepSeek-R1: MIT permits commercial use, modification and distillation, and self-hosting keeps your data yours, so use-and-modify and data-control are both strong - ownership is substantial. It stops short of full because the training corpus and code are closed (not reproducible), the weights carry China-aligned filtering you cannot inspect, and there is no first-party guard model. Deploy behind your own guardrails, and treat EU high-stakes use as needing a self-assembled compliance package.
Do you really own it?
Substantial
none·limited·partial·substantial·full
Analytical input: AOI C · 64.4/100
The four ownership factors
Floor-weighted, not averaged. Nothing is weak and both use & modify and data control are strong, so the floor is high; transparency sits at moderate, which is what keeps it short of full.
1
Use and modify freelyCan you run, modify and adapt it with no gate and no field-of-use trap?
StrongDeepSeek-R1 is MIT-licensed (OSI, permissive, ungated), with commercial use, any modifications and derivative works, and distillation expressly permitted - a clean permissive grant, so use-and-modify is strong.
How this scores (AOI sub-dimensions)
Openness3/5how much is released - weights, data, code, licence - and how freelyOpen-weights tier: MIT-licensed weights and open documentation via a detailed technical report, but the training data is closed, the training code only partial, and evaluation partial.
Legal2/5how permissive and clean the licence is for real commercial useThe MIT licence is a genuine plus and the cleanest in the DeepSeek family, but for EU compliance it is outweighed by the gaps: no Article 55 documentation, no copyright policy, no training-content summary, and at 671B the systemic-risk exemption may be void - a concrete compliance gap for a downstream deployer, despite the permissive licence.
2
TransparencyDo you know what it is: weights, training, behaviour, and legible terms?
ModerateWeights are inspectable and there is a detailed technical report, but the training data and code are closed, and the weights carry China-aligned topic filtering you cannot inspect - open_weights, so moderate.
How this scores (AOI sub-dimensions)
Provenance3/5how well we can trace and verify what went into the modelVerified deepseek-ai org on Hugging Face, safetensors distribution with checksums and a clear canonical source with no malicious-checkpoint incident (checklist ~5/8).
Governance3/5how accountable and well-documented the publisher isActive, named publisher (DeepSeek) with a verified org and a track record of technical reports, meeting the score-3 anchor.
3
ReliabilityIs it reliable and good enough for the job?
StrongPerformance 4, operational 5 and safety 3: a strong reasoner with first-class serving; all three at or above 3 with two at or above 4, so reliability is strong. The caveat is the absence of a first-party guard model.
How this scores (AOI sub-dimensions)
Operational5/5how practical it is to run, serve and maintain in productionFirst-class ecosystem support: broad serving across vLLM, SGLang, llama.cpp and Ollama, an extensive family of community quantizations and distils, and wide third-party hosting shortly after release.
Safety3/5whether misuse risks are evaluated and guardrails are providedReleased as reasoning variants with documented behaviour, meeting the score-3 anchor, but safety tuning is lighter than Western frontier labs, the model exhibits China-aligned topic censorship, and no first-party guard model ships - deployers must add their own guardrails.
4
Doesn't extract your dataDoes running it keep your knowledge and data yours?
StrongSelf-hosted, the MIT weights run entirely on your own infrastructure with no telemetry and no licence claw-back, so your data stays yours. (The hosted DeepSeek API is a separate matter with its own data terms.)
How this scores
Not a scored AOI dimension. For a self-hosted model, data-control is a structural property of running the weights yourself, strong by default unless the model phones home or the licence claws back rights. For a hosted API this factor is the retention + train-on-inputs + residency read, scored from the binding terms.
How the AOI score is computed
The seven dimensions above, each scored 0 to 5, weighted and summed to the 0 to 100 headline. The score is the analytical input behind the ownership verdict, not the verdict itself.
DimensionScoreWeightPoints
Openness3/50.1810.8
Provenance3/50.169.6
Legal2/50.166.4
Safety3/50.169.6
Performance4/50.1411.2
Operational5/50.1212.0
Governance3/50.084.8
HeadlineC · 64.4/100
Sources
Every rating traces to a primary document. Read means the text was verified; unverified means it is known to exist but has not yet been read.
DocumentWhat it grounds
Licenceread2026-08-03
DeepSeek-R1 README, read: "This code repository and the model weights are licensed under the MIT License.
Model cardread2026-08-03
DeepSeek-R1 model card on the verified deepseek-ai HF org: 671B total / 37B active MoE, 128K context, safetensors; R1-0528 is a later revision "also subject to MIT License"; first-class serving (vLLM, SGLang, llama.cpp, Ollama) and an extensive community quant/distil ecosystem.
Third-party analysisunverified2026-08-03
On independent public leaderboards DeepSeek-R1 is among the strongest open-weight reasoning models (maths, coding, analysis); not OneHill-reproduced this session.
Third-party analysisunverified2026-08-03
Independent analysis notes DeepSeek open-weight models apply China-aligned content filtering on politically sensitive topics, with lighter safety tuning than Western frontier labs and no companion guard model.
Third-party analysisunverified2026-08-03
No public EU AI Act training-content summary, copyright policy, or GPAI documentation package is published for DeepSeek-R1, and the training corpus is not released.