Model
DeepSeek-R1-Distill
Publisher
DeepSeek (China)
Licence
Apache License 2.0
You substantially own these self-hosted Apache-2.0 R1-Distill (Qwen) models: a clean permissive licence makes use-and-modify strong, and self-hosting - including on-device at the small sizes - keeps your data yours, so data-control is strong too. Ownership is substantial, a step above the Llama-base distils. It stops short of full because the distillation data and code are closed (not reproducible) and the weights inherit R1's China-aligned filtering and carry no safety tuning of their own - deploy behind your own guardrails.
Do you really own it?
Substantial
none·limited·partial·substantial·full
Analytical input: AOI C · 64.8/100
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?
StrongApache-2.0 (OSI, permissive, patent grant, ungated) inherited from the Qwen2.5 bases, with commercial use and any modification permitted and no field-of-use limit - a clean permissive grant, so use-and-modify is strong. A step above the Llama-base distils.
How this scores (AOI sub-dimensions)
Openness3/5how much is released - weights, data, code, licence - and how freelyOpen-weights tier: Apache-2.0 weights and open documentation, but the distillation data is closed, training code partial, evaluation partial.
Legal3/5how permissive and clean the licence is for real commercial useCleaner than the 671B family models: Apache-2.0 (OSI, permissive, patent grant) and, being small, not systemic-risk - so the open-source exemption applies to the transparency duties.
2
TransparencyDo you know what it is: weights, training, behaviour, and legible terms?
ModerateWeights are inspectable under Apache-2.0, but the distillation data and code are closed and the weights inherit R1's 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 with checksums, clear canonical source, no malicious-checkpoint incident (checklist ~5/8).
Governance3/5how accountable and well-documented the publisher isActive, named publisher (DeepSeek) with a verified org and technical reports, meeting the score-3 anchor.
3
ReliabilityIs it reliable and good enough for the job?
StrongPerformance 3, operational 5 and safety 3: solid reasoning-for-size with the easiest serving in the family; all three at or above 3, operational at 5, so reliability is strong. The caveat is the absence of own safety tuning / a first-party guard model.
How this scores (AOI sub-dimensions)
Operational5/5how practical it is to run, serve and maintain in productionThe easiest DeepSeek weights to run: small dense models with first-class serving (vLLM, SGLang, llama.cpp, Ollama), the most widely mirrored and quantized DeepSeek artifacts, deployable on consumer hardware.
Safety3/5whether misuse risks are evaluated and guardrails are providedDocumented reasoning behaviour meeting the score-3 anchor, but these are research distils with no safety tuning of their own, inheriting R1's 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 Apache-2.0 weights run entirely on your own infrastructure - including on-device at the small sizes - with no telemetry and no claw-back, so your data stays yours.
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
Legal3/50.169.6
Safety3/50.169.6
Performance3/50.148.4
Operational5/50.1212.0
Governance3/50.084.8
HeadlineC · 64.8/100
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 + the distil checkpoint cards, read: the R1-Distill-Qwen checkpoints are built on Qwen2.5 bases (1.5B/7B/14B/32B) and "are derived from ...
Model cardread2026-08-03
R1-Distill-Qwen model cards on the verified deepseek-ai HF org: dense 1.5B/7B/14B/32B models distilled from R1, 128K context (from the Qwen2.5 bases), safetensors; the most widely mirrored/quantized DeepSeek artifacts, serving on consumer hardware via Ollama, llama.cpp, vLLM, SGLang.
Third-party analysisunverified2026-08-03
On public leaderboards the R1-Distill-Qwen-14B/32B are notably strong reasoning models for their size; the 1.5B is modest.
Third-party analysisunverified2026-08-03
The R1-Distill checkpoints are research distils with no safety tuning of their own and inherit R1's China-aligned content filtering; no companion guard model ships.
Third-party analysisunverified2026-08-03
The R1-Distill-Qwen checkpoints are small (1.5B-32B), well below the systemic-risk threshold; their Apache-2.0 licence is FOSS, so the open-source exemption applies to the transparency duties, but DeepSeek publishes no copyright policy or training-content summary.