Use · DeepSeek-R1-Distill
Is it good enough?
Ownership levelSubstantialnone·limited·partial·substantial·fullAnalytical input C ยท 64.8/100
This page is a projection of the one entry record, the Reliability factor that Use covers. The full verdict is set by all four factors together, floor-weighted so the weakest caps the whole.
Which domain expands which factor
- AssessUse & modify + Transparency
- ImplementData control + Reliability
- UseReliability
- SupportTransparency
Capabilities & modalities
Text reasoning models (maths, coding, analysis) distilled from R1 into dense 1.5B-32B
weights. They emit explicit reasoning traces (think tags) and are strong for their size,
especially the 14B and 32B.
Context window & long-context behaviour
128K-token context per the cards (inherited from the Qwen2.5 bases). OneHill has not
independently measured effective long-context recall, so treat it as the declared window.
Ship a chat template with the R1 reasoning / think-tag convention. Use apply_chat_template
and handle the reasoning segment at the client - decide whether to expose or strip it.
Language coverage
English and Chinese are strongest (Qwen2.5 base plus R1 distillation); per-language depth
varies with model size.
These are reasoning distils; native structured tool-calling is not the primary documented
behaviour and was not grounded this session. Confirm per serving stack and size, and test.
Structured / JSON-constrained output
No model-documented native JSON/schema-constrained output guarantee. Constrained decoding is
available at the serving layer (vLLM/SGLang grammars), a runtime feature of the server.
How this scores
The ownership factor this domain covers, drawn from the one entry record.
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.
What this means for adoptionYou 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.
Sources
The same evidence records as the entry sheet. Read means the text was verified; unverified means it is known to exist but not yet read.
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.