Who we are, why we built an independent reference on the DeepSeek model family, how we select and vet content, and what this site explicitly does not do.
An independent editorial team built this reference to give developers, researchers, and product teams a single organised resource on the DeepSeek model family without the noise of a product page or the sparsity of a single model card.
deepseek.gr.com is operated by an editorial team with a background in developer documentation and open-weight AI research coverage. We have no commercial relationship with the DeepSeek lab, no equity stake in any model-serving business, and no affiliate arrangement with any inference provider. The site earns no revenue from lead generation. Those facts matter because they are the structural reason this reference can take positions on model quality, license scope, and deployment trade-offs without pulling punches toward one commercial outcome.
The immediate trigger for building the site was the release cadence of the family. Between 2024 and 2026, the lab shipped general-purpose chat generations, reasoning-tuned R1 variants, code-specialised Coder releases, and parameter sweeps from small laptop builds to frontier-class flagships in close succession. No single existing resource tracked all of that in plain, reader-first prose. GitHub repositories and Hugging Face model cards are authoritative but terse; the broader tech press covers individual releases but rarely the cumulative picture. This reference fills that gap.
Everything here is derived from public, verifiable sources; nothing is paywalled, speculative, or drawn from non-public model access.
Our sourcing methodology is straightforward: we work from published model cards on Hugging Face, papers posted to arXiv, the official GitHub repositories, public benchmark leaderboards, and the upstream team's published blog posts and technical reports. Where a claim can be traced to one of those primary sources, we include it with the appropriate context. Where it cannot, we either frame it as a hypothesis or leave it out entirely.
We do not run private capability evaluations. We do not interview upstream employees under NDA. We do not reproduce content from any paywalled source. When we describe benchmark performance, the numbers come from published evaluation suites that any reader can independently verify — MMLU, HumanEval, MATH, or similar. Guidance from the NIST AI Risk Management Framework informs how we frame risk-adjacent topics such as safety, alignment posture, and supply-chain considerations.
The scope boundary is the publicly documented DeepSeek family: V3, R1, Coder, the API surface, the chat interface, the mobile app, the GitHub presence, and the surrounding third-party ecosystem. We do not cover unannounced variants, rumoured releases, or internal roadmap items. When the lab publishes something new, we update the relevant pages on this site within a reasonable editorial cycle.
A wiki accumulates contributions without editorial ownership; a forum thread buries the answer under replies. This reference aims to be the structured middle ground: maintained by a consistent editorial voice, readable in one sitting, and updated when the upstream picture changes.
The open-weight AI ecosystem has a content quality problem that is distinct from quantity. There is a lot of content, but most of it is either highly technical and audience-specific, or it is a quick news piece that will be stale in a month. A reader who needs to understand the trade-off between self-hosting a DeepSeek Coder variant and using the API for a production code-review pipeline does not need ten articles about the initial release; they need one well-structured reference page that has been maintained as the pricing, rate limits, and model quality have evolved.
That is the editorial bet this site makes. Each page covers a topic at the depth appropriate to a reader making a real decision, cites the load-bearing sources, and is revised when the source material changes. The AI.gov responsible AI guidance is one of the external frameworks we keep on hand when framing governance-adjacent topics, not because we are writing compliance documentation, but because those frameworks surface the questions practitioners should be asking before a production deployment.
This site is an independent, non-commercial reference on the DeepSeek AI model family. Sources are public and verifiable. We do not host weights, proxy inference, or reproduce paywalled content. The editorial team has no affiliation with the upstream DeepSeek research group.
Five principles govern what goes in, what stays out, and how we handle uncertainty when the upstream picture is ambiguous.
| Editorial principle | What it means in practice | What it excludes |
|---|---|---|
| Public-sources-only sourcing | Every factual claim traces to a published model card, arXiv paper, official repo, or public leaderboard. | NDA-covered briefings, private API access data, rumoured roadmap items, and paywalled research. |
| No weight hosting or proxying | We describe where weights live and how to access them; we do not serve model files or inference endpoints ourselves. | Direct weight downloads, inference-proxy URLs, and any arrangement that routes live requests through this domain. |
| Uncertainty surfacing | When a topic is genuinely contested or unclear in the public record, we say so and frame it as a hypothesis rather than a verdict. | Confident claims on topics where the primary sources are inconsistent or incomplete. |
| Commercial independence | We carry no affiliate links, no sponsored placements, and no revenue arrangement tied to user decisions about which model or provider to choose. | Affiliate codes, referral arrangements, and sponsored content from any model provider or inference platform. |
| Timely revision | Pages are reviewed and updated when the upstream model family ships a new generation or when a benchmark result materially changes the picture. | Static content that silently ages; pages that describe a model generation as current after a successor has shipped. |
Four questions that readers most often ask before trusting a third-party reference on a fast-moving AI model family.
deepseek.gr.com is owned and operated by an independent editorial team with no affiliation to the DeepSeek AI research group or any commercial model-serving provider. The site carries no sponsored content and earns no revenue from affiliate links or referral arrangements. It was built to provide a structured, maintained reference for developers and researchers evaluating the DeepSeek family.
We include content that helps readers make informed decisions about the DeepSeek family. Our sourcing methodology relies entirely on publicly available materials: official model cards on Hugging Face, published arXiv papers, GitHub repositories, and verified public benchmarks. We do not reproduce paywalled material, private API data, or claims that cannot be traced to a verifiable public source.
No. Every benchmark figure cited here comes from publicly published evaluations: official model cards, open leaderboards such as the Open LLM Leaderboard, or peer-reviewed papers. We do not run private capability evaluations against unreleased model versions or non-public API endpoints. Where numbers differ across sources, we note the discrepancy and link to both.
No. This site does not host, proxy, distribute, or link to downloadable model weight files. For weights, the canonical source is the official Hugging Face repository maintained by the upstream team. We describe where weights can be found and how to use common inference engines to load them, but we do not serve the files ourselves.