One hub for DeepSeek R1, V3, V3.1, V3.2-Exp, V2.5, Coder, Math and more. Download open weights, run them in the cloud or locally, and follow clear, step-by-step guides tailored for developers and teams in the US.
Pick the version that fits your workload—reasoning, general chat, coding or math. Each card links out to official weights (Hugging Face / GitHub / mirrors) plus quick docs on how to run the model.
Open-weight reasoning models with chain-of-thought style “thinking”. Available as full R1 and smaller distilled checkpoints (1.5B–70B).
Mixture-of-Experts models for everyday chat, tools and agents. V3.2-Exp adds sparse attention for efficient long-context inference.
Unified chat + coder model (V2.5) plus specialist Coder and Math models for repositories, algorithms, and proofs.
Whether you’re a solo developer or an enterprise team in the US, you can run DeepSeek in the cloud, locally on your own hardware, or embedded inside your app.
Get instant access through DeepSeek’s official web app and API, or partner platforms like Together AI / OpenRouter. No GPUs required.
Download open weights from GitHub / Hugging Face and run them with tools like Ollama, vLLM or LM Studio for full control.
Wire DeepSeek into your Python or JavaScript stack and frameworks like LangChain, LangGraph, or LlamaIndex.
Jump into the DeepSeek ecosystem with quick cards for each model, feature and entry point. Click any card to open the related guide or landing page.
DeepSeek is a new generation of open AI models built for fast, affordable and powerful reasoning, coding and chat. This site gives you guides, downloads and tutorials for the full DeepSeek ecosystem.
Explore DeepSeek →DeepSeek AI combines advanced language models, reasoning engines and coding assistants into one flexible platform. Run it in the cloud, locally or embedded inside your apps.
Learn more about DeepSeek AI →DeepSeek R1 is the reasoning-focused model for complex problem solving, multi-step analysis, math and advanced coding work. Use it when you need the AI to really “think”.
Read the DeepSeek R1 guide →DeepSeek V3 is a versatile general-purpose model for everyday chat, content creation, brainstorming and basic coding assistance. A great default for chatbots and copilots.
See DeepSeek V3 features →DeepSeek V3.1 improves on V3 with stronger instruction-following, more stable outputs and better real-world performance—ideal for production-grade agents and customer apps.
Discover DeepSeek V3.1 →DeepSeek V3.2 Exp (Experimental) pushes long-context and high-throughput inference, making it a fit for power users processing large docs, running advanced agents and heavy workloads.
Learn about V3.2 Exp →The DeepSeek family includes R1 for reasoning, the V3 series for general chat, plus specialist Coder and Math models. Pick the right model to match your use case.
Browse all DeepSeek models →DeepSeek Chat is the conversational interface powered by DeepSeek models. Use it for daily questions, coding help, research, content generation and more in your browser or app.
Open DeepSeek Chat overview →DeepSeek Reasoner refers to the reasoning-optimized models like R1 that can explain their thinking and solve multi-step problems for analysis, math-heavy work and complex debugging.
See how DeepSeek Reasoner works →The DeepSeek R1 model is the core reasoning engine in the lineup, shipped in multiple sizes and deployment options, with strong benchmark and real-world performance.
View DeepSeek R1 model details →A DeepSeek AI chatbot lets you embed DeepSeek models into your website, SaaS product or internal tools to answer questions, support users and automate workflows 24/7.
Build a DeepSeek AI chatbot →The DeepSeek web app is the easiest starting point—no installs, no GPUs. Log in, choose a model like R1 or V3, and start chatting while you explore DeepSeek’s capabilities.
Learn about the DeepSeek web app →Use DeepSeek login to access your account, manage API keys, monitor usage and switch between models. From there you can jump straight into chat or integrations.
Go to DeepSeek login info →DeepSeek download means grabbing the open-weight model files so you can run DeepSeek on your own hardware or private cloud. We link to official repos and provide setup guides.
View DeepSeek download options →Explore DeepSeek APIs, open-source models, GitHub repos and local runtimes. Click any card to open a focused guide or resource.
The DeepSeek API lets you call R1, V3 and other models from your own apps using simple HTTPS requests. Integrate chat, reasoning and coding into websites, SaaS products and internal tools.
Get started with the DeepSeek API →The DeepSeek API docs cover authentication, endpoints, model names, parameters and code samples in Python, JavaScript and more—your main reference when building with DeepSeek.
Open DeepSeek API documentation →The DeepSeek R1 API gives programmatic access to the R1 reasoning model for complex analysis, math, code review and multi-step workflows—ideal for agents and decision-support tools.
Read the DeepSeek R1 API guide →The DeepSeek V3 API is optimized for fast, general-purpose chat and content generation. Use it as your default model for chatbots, copilots and everyday AI features.
Explore DeepSeek V3 API examples →DeepSeek open-source releases let you run powerful LLMs on your own infrastructure with full control over data, latency and deployment.
Learn about DeepSeek open source →DeepSeek’s open-source models include R1, V3 variants, Coder, Math and more. Download checkpoints and host them in your own environment or cloud.
Browse DeepSeek open-source models →The DeepSeek R1 GitHub repo contains official code, configs and links to R1 model weights. Follow the instructions to set up training, inference and evaluation.
View DeepSeek R1 on GitHub →The DeepSeek V3 GitHub repository provides the reference implementation, scripts and model cards for V3 and its variants, ready to integrate or extend.
View DeepSeek V3 on GitHub →The DeepSeek Hugging Face page lists all published checkpoints, including R1, V3 and specialist models. Download them, use Transformers pipelines or deploy via hosted inference.
Open DeepSeek on Hugging Face →DeepSeek Ollama models let you run R1 or V3 locally with a single command, exposing a local REST API for desktop-friendly development.
See DeepSeek models for Ollama →Run DeepSeek R1 in Ollama to get a local reasoning model for coding, debugging and analysis on your own machine, with simple integration into tools.
Run DeepSeek R1 in Ollama →Running DeepSeek locally gives privacy and control. Learn about hardware requirements, downloading weights, choosing a runtime and exposing a local API.
Guide: Run DeepSeek locally →vLLM is a high-throughput inference engine that pairs well with DeepSeek models for serving many concurrent requests efficiently from GPU servers.
Configure DeepSeek with vLLM →A DeepSeek R1 Docker setup lets you containerize the reasoning model and deploy it consistently across development, staging and production.
Use DeepSeek R1 with Docker →DeepSeek R1 is available in sizes like 7B, 32B and 70B parameters. Choose smaller models for cheaper, faster inference or larger ones for maximum reasoning quality.
Compare DeepSeek R1 7B / 32B / 70B →The DeepSeek Coder model is tuned for software development: it writes, edits and explains code in multiple languages and can act as a coding copilot.
Learn about DeepSeek Coder →The DeepSeek Math model focuses on mathematical problem solving—from algebra and calculus to proofs—making it ideal for education and research tools.
Explore the DeepSeek Math model →Understand DeepSeek pricing, free vs paid options, enterprise plans and commercial-use rules. Use these cards to jump into focused guides for each topic.
Get a clear overview of DeepSeek pricing across core models like R1 and V3, including on-demand usage, monthly estimates and how costs compare to other AI providers.
View DeepSeek pricing overview →DeepSeek R1 pricing explains the cost of using the reasoning model for workloads such as analysis, math and advanced coding, with examples for different token volumes.
Check DeepSeek R1 pricing →DeepSeek API pricing covers per-token rates, model-specific costs and expected monthly spend for SaaS apps, internal tools and production deployments.
See DeepSeek API pricing →Learn how DeepSeek’s cost per token works, how tokens are counted and how to estimate expenses for prompts, responses and long-context tasks.
Understand cost per token →Find out what you can do with DeepSeek for free, including web access or limited API usage, and where the paid tiers start to apply.
Is DeepSeek free? Learn more →Compare free vs paid DeepSeek features: rate limits, model access, priority throughput, support options and recommended plans for different team sizes.
Compare free vs paid →The DeepSeek enterprise plan section explains custom pricing, SLAs, private deployments, security options and support for large organizations.
Explore enterprise options →DeepSeek business pricing breaks down recommended plans for startups, agencies and SMBs, with examples of typical monthly usage and budgets.
See business pricing guide →Learn what counts as commercial use of DeepSeek models, how it relates to your projects, and which plans and licenses you need for client or revenue-generating work.
Read about commercial use →This page explains DeepSeek’s licensing terms for commercial use, including open-source model licenses, attribution requirements and compliance best practices.
Check commercial-use license →Compare DeepSeek and DeepSeek R1 with leading AI models like GPT-4, GPT-5, OpenAI o1, Gemini, Claude, Perplexity and Mistral. Each card links to a focused head-to-head breakdown.
See how DeepSeek compares to OpenAI’s GPT family in quality, pricing, openness and use cases, with examples for developers, startups and enterprises.
Compare DeepSeek vs OpenAI →A side-by-side look at DeepSeek R1 versus GPT-4 for reasoning, coding, math and real project costs, including pros and cons for each.
See R1 vs GPT-4 analysis →Compare DeepSeek’s latest models to GPT-5 in capabilities, ecosystem, pricing and when you might choose open-source vs closed-source.
Compare DeepSeek vs GPT-5 →Learn when to use DeepSeek instead of ChatGPT for chatbots, agents, coding copilots and custom deployments on your own infrastructure.
DeepSeek vs ChatGPT breakdown →Compare DeepSeek R1 with OpenAI o1 on reasoning depth, chain-of-thought behavior, latency and cost for complex analytical workloads.
See R1 vs o1 comparison →DeepSeek vs Google Gemini across quality, multimodal features, pricing, integrations and which one fits different product and research scenarios.
Compare DeepSeek vs Gemini →A detailed look at DeepSeek and Anthropic’s Claude models for safety, long-context tasks, reasoning and enterprise usage.
DeepSeek vs Claude analysis →Understand when to use DeepSeek as a reasoning model and when Perplexity’s AI search engine is a better fit for research and browsing.
Compare DeepSeek vs Perplexity →Compare DeepSeek and Mistral in terms of open-source models, performance, deployment options and cost for self-hosted AI stacks.
DeepSeek vs Mistral guide →Decide when to pick the reasoning-focused R1 versus the general-purpose V3 models for chat, agents, coding and analytics.
See R1 vs V3 guide →Compare V3 and V3.1 to understand improvements in instruction following, stability and performance for production chatbots and tools.
Compare V3 vs V3.1 →DeepSeek V3.2 vs V3.1 for long-context, throughput and advanced workloads, with practical guidance on which model to deploy.
See V3.2 vs V3.1 breakdown →See how DeepSeek can power coding, analytics, SEO, content, startups and more. Each card links to a focused guide for that specific use case.
Use DeepSeek as a coding copilot to write, refactor and explain code across multiple languages, speeding up development and reducing bugs in your projects.
Use DeepSeek for coding →DeepSeek helps developers ship faster with code generation, debugging assistance, API design, documentation drafts and quick technical research—all from one AI stack.
DeepSeek for developers →Startups use DeepSeek to build MVPs, automate support, generate content and power in-product AI features while keeping costs lower than many closed models.
DeepSeek for startups →Turn raw text, tables and reports into insights. DeepSeek can summarize findings, explain metrics, generate hypotheses and help analysts explore data faster.
Data analysis with DeepSeek →Connect DeepSeek to dashboards and docs so business teams can ask natural-language questions, get executive summaries and uncover trends without digging through every report.
DeepSeek for BI teams →Use DeepSeek to generate keyword ideas, outlines, briefs and SEO-optimized drafts while still keeping human control over strategy and final content quality.
SEO with DeepSeek →DeepSeek can draft blog posts, landing pages, emails and social content in your brand voice, helping marketers move from blank page to first draft in minutes.
Content writing with DeepSeek →Embed a DeepSeek-powered chatbot on your site to answer FAQs, guide visitors, qualify leads and provide basic support around the clock.
Website chatbot with DeepSeek →Use DeepSeek’s reasoning capabilities for math tutoring, problem solving and step-by-step explanations across algebra, calculus and more advanced topics.
Math with DeepSeek →Turn DeepSeek into a full coding assistant that understands your codebase, suggests changes, writes tests and explains unfamiliar libraries or frameworks.
Build a coding assistant →Learn when it makes sense to use DeepSeek as an alternative to ChatGPT—for cost savings, open-weight deployments or more control over model behavior.
DeepSeek vs ChatGPT use cases →DeepSeek’s open models let you build a ChatGPT-style experience on infrastructure you control, combining open weights with your own data and guardrails.
Open-source ChatGPT alternative →Answer:
DeepSeek is a Chinese AI company that released a family of large language models, including DeepSeek-R1 (a reasoning-focused model) and the V3 / V3.1 general chat models. What made it blow up online is:
Very strong reasoning performance (often compared to OpenAI’s o1)
Much lower reported training/inference cost than Western competitors
Open-weight releases (R1 and others) under permissive licenses
This combination led to a ton of hype, media coverage, and even big market reactions when investors realized a cheaper, strong competitor had arrived.
Answer:
On Reddit, a lot of users report that:
R1 is excellent at logic puzzles, math, and step-by-step reasoning, sometimes beating OpenAI’s o1 on user-made tests.
Many devs say it’s very strong at coding, especially compared to smaller or more “fun” models.
But there are caveats:
Benchmarks can be cherry-picked.
Access (web/app/API) can be flaky due to “server busy” issues.
Some people feel its style or safety tuning is inconsistent.
So the community view is: “Yes, it’s very good, especially for reasoning & coding—just not perfect or magic.”
Answer:
Common points from forums and articles:
R1 vs o1:
R1’s reasoning is often described as “on par with o1” on many tasks, especially math and logic, and it emits visible “thinking” traces.
R1 is open-weight and MIT-licensed (distills), which is very attractive for self-hosters.
R1 vs GPT-4:
GPT-4 is still seen as more polished, especially in safety, style and “just works” UX.
R1 is praised for raw reasoning power and being free/open on some platforms.
The typical Reddit answer: If you want polished plus strong tools & ecosystem → GPT-4 / o1.
If you want open, cheap reasoning you can host or tinker with → R1.
Answer:
Community answers usually look like this:
Yes, partly:
Some providers (like OpenRouter’s deepseek-r1:free) offer R1 access at $0 per token with rate limits.
The open-weight models are free to download and run yourself (assuming you have the hardware).
Not completely free:
DeepSeek’s own API and app usually have paid tiers and limits.
Third-party providers charge for higher throughput, priority, or pro features.
So: Yes, you can use DeepSeek for free in some ways, but serious or high-volume use usually costs money.
Answer:
There’s a frequently-linked Reddit FAQ listing alternative hosts for R1 and other DeepSeek models. Commonly mentioned:
OpenRouter
Together AI
Perplexity (for R1-powered reasoning)
Azure, AWS and other cloud / MaaS integrations
These are popular because the official web/app sometimes returns “Server busy” or the API gets throttled when traffic spikes or under cyberattacks.
Answer:
Yes—this is one of the biggest draws in technical threads:
Full R1 is massive (hundreds of billions of parameters) and requires very large GPU/CPU setups.
Community guides show how to run quantized R1 variants (GGUF, etc.) with tools like vLLM, Ollama, LM Studio, or Unsloth.
Typical advice:
Use smaller distilled / quantized versions (e.g., 7B/14B/32B) if you’re on consumer GPUs.
For full-blown R1, expect data-center class hardware.
Answer:
On forums, people highlight two key points:
Distilled R1 models are MIT-licensed, which is about as permissive as it gets. You can distill, modify and commercialize under normal MIT conditions (no warranty, include license notice).
For hosted services (DeepSeek’s own API/app), your rights are also governed by DeepSeek’s Terms of Use, which cover data usage, rate limits, and other legal constraints.
Community summary:
Self-hosted open-weight R1 = generally safe for commercial projects (MIT), but still check each model card.
Hosted services = check the ToS carefully like you would with any SaaS.
Answer:
People often ask this because they’re worried about a foreign provider. Typical community answer:
For official cloud services, data handling is described in DeepSeek’s Terms & privacy policies—they may log interactions for safety, analytics or improvement, similar to other providers, with some opt-out options depending on service.
For self-hosted open-weight models, your data stays wherever you host it (your server, your laptop), so privacy is under your control.
Most technical users who are extremely privacy-sensitive choose self-hosting or run on their own cloud accounts.
Answer:
This is one of the hottest topics:
Some journalists and users report that the official Chinese-hosted chatbot can “self-censor in real time”—it starts answering and then replaces its text with a safe/vague response on sensitive political topics.
Others point out that open-weight versions (self-hosted or third-party) can be tuned with different safety layers, and may answer more freely.
On Reddit and other forums you’ll see two camps:
Critics: worried about Chinese state influence, censorship and propaganda in responses.
Supporters: say the model is great at coding/reasoning and that you can avoid censorship by self-hosting an uncensored variant.
Net conclusion in community threads:
“Official Chinese frontends are censored; open weights let you choose your own guardrails.”
Answer:
From Reddit and tech forums:
Reasons people love it:
Strong reasoning and coding performance.
Free/cheap access on some platforms.
Open weights + MIT license.
Reasons people dislike or distrust it:
Concerns about censorship and propaganda in Chinese-hosted versions.
Geopolitical worries (“AI cold war”, “China vs West”).
Fears that benchmarks or marketing claims might be exaggerated.
So the emotional split is part technical, part geopolitical.
Answer:
When DeepSeek took off, there was a huge market reaction:
Tech stocks lost around $1T in value, with Nvidia alone losing hundreds of billions in market cap after reports about DeepSeek’s ultra-low costs.
Some experts (like Meta’s Yann LeCun) said the panic was overblown and “unjustified”, arguing inference demand will still drive enormous hardware needs.
Forum consensus:
DeepSeek adds competition and pressure on prices, but it’s not going to make Western AI or GPU makers disappear.
It does, however, accelerate the race and push everyone to be more efficient.
Answer:
Common community recommendations:
Use R1 when you need:
Hard reasoning (math, puzzles, proofs)
Complex coding/debugging
Detailed chain-of-thought style explanations
Use V3 / V3.1 / V3.2 when you need:
General chat & assistants
Everyday content generation
Fast, cheaper throughput, especially via managed cloud (Vertex AI, etc.)
Many users mix them: V3.x as default conversational model + R1 for “deep thinking” calls.
Answer:
Common answers:
Official web/app – sign up on DeepSeek’s site or app stores (if available in your region). Expect occasional “server busy” messages.
Third-party hosts – OpenRouter, Perplexity, Vertex AI, etc., often with simple settings to switch models.
Self-hosting – download open weights from Hugging Face / GitHub, run with Ollama, vLLM, Unsloth or other frameworks.
Answer:
Yes—this is one of the strongest repeated themes:
Several Reddit devs say DeepSeek is “excellent at coding,” often rating it above some GPT variants or niche models in their personal tests.
Users like its direct, less-fluffy style and detailed explanations.
That said, coding quality can vary between R1, V3, Coder-specialized models and the host you use.
Answer:
From FAQs and long threads:
Reliability issues: frequent “server busy” on the official app/API during peaks.
Guardrails & censorship: especially on sensitive political topics when using Chinese-hosted frontends.
Hardware demands: full-size R1 is huge; you may need quantized variants or strong GPUs.
Rapid changes: version numbers, hosts, and pricing change fast, so old forum posts can go out of date quickly.
This openness fuels rapid innovation and customization.
Developers can:
Upcoming improvements include:
Using English improves context retention, logic accuracy, and response fluency.
DeepSeek is multilingual but performs best in English and Chinese.
DeepSeek can understand advanced terms in major non-English languages but may need clearer phrasing or translated prompts for precision.
Liang founded DeepSeek in 2023. He’s a former lead at top Chinese AI firms and studied advanced machine learning and NLP, giving him the vision to create a scalable, open AI ecosystem.