The catch is up front: distills are weaker than the full R1, and picking the wrong size wastes either money or accuracy.

Which DeepSeek R1 Distill Should You Pick?

The distills come in sizes built on different base models — roughly 1.5B, 7B, 8B, 14B, 32B and 70B. Bigger means better reasoning and more hardware. A 7B or 8B runs on a modest GPU or a 16GB Mac; the 32B wants a 24GB GPU; the 70B wants serious VRAM.

How Do You Serve It?

1. Install Ollama for the simplest path — it pulls the distill with one command.
2. Choose a quantised build (4-bit) to fit your GPU.
3. Expose the local endpoint to your client or app.
4. Benchmark on your real tasks; measure tokens per second before deciding.

The reasoning traces still appear, so you keep the transparency that makes R1 useful — just at lower accuracy.

Where Do Distills Lose to the Full Model?

Hard maths, long multi-step logic and rare edge cases degrade first. A 32B distill is impressive for its size but will miss problems the full DeepSeek R1 solves. Use distills for privacy-sensitive, moderate-difficulty work; route the genuinely hard problems to the hosted full model or a stronger API.

Is Local Reasoning Worth It?

Yes when data cannot leave, or when you want an always-available reasoning tool with no per-call cost. No if you need top accuracy and have no privacy constraint — a hosted API is simpler and better. The honest rule: pick local for control, hosted for capability.

Key Takeaways

• R1 distills come in ~1.5B to 70B sizes; bigger needs more VRAM and reasons better.
• 7-8B runs on a laptop; 32B needs a 24GB GPU; 70B needs multi-GPU.
• Serve with Ollama or llama.cpp using 4-bit quantisation.
• Distills lose on hard maths and long logic — route those to the full model.

It is the model to reach for when a problem has steps, and to avoid when you just want a quick sentence.

What Makes DeepSeek R1 Different?

Most chat models hide their reasoning. R1 shows it, so you can catch a bad assumption before you act on the answer. That transparency is useful for debugging logic, checking a calculation, or learning how a conclusion was reached.

The cost of that transparency is latency. R1 thinks longer than a standard model, so replies take more time and you pay for the thinking tokens.

How Do You Get Good Results from R1?

1. State the final question and the constraints, then let it reason.
2. Ask it to verify its own answer: “Check the last step for errors.”
3. Use it for maths, logic, code design and debugging — not small talk.
4. Read the reasoning block; if a step is wrong, correct it and re-ask.

Do not flood it with trivia. Reasoning models shine on hard, multi-step problems and waste tokens on simple lookups.

Is DeepSeek R1 Free and Open?

Yes, in both senses. You can chat with it free on DeepSeek’s site with usage caps, and the model weights are open so you can self-host. The official API is paid but cheap; third-party providers host it too, at varying prices and quality. Exact rates move around, so compare before committing volume.

Where Does R1 Fall Short?

It over-thinks simple tasks, its visible reasoning can be verbose, and some deployments have stricter content handling than users expect. For creative writing and fast chat, a general model feels better. Use DeepSeek R1 as the specialist you call for the hard parts, not the assistant you chat with all day.

Key Takeaways

• DeepSeek R1 (Jan 2025) is an open reasoning model with visible chain-of-thought.
• Free to chat with caps; cheap paid API; open weights allow self-hosting.
• Best for maths, logic, code design and debugging — not small talk.
• Slower and more token-hungry than standard models by design.