DeepSeek R1 (671B reasoning)

Positioning

DeepSeek R1 is the o1-equivalent open-weight model — explicit reasoning training, visible chain-of-thought, state-of-the-art on math and competitive programming benchmarks. Same MoE architecture as V3, same workstation-class hardware requirement.

Strengths

• Reasoning ceiling matches closed frontier models — true o1-class on hard math and code planning.
• Fully open weights — uniquely valuable in the reasoning space where most leaders are closed.
• Clean MIT-style license.

Limitations

• Workstation hardware required — same ~380 GB footprint as V3.
• Verbose chain-of-thought consumes lots of tokens.
• Distill versions exist (R1 Distill 70B, 32B, 14B, 7B) — those are the practical local picks.

Real-world performance on RTX 4090

• Direct R1 Q4_K_M (~380 GB) — workstation only, same as V3
• Practical local path: run R1 Distill Llama 70B or R1 Distill Qwen 32B (much more accessible)

Should you run this locally?

Yes, for workstation owners — same hardware story as V3. No, for consumer hardware — pick the R1 Distill variants instead, which deliver most of the reasoning quality at viable hardware costs.

How it compares

• vs DeepSeek V3 → R1 is the reasoning specialist, V3 is the generalist. Different jobs.
• vs DeepSeek R1 Distill Llama 70B → Distill is much more accessible (single 4090 with offload) and captures most of the reasoning lift. Default pick for local hardware.
• vs QwQ 32B → QwQ is the reasoning specialist that fits on a single 4090; R1 has higher ceiling.
• vs OpenAI o1 → R1 is the open-weight equivalent; quality competitive on math/code.

Run this yourself

# For local hardware, prefer the distills:
ollama pull deepseek-r1:70b-distill-llama-q4_K_M
ollama pull deepseek-r1:32b-distill-qwen-q4_K_M

Direct R1 settings: Q4_K_M, multi-GPU, A100/H100 cluster