DE
DeepSeek
Language model · China ·Jan 2025

DeepSeek-R1-Zero

Language Open (Restricted)
671B
Parameters
4
Benchmarks

About DeepSeek-R1-Zero

DeepSeek-R1-Zero is an AI model developed by DeepSeek, in the language category, released in 2025, made available as an open-weights model with 671B params.

On this page you'll find DeepSeek-R1-Zero's full specifications, including 4 benchmark results. Review provider pricing and benchmark scores below, or compare DeepSeek-R1-Zero head-to-head with other language models.

Links

Run DeepSeek-R1-Zero locally — what it takes

DeepSeek-R1-Zero (671B) is too large to run on a single consumer or Apple machine. At Q4 its weights alone are about 376 GB — it needs a multi-GPU / datacenter setup:

  • ≈ 2× AMD Instinct MI325X
  • ≈ 3× NVIDIA B200

Lower quantization (Q3/Q2) or CPU + system-RAM offload (e.g. ktransformers) reduces the requirement at the cost of speed.

Software support

✓ measured · · compatible · — not supported. Informational only — speed is hardware-based.

Community

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Discussion

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DeepSeek-R1-Zero benchmark scores
Benchmark Category Score Variant
LiveCodeBench v5 code 50% cited-magistral
AIME 2024 math 71% cited-magistral
MATH-500 math 95.9% cited-magistral
GPQA Diamond reasoning 73.3% cited-magistral

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Frequently asked questions

What is DeepSeek-R1-Zero?

DeepSeek-R1-Zero is an AI model developed by DeepSeek, in the language category, released in 2025. It is tracked on GenAIList with its specifications, benchmark scores and provider pricing.

Who created DeepSeek-R1-Zero?

DeepSeek-R1-Zero was developed by DeepSeek and released in 2025.

Is DeepSeek-R1-Zero open source or proprietary?

DeepSeek-R1-Zero ships as an open-weights model: you can download and self-host the weights, though the licence may place some restrictions on use.

How much does DeepSeek-R1-Zero cost?

Pricing for DeepSeek-R1-Zero depends on the provider. See the providers table on this page for the latest API rates.

How does DeepSeek-R1-Zero perform on benchmarks?

DeepSeek-R1-Zero is benchmarked across 4 evaluations on GenAIList, including LiveCodeBench v5 (50%). See the full benchmark table below and compare it with other models.

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