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DeepSeek R1 Zero vs Nemotron Nano 9B v2
Comprehensive side-by-side LLM comparison
Nemotron Nano 9B v2 leads with 4.6% higher average benchmark score. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-R1-Zero was introduced as an experimental variant trained with minimal human supervision, designed to develop reasoning patterns through self-guided reinforcement learning. Built to explore how models can discover analytical strategies independently, it represents research into autonomous reasoning capability development.
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NVIDIA
Nemotron Nano 9B v2 is a language model developed by NVIDIA. It achieves strong performance with an average score of 77.0% across 6 benchmarks. It excels particularly in MATH-500 (97.8%), IFEval (90.3%), AIME 2025 (72.1%). It's licensed for commercial use, making it suitable for enterprise applications. Released in 2025, it represents NVIDIA's latest advancement in AI technology.
7 months newer
DeepSeek R1 Zero
DeepSeek
2025-01-20
Nemotron Nano 9B v2
NVIDIA
2025-08-18
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Average performance across 3 common benchmarks
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DeepSeek R1 Zero
Average Score:73.1%
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Nemotron Nano 9B v2
Average Score:77.6%(+4.6%)
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Knowledge Cutoff
Training data recency comparison
Nemotron Nano 9B v2
2024-09
More recent knowledge cutoff means awareness of newer technologies and frameworks
Provider Availability & Performance
Available providers and their performance metrics
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DeepSeek R1 Zero
0 providers
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Nemotron Nano 9B v2
0 providers
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DeepSeek R1 Zero
Avg Score:73.1%
Providers:0
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Nemotron Nano 9B v2
Avg Score:77.6%(+4.6%)
Providers:0