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DeepSeek-R1 vs Nemotron Nano 9B v2
Comprehensive side-by-side LLM comparison
DeepSeek-R1 is available on 5 providers. Both models have their strengths depending on your specific coding needs.
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DeepSeek
DeepSeek-R1 was developed as a reasoning-focused language model, designed to combine chain-of-thought reasoning with reinforcement learning techniques. Built to excel at complex problem-solving through trial-and-error learning and deliberate analytical processes, it demonstrates the power of efficient training methods in open-source model 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
DeepSeek
2025-01-20
Nemotron Nano 9B v2
NVIDIA
2025-08-18
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Performance Metrics
Context window and performance specifications
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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
5 providers
DeepInfra
Throughput: 0.9 tok/s
Latency: 0.3ms
DeepSeek
Throughput: 9 tok/s
Latency: 0.3ms
Fireworks
Throughput: 2.1 tok/s
Latency: 0.3ms
Together
Throughput: 4 tok/s
Latency: 0.6ms
ZeroEval
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DeepSeek-R1
Avg Score:0.0%
Providers:5
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Nemotron Nano 9B v2
Avg Score:0.0%
Providers:0