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Side-by-side model comparison

NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 vs Qwen2-0.5B

Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.

Model A

nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4

Benchmark score 98.50
Parameters 120.00B
Model family Other
Dataset status Available
Model B

Qwen/Qwen2-0.5B

Benchmark score 98.50
Parameters 0.50B
Model family Qwen
Dataset status Available

Metric Comparison

The table keeps the core specs visible for quick evaluation.

Live dataset
Metric NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 Qwen2-0.5B Difference
Benchmark average score 98.50 98.50 Equal
Parameter size 120.00B 0.50B +119.5B (+23900%)
Model family Other Qwen Different

Performance Verdict

Based on the available leaderboard data, nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 has the stronger overall benchmark score.

  • nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 is the stronger performer, scoring 98.50 on average compared to Qwen/Qwen2-0.5B's 98.50.
  • nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 is 23900% larger in parameter capacity than Qwen/Qwen2-0.5B (120.00B vs 0.50B parameters).
  • nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4 has more parameter capacity, which may contribute to its stronger benchmark score.

Integration & Implementation Guide

Learn how to load and execute these models programmatically in Python using Hugging Face's transformers library.

Python tutorial
Load Model A (NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4) 
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4")
model = AutoModelForCausalLM.from_pretrained("nvidia/NVIDIA-Nemotron-3-Super-120B-A12B-NVFP4")
Load Model B (Qwen2-0.5B) 
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2-0.5B")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2-0.5B")

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