Side-by-side model comparison

Gemma-4-26B-A4B-NVFP4 vs Qwen3-4B-Instruct-2507-FP8

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

Model A

nvidia/Gemma-4-26B-A4B-NVFP4

Benchmark score 98.50
Parameters 26.00B
Model family Gemma
Dataset status Available
Model B

Qwen/Qwen3-4B-Instruct-2507-FP8

Benchmark score 98.50
Parameters 4.00B
Model family Qwen
Dataset status Available

Metric Comparison

The table keeps the core specs visible for quick evaluation.

Live dataset
Metric Gemma-4-26B-A4B-NVFP4 Qwen3-4B-Instruct-2507-FP8 Difference
Benchmark average score 98.50 98.50 Equal
Parameter size 26.00B 4.00B +22B (+550%)
Model family Gemma Qwen Different

Performance Verdict

Based on the available leaderboard data, nvidia/Gemma-4-26B-A4B-NVFP4 has the stronger overall benchmark score.

  • nvidia/Gemma-4-26B-A4B-NVFP4 is the stronger performer, scoring 98.50 on average compared to Qwen/Qwen3-4B-Instruct-2507-FP8's 98.50.
  • nvidia/Gemma-4-26B-A4B-NVFP4 is 550% larger in parameter capacity than Qwen/Qwen3-4B-Instruct-2507-FP8 (26.00B vs 4.00B parameters).
  • nvidia/Gemma-4-26B-A4B-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 (Gemma-4-26B-A4B-NVFP4)
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("nvidia/Gemma-4-26B-A4B-NVFP4")
model = AutoModelForCausalLM.from_pretrained("nvidia/Gemma-4-26B-A4B-NVFP4")
Load Model B (Qwen3-4B-Instruct-2507-FP8)
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-4B-Instruct-2507-FP8")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-4B-Instruct-2507-FP8")

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