Side-by-side model comparison

Gemma-4-26B-A4B-NVFP4 vs Qwen3.6-27B-NVFP4

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

nvidia/Qwen3.6-27B-NVFP4

Benchmark score 98.50

Parameters 27.00B

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	Qwen3.6-27B-NVFP4	Difference
Benchmark average score	98.50	98.50	Equal
Parameter size	26.00B	27.00B	-1B (-3.8%)
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 nvidia/Qwen3.6-27B-NVFP4's 98.50.
nvidia/Qwen3.6-27B-NVFP4 is 3.8% larger in parameter capacity than nvidia/Gemma-4-26B-A4B-NVFP4 (27.00B vs 26.00B parameters).
nvidia/Gemma-4-26B-A4B-NVFP4 is also smaller, which makes its score advantage especially efficient.

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 (Qwen3.6-27B-NVFP4)

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("nvidia/Qwen3.6-27B-NVFP4")
model = AutoModelForCausalLM.from_pretrained("nvidia/Qwen3.6-27B-NVFP4")

Compare Alternative Models

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Comparison

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

nvidia/Qwen3.6-27B-NVFP4

Metric Comparison

Performance Verdict

Integration & Implementation Guide

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