Side-by-side model comparison

Gemma-4-26B-A4B-NVFP4 vs Qwen3-30B-A3B-Instruct-2507

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/Qwen3-30B-A3B-Instruct-2507

Benchmark score 98.50

Parameters 30.00B

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	Qwen3-30B-A3B-Instruct-2507	Difference
Benchmark average score	98.50	98.50	Equal
Parameter size	26.00B	30.00B	-4B (-15.4%)
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/Qwen3-30B-A3B-Instruct-2507's 98.50.
Qwen/Qwen3-30B-A3B-Instruct-2507 is 15.4% larger in parameter capacity than nvidia/Gemma-4-26B-A4B-NVFP4 (30.00B 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 (Qwen3-30B-A3B-Instruct-2507)

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B-Instruct-2507")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-30B-A3B-Instruct-2507")

Compare Alternative Models

Explore nearby pairings from the same model dataset.

Comparison

Need This In Production?

I can help with model hosting, quantization, API integration, RAG systems, and production rollout.

Request Integration Help Email Me

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

Qwen/Qwen3-30B-A3B-Instruct-2507

Metric Comparison

Performance Verdict

Integration & Implementation Guide

Compare Alternative Models

Qwen3-0.6B vs Qwen3-8B

Qwen3-0.6B vs opt-125m

Qwen3-0.6B vs gpt2

Qwen3-0.6B vs tiny-Qwen2ForCausalLM-2.5

Need This In Production?

Abhishek's AI Clone