Side-by-side model comparison

diffusiongemma-26B-A4B-it-NVFP4 vs Qwen3-Coder-30B-A3B-Instruct-GGUF

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

Model A

nvidia/diffusiongemma-26B-A4B-it-NVFP4

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

unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF

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 diffusiongemma-26B-A4B-it-NVFP4 Qwen3-Coder-30B-A3B-Instruct-GGUF 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/diffusiongemma-26B-A4B-it-NVFP4 has the stronger overall benchmark score.

  • nvidia/diffusiongemma-26B-A4B-it-NVFP4 is the stronger performer, scoring 98.50 on average compared to unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF's 98.50.
  • unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF is 15.4% larger in parameter capacity than nvidia/diffusiongemma-26B-A4B-it-NVFP4 (30.00B vs 26.00B parameters).
  • nvidia/diffusiongemma-26B-A4B-it-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, JavaScript/TypeScript, Go, Rust, C++, and PHP.

Integration code
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("nvidia/diffusiongemma-26B-A4B-it-NVFP4")
model = AutoModelForCausalLM.from_pretrained("nvidia/diffusiongemma-26B-A4B-it-NVFP4")
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF")
model = AutoModelForCausalLM.from_pretrained("unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF")

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