Side-by-side model comparison

llama-4-maverick-17b-128e-instruct vs Gemma-4-26B-A4B-NVFP4

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

Model A

meta/llama-4-maverick-17b-128e-instruct

Benchmark score
Parameters N/A
Model family Meta
Dataset status Available
Model B

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

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

Metric Comparison

The table keeps the core specs visible for quick evaluation.

Live dataset
Metric llama-4-maverick-17b-128e-instruct Gemma-4-26B-A4B-NVFP4 Difference
Benchmark average score 98.50 -98.5 (-%)
Parameter size N/A 26.00B N/A
Model family Meta Gemma 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 meta/llama-4-maverick-17b-128e-instruct's .
  • Parameter size comparison is not available due to missing parameter metadata.

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("meta/llama-4-maverick-17b-128e-instruct")
model = AutoModelForCausalLM.from_pretrained("meta/llama-4-maverick-17b-128e-instruct")
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")

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