Side-by-side model comparison

SmolLM-1.7B-Instruct-quantized.w4a16 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

nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16

Benchmark score 98.50

Parameters 1.70B

Model family Llama

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	SmolLM-1.7B-Instruct-quantized.w4a16	Gemma-4-26B-A4B-NVFP4	Difference
Benchmark average score	98.50	98.50	Equal
Parameter size	1.70B	26.00B	-24.3B (-1429.4%)
Model family	Llama	Gemma	Different

Performance Verdict

Based on the available leaderboard data, nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16 has the stronger overall benchmark score.

nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16 is the stronger performer, scoring 98.50 on average compared to nvidia/Gemma-4-26B-A4B-NVFP4's 98.50.
nvidia/Gemma-4-26B-A4B-NVFP4 is 1429.4% larger in parameter capacity than nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16 (26.00B vs 1.70B parameters).
nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16 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 (SmolLM-1.7B-Instruct-quantized.w4a16)

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16")
model = AutoModelForCausalLM.from_pretrained("nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16")

Load Model B (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")

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

nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16

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

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