Side-by-side model comparison

Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF vs SmolLM-1.7B-Instruct-quantized.w4a16

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

Model A

Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF

Benchmark score 98.50

Parameters 1.00B

Model family Gemma

Dataset status Available

Model B

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

Benchmark score 98.50

Parameters 1.70B

Model family Llama

Dataset status Available

Metric Comparison

The table keeps the core specs visible for quick evaluation.

Live dataset

Metric	Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF	SmolLM-1.7B-Instruct-quantized.w4a16	Difference
Benchmark average score	98.50	98.50	Equal
Parameter size	1.00B	1.70B	-0.7B (-70%)
Model family	Gemma	Llama	Different

Performance Verdict

Based on the available leaderboard data, Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF has the stronger overall benchmark score.

Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF is the stronger performer, scoring 98.50 on average compared to nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16's 98.50.
nm-testing/SmolLM-1.7B-Instruct-quantized.w4a16 is 70% larger in parameter capacity than Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF (1.70B vs 1.00B parameters).
Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF 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-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF)

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF")
model = AutoModelForCausalLM.from_pretrained("Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF")

Load Model B (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")

Compare Alternative Models

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Comparison

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Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF

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

Metric Comparison

Performance Verdict

Integration & Implementation Guide

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