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Side-by-side model comparison

Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF vs dolphin-2.9.1-yi-1.5-34b

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

dphn/dolphin-2.9.1-yi-1.5-34b

Benchmark score 98.50
Parameters 34.00B
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 dolphin-2.9.1-yi-1.5-34b Difference
Benchmark average score 98.50 98.50 Equal
Parameter size 1.00B 34.00B -33B (-3300%)
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 dphn/dolphin-2.9.1-yi-1.5-34b's 98.50.
  • dphn/dolphin-2.9.1-yi-1.5-34b is 3300% larger in parameter capacity than Andycurrent/Gemma-3-1B-it-GLM-4.7-Flash-Heretic-Uncensored-Thinking_GGUF (34.00B 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 (dolphin-2.9.1-yi-1.5-34b) 
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("dphn/dolphin-2.9.1-yi-1.5-34b")
model = AutoModelForCausalLM.from_pretrained("dphn/dolphin-2.9.1-yi-1.5-34b")

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