Side-by-side model comparison

Llama-3.1-8B-Instruct vs Qwen3-VL-30B-A3B-Instruct-AWQ

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

Model A

meta-llama/Llama-3.1-8B-Instruct

Benchmark score 98.50
Parameters 8.00B
Model family Llama
Dataset status Available
Model B

QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ

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 Llama-3.1-8B-Instruct Qwen3-VL-30B-A3B-Instruct-AWQ Difference
Benchmark average score 98.50 98.50 Equal
Parameter size 8.00B 30.00B -22B (-275%)
Model family Llama Qwen Different

Performance Verdict

Based on the available leaderboard data, meta-llama/Llama-3.1-8B-Instruct has the stronger overall benchmark score.

  • meta-llama/Llama-3.1-8B-Instruct is the stronger performer, scoring 98.50 on average compared to QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ's 98.50.
  • QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ is 275% larger in parameter capacity than meta-llama/Llama-3.1-8B-Instruct (30.00B vs 8.00B parameters).
  • meta-llama/Llama-3.1-8B-Instruct 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 (Llama-3.1-8B-Instruct)
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B-Instruct")
Load Model B (Qwen3-VL-30B-A3B-Instruct-AWQ)
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ")
model = AutoModelForCausalLM.from_pretrained("QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ")

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