Side-by-side model comparison

Llama-3.1-8B vs Qwen3-30B-A3B-Instruct-2507

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

Benchmark score 98.50

Parameters 8.00B

Model family Llama

Dataset status Available

Model B

Qwen/Qwen3-30B-A3B-Instruct-2507

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	Qwen3-30B-A3B-Instruct-2507	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 has the stronger overall benchmark score.

meta-llama/Llama-3.1-8B is the stronger performer, scoring 98.50 on average compared to Qwen/Qwen3-30B-A3B-Instruct-2507's 98.50.
Qwen/Qwen3-30B-A3B-Instruct-2507 is 275% larger in parameter capacity than meta-llama/Llama-3.1-8B (30.00B vs 8.00B parameters).
meta-llama/Llama-3.1-8B 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)

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("meta-llama/Llama-3.1-8B")
model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-3.1-8B")

Load Model B (Qwen3-30B-A3B-Instruct-2507)

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-30B-A3B-Instruct-2507")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-30B-A3B-Instruct-2507")

Compare Alternative Models

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Comparison

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meta-llama/Llama-3.1-8B

Qwen/Qwen3-30B-A3B-Instruct-2507

Metric Comparison

Performance Verdict

Integration & Implementation Guide

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