Side-by-side model comparison

Qwen3-VL-30B-A3B-Instruct-AWQ vs Qwen3-Coder-30B-A3B-Instruct-FP8

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

Model A

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

Benchmark score 98.50
Parameters 30.00B
Model family Qwen
Dataset status Available
Model B

Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8

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 Qwen3-VL-30B-A3B-Instruct-AWQ Qwen3-Coder-30B-A3B-Instruct-FP8 Difference
Benchmark average score 98.50 98.50 Equal
Parameter size 30.00B 30.00B Equal
Model family Qwen Qwen Match

Performance Verdict

Based on the available leaderboard data, QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ has the stronger overall benchmark score.

  • QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ is the stronger performer, scoring 98.50 on average compared to Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8's 98.50.
  • Both models share the exact same parameter size of 30.00B parameters.
  • QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ has more parameter capacity, which may contribute to its stronger benchmark score.

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 (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")
Load Model B (Qwen3-Coder-30B-A3B-Instruct-FP8)
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8")

Compare Alternative Models

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