Side-by-side model comparison

Qwen3-30B-A3B-Instruct-2507 vs Qwen3-Coder-30B-A3B-Instruct-GGUF

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

Model A

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

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

unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF

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 Qwen3-30B-A3B-Instruct-2507 Qwen3-Coder-30B-A3B-Instruct-GGUF 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, Qwen/Qwen3-30B-A3B-Instruct-2507 has the stronger overall benchmark score.

  • Qwen/Qwen3-30B-A3B-Instruct-2507 is the stronger performer, scoring 98.50 on average compared to unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF's 98.50.
  • Both models share the exact same parameter size of 30.00B parameters.
  • Qwen/Qwen3-30B-A3B-Instruct-2507 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, JavaScript/TypeScript, Go, Rust, C++, and PHP.

Integration code
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")
from transformers import AutoModelForCausalLM, AutoTokenizer

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

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