Side-by-side model comparison

llama-guard-4-12b 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

meta/llama-guard-4-12b

Benchmark score
Parameters N/A
Model family Meta
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 llama-guard-4-12b Qwen3-Coder-30B-A3B-Instruct-FP8 Difference
Benchmark average score 98.50 -98.5 (-%)
Parameter size N/A 30.00B N/A
Model family Meta Qwen Different

Performance Verdict

Based on the available leaderboard data, Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 has the stronger overall benchmark score.

  • Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 is the stronger performer, scoring 98.50 on average compared to meta/llama-guard-4-12b's .
  • Parameter size comparison is not available due to missing parameter metadata.

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("meta/llama-guard-4-12b")
model = AutoModelForCausalLM.from_pretrained("meta/llama-guard-4-12b")
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")

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