Side-by-side model comparison

TinyLlama-1.1B-Chat-v1.0 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

TinyLlama/TinyLlama-1.1B-Chat-v1.0

Benchmark score 98.50
Parameters 1.10B
Model family Llama
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 TinyLlama-1.1B-Chat-v1.0 Qwen3-Coder-30B-A3B-Instruct-GGUF Difference
Benchmark average score 98.50 98.50 Equal
Parameter size 1.10B 30.00B -28.9B (-2627.3%)
Model family Llama Qwen Different

Performance Verdict

Based on the available leaderboard data, TinyLlama/TinyLlama-1.1B-Chat-v1.0 has the stronger overall benchmark score.

  • TinyLlama/TinyLlama-1.1B-Chat-v1.0 is the stronger performer, scoring 98.50 on average compared to unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF's 98.50.
  • unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF is 2627.3% larger in parameter capacity than TinyLlama/TinyLlama-1.1B-Chat-v1.0 (30.00B vs 1.10B parameters).
  • TinyLlama/TinyLlama-1.1B-Chat-v1.0 is also smaller, which makes its score advantage especially efficient.

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("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
model = AutoModelForCausalLM.from_pretrained("TinyLlama/TinyLlama-1.1B-Chat-v1.0")
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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