Side-by-side model comparison

OTel-LLM-E4B-IT vs Qwen3-VL-30B-A3B-Instruct-AWQ

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

Model A

farbodtavakkoli/OTel-LLM-E4B-IT

Benchmark score 98.50
Parameters 4.00B
Model family Other
Dataset status Available
Model B

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

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 OTel-LLM-E4B-IT Qwen3-VL-30B-A3B-Instruct-AWQ Difference
Benchmark average score 98.50 98.50 Equal
Parameter size 4.00B 30.00B -26B (-650%)
Model family Other Qwen Different

Performance Verdict

Based on the available leaderboard data, farbodtavakkoli/OTel-LLM-E4B-IT has the stronger overall benchmark score.

  • farbodtavakkoli/OTel-LLM-E4B-IT is the stronger performer, scoring 98.50 on average compared to QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ's 98.50.
  • QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ is 650% larger in parameter capacity than farbodtavakkoli/OTel-LLM-E4B-IT (30.00B vs 4.00B parameters).
  • farbodtavakkoli/OTel-LLM-E4B-IT 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 (OTel-LLM-E4B-IT)
from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("farbodtavakkoli/OTel-LLM-E4B-IT")
model = AutoModelForCausalLM.from_pretrained("farbodtavakkoli/OTel-LLM-E4B-IT")
Load Model B (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")

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