Qwen3-VL-30B-A3B-Instruct-AWQ vs Qwen2.5-14B-Instruct
Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.
Qwen/Qwen2.5-14B-Instruct
Metric Comparison
The table keeps the core specs visible for quick evaluation.
| Metric | Qwen3-VL-30B-A3B-Instruct-AWQ | Qwen2.5-14B-Instruct | Difference |
|---|---|---|---|
| Benchmark average score | 98.50 | 98.50 | Equal |
| Parameter size | 30.00B | 14.00B | +16B (+114.3%) |
| 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/Qwen2.5-14B-Instruct's 98.50.
- QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ is 114.3% larger in parameter capacity than Qwen/Qwen2.5-14B-Instruct (30.00B vs 14.00B 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.
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")
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-14B-Instruct")
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