Gemma-4-26B-A4B-NVFP4 vs Qwen3-VL-30B-A3B-Instruct-AWQ
Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.
QuantTrio/Qwen3-VL-30B-A3B-Instruct-AWQ
Metric Comparison
The table keeps the core specs visible for quick evaluation.
| Metric | Gemma-4-26B-A4B-NVFP4 | Qwen3-VL-30B-A3B-Instruct-AWQ | Difference |
|---|---|---|---|
| Benchmark average score | 98.50 | 98.50 | Equal |
| Parameter size | 26.00B | 30.00B | -4B (-15.4%) |
| Model family | Gemma | Qwen | Different |
Performance Verdict
Based on the available leaderboard data, nvidia/Gemma-4-26B-A4B-NVFP4 has the stronger overall benchmark score.
- nvidia/Gemma-4-26B-A4B-NVFP4 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 15.4% larger in parameter capacity than nvidia/Gemma-4-26B-A4B-NVFP4 (30.00B vs 26.00B parameters).
- nvidia/Gemma-4-26B-A4B-NVFP4 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.
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("nvidia/Gemma-4-26B-A4B-NVFP4")
model = AutoModelForCausalLM.from_pretrained("nvidia/Gemma-4-26B-A4B-NVFP4")
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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