Qwen2.5-7B-Instruct vs Qwen3-Coder-30B-A3B-Instruct-FP8
Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.
Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8
Metric Comparison
The table keeps the core specs visible for quick evaluation.
| Metric | Qwen2.5-7B-Instruct | Qwen3-Coder-30B-A3B-Instruct-FP8 | Difference |
|---|---|---|---|
| Benchmark average score | 98.50 | 98.50 | Equal |
| Parameter size | 7.00B | 30.00B | -23B (-328.6%) |
| Model family | Qwen | Qwen | Match |
Performance Verdict
Based on the available leaderboard data, Qwen/Qwen2.5-7B-Instruct has the stronger overall benchmark score.
- Qwen/Qwen2.5-7B-Instruct is the stronger performer, scoring 98.50 on average compared to Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8's 98.50.
- Qwen/Qwen3-Coder-30B-A3B-Instruct-FP8 is 328.6% larger in parameter capacity than Qwen/Qwen2.5-7B-Instruct (30.00B vs 7.00B parameters).
- Qwen/Qwen2.5-7B-Instruct 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("Qwen/Qwen2.5-7B-Instruct")
model = AutoModelForCausalLM.from_pretrained("Qwen/Qwen2.5-7B-Instruct")
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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