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Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.
The table keeps the core specs visible for quick evaluation.
| Metric | dolphin-2.9.1-yi-1.5-34b | Gemma-4-31B-IT-NVFP4 | Difference |
|---|---|---|---|
| Benchmark average score | 98.50 | 98.50 | Equal |
| Parameter size | 34.00B | 31.00B | +3B (+9.7%) |
| Model family | Llama | Gemma | Different |
Based on the available leaderboard data, dphn/dolphin-2.9.1-yi-1.5-34b has the stronger overall benchmark score.
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("dphn/dolphin-2.9.1-yi-1.5-34b")
model = AutoModelForCausalLM.from_pretrained("dphn/dolphin-2.9.1-yi-1.5-34b")from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("nvidia/Gemma-4-31B-IT-NVFP4")
model = AutoModelForCausalLM.from_pretrained("nvidia/Gemma-4-31B-IT-NVFP4")Explore nearby pairings from the same model dataset.
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