Side-by-side model comparison

dolphin-2.9.1-yi-1.5-34b vs Ornith-1.5-35B-A3B-GGUF

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

Model A

dphn/dolphin-2.9.1-yi-1.5-34b

Benchmark score 98.50
Parameters 34.00B
Model family Llama
Dataset status Available
Model B

ornith-ai/Ornith-1.5-35B-A3B-GGUF

Benchmark score 98.50
Parameters 35.00B
Model family Other
Dataset status Available

Metric Comparison

The table keeps the core specs visible for quick evaluation.

Live dataset
Metric dolphin-2.9.1-yi-1.5-34b Ornith-1.5-35B-A3B-GGUF Difference
Benchmark average score 98.50 98.50 Equal
Parameter size 34.00B 35.00B -1B (-2.9%)
Model family Llama Other Different

Performance Verdict

Based on the available leaderboard data, dphn/dolphin-2.9.1-yi-1.5-34b has the stronger overall benchmark score.

  • dphn/dolphin-2.9.1-yi-1.5-34b is the stronger performer, scoring 98.50 on average compared to ornith-ai/Ornith-1.5-35B-A3B-GGUF's 98.50.
  • ornith-ai/Ornith-1.5-35B-A3B-GGUF is 2.9% larger in parameter capacity than dphn/dolphin-2.9.1-yi-1.5-34b (35.00B vs 34.00B parameters).
  • dphn/dolphin-2.9.1-yi-1.5-34b is also smaller, which makes its score advantage especially efficient.

Integration & Implementation Guide

Learn how to load and execute these models programmatically in Python, JavaScript/TypeScript, Go, Rust, C++, and PHP.

Integration code
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("ornith-ai/Ornith-1.5-35B-A3B-GGUF")
model = AutoModelForCausalLM.from_pretrained("ornith-ai/Ornith-1.5-35B-A3B-GGUF")

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