DeepSeek-R1-0528-Qwen3-8B vs Ornith-1.5-9B-GGUF
Compare benchmark score, parameter size, model family, and practical tradeoffs between these two Hugging Face LLM models.
ornith-ai/Ornith-1.5-9B-GGUF
Metric Comparison
The table keeps the core specs visible for quick evaluation.
| Metric | DeepSeek-R1-0528-Qwen3-8B | Ornith-1.5-9B-GGUF | Difference |
|---|---|---|---|
| Benchmark average score | 98.50 | 98.50 | Equal |
| Parameter size | 8.00B | 9.00B | -1B (-12.5%) |
| Model family | Qwen | Other | Different |
Performance Verdict
Based on the available leaderboard data, deepseek-ai/DeepSeek-R1-0528-Qwen3-8B has the stronger overall benchmark score.
- deepseek-ai/DeepSeek-R1-0528-Qwen3-8B is the stronger performer, scoring 98.50 on average compared to ornith-ai/Ornith-1.5-9B-GGUF's 98.50.
- ornith-ai/Ornith-1.5-9B-GGUF is 12.5% larger in parameter capacity than deepseek-ai/DeepSeek-R1-0528-Qwen3-8B (9.00B vs 8.00B parameters).
- deepseek-ai/DeepSeek-R1-0528-Qwen3-8B 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.
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")
model = AutoModelForCausalLM.from_pretrained("deepseek-ai/DeepSeek-R1-0528-Qwen3-8B")
from transformers import AutoModelForCausalLM, AutoTokenizer
tokenizer = AutoTokenizer.from_pretrained("ornith-ai/Ornith-1.5-9B-GGUF")
model = AutoModelForCausalLM.from_pretrained("ornith-ai/Ornith-1.5-9B-GGUF")
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