Side-by-side model comparison

DeepSeek-R1-0528-Qwen3-8B vs llama-3.1-nemotron-ultra-253b-v1

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

Model A

deepseek-ai/DeepSeek-R1-0528-Qwen3-8B

Benchmark score 98.50
Parameters 8.00B
Model family Qwen
Dataset status Available
Model B

nvidia/llama-3.1-nemotron-ultra-253b-v1

Benchmark score
Parameters N/A
Model family Nvidia
Dataset status Available

Metric Comparison

The table keeps the core specs visible for quick evaluation.

Live dataset
Metric DeepSeek-R1-0528-Qwen3-8B llama-3.1-nemotron-ultra-253b-v1 Difference
Benchmark average score 98.50 +98.5 (+%)
Parameter size 8.00B N/A N/A
Model family Qwen Nvidia 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 nvidia/llama-3.1-nemotron-ultra-253b-v1's .
  • Parameter size comparison is not available due to missing parameter metadata.

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("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("nvidia/llama-3.1-nemotron-ultra-253b-v1")
model = AutoModelForCausalLM.from_pretrained("nvidia/llama-3.1-nemotron-ultra-253b-v1")

Compare Alternative Models

Explore nearby pairings from the same model dataset.

Need This In Production?

I can help with model hosting, quantization, API integration, RAG systems, and production rollout.