Side-by-side model comparison

DeepSeek-R1-0528-Qwen3-8B vs granite-4.1-8b

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

ibm-granite/granite-4.1-8b

Benchmark score 98.50

Parameters 8.00B

Model family Other

Dataset status Available

Metric Comparison

The table keeps the core specs visible for quick evaluation.

Live dataset

Metric	DeepSeek-R1-0528-Qwen3-8B	granite-4.1-8b	Difference
Benchmark average score	98.50	98.50	Equal
Parameter size	8.00B	8.00B	Equal
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 ibm-granite/granite-4.1-8b's 98.50.
Both models share the exact same parameter size of 8.00B parameters.
deepseek-ai/DeepSeek-R1-0528-Qwen3-8B has more parameter capacity, which may contribute to its stronger benchmark score.

Integration & Implementation Guide

Learn how to load and execute these models programmatically in Python using Hugging Face's transformers library.

Python tutorial

Load Model A (DeepSeek-R1-0528-Qwen3-8B)

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")

Load Model B (granite-4.1-8b)

from transformers import AutoModelForCausalLM, AutoTokenizer

tokenizer = AutoTokenizer.from_pretrained("ibm-granite/granite-4.1-8b")
model = AutoModelForCausalLM.from_pretrained("ibm-granite/granite-4.1-8b")

Compare Alternative Models

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Comparison

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

ibm-granite/granite-4.1-8b

Metric Comparison

Performance Verdict

Integration & Implementation Guide

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