Model Comparison

Granite 3.3 8B Base vs QwQ-32B-Preview

Both models are evenly matched across the benchmarks.

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Granite 3.3 8B Base outperforms in 1 benchmarks (AIME 2024), while QwQ-32B-Preview is better at 1 benchmark (MATH-500).

Both models are evenly matched across the benchmarks.

Sun May 31 2026 • llm-stats.com

Arena Performance

Human preference votes

Model Size

Parameter count comparison

24.3B diff

QwQ-32B-Preview has 24.3B more parameters than Granite 3.3 8B Base, making it 297.8% larger.

IBM
Granite 3.3 8B Base
8.2Bparameters
Alibaba Cloud / Qwen Team
QwQ-32B-Preview
32.5Bparameters
8.2B
Granite 3.3 8B Base
32.5B
QwQ-32B-Preview

Context Window

Maximum input and output token capacity

Only QwQ-32B-Preview specifies input context (32,768 tokens). Only QwQ-32B-Preview specifies output context (32,768 tokens).

IBM
Granite 3.3 8B Base
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
QwQ-32B-Preview
Input32,768 tokens
Output32,768 tokens
Sun May 31 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Granite 3.3 8B Base supports multimodal inputs, whereas QwQ-32B-Preview does not.

Granite 3.3 8B Base can handle both text and other forms of data like images, making it suitable for multimodal applications.

Granite 3.3 8B Base

Text
Images
Audio
Video

QwQ-32B-Preview

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under Apache 2.0.

Both models share the same licensing terms, providing consistent usage rights.

Granite 3.3 8B Base

Apache 2.0

Open weights

QwQ-32B-Preview

Apache 2.0

Open weights

Release Timeline

When each model was launched

Granite 3.3 8B Base was released on 2025-04-16, while QwQ-32B-Preview was released on 2024-11-28.

Granite 3.3 8B Base is 5 months newer than QwQ-32B-Preview.

Granite 3.3 8B Base

Apr 16, 2025

1.1 years ago

4mo newer
QwQ-32B-Preview

Nov 28, 2024

1.5 years ago

Knowledge Cutoff

When training data ends

Granite 3.3 8B Base has a knowledge cutoff of 2024-04-01, while QwQ-32B-Preview has a cutoff of 2024-11-28.

QwQ-32B-Preview has more recent training data (up to 2024-11-28), making it potentially better informed about events through that date compared to Granite 3.3 8B Base (2024-04-01).

Granite 3.3 8B Base

Apr 2024

QwQ-32B-Preview

Nov 2024

7 mo newer

Outputs Comparison

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Key Takeaways

Supports multimodal inputs
Higher AIME 2024 score (81.2% vs 50.0%)
Alibaba Cloud / Qwen Team

QwQ-32B-Preview

View details

Alibaba Cloud / Qwen Team

Larger context window (32,768 tokens)
Higher MATH-500 score (90.6% vs 69.0%)

Detailed Comparison

AI Model Comparison Table
Feature
IBM
Granite 3.3 8B Base
Alibaba Cloud / Qwen Team
QwQ-32B-Preview

FAQ

Common questions about Granite 3.3 8B Base vs QwQ-32B-Preview.

Which is better, Granite 3.3 8B Base or QwQ-32B-Preview?

Both models are evenly matched across the benchmarks. Granite 3.3 8B Base is made by IBM and QwQ-32B-Preview is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Granite 3.3 8B Base compare to QwQ-32B-Preview in benchmarks?

Granite 3.3 8B Base scores HumanEval: 89.7%, AttaQ: 88.5%, HumanEval+: 86.1%, AIME 2024: 81.2%, HellaSwag: 80.1%. QwQ-32B-Preview scores MATH-500: 90.6%, GPQA: 65.2%, AIME 2024: 50.0%, LiveCodeBench: 50.0%.

What are the context window sizes for Granite 3.3 8B Base and QwQ-32B-Preview?

Granite 3.3 8B Base supports an unknown number of tokens and QwQ-32B-Preview supports 33K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Granite 3.3 8B Base and QwQ-32B-Preview?

Key differences include multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.

Who makes Granite 3.3 8B Base and QwQ-32B-Preview?

Granite 3.3 8B Base is developed by IBM and QwQ-32B-Preview is developed by Alibaba Cloud / Qwen Team.