Model Comparison

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

Both models are evenly matched across the benchmarks. QwQ-32B-Preview is 3.1x cheaper per token.

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Granite 3.3 8B Instruct 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.

Tue May 12 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

QwQ-32B-Preview costs less

For input processing, Granite 3.3 8B Instruct ($0.50/1M tokens) is 3.3x more expensive than QwQ-32B-Preview ($0.15/1M tokens).

For output processing, Granite 3.3 8B Instruct ($0.50/1M tokens) is 2.5x more expensive than QwQ-32B-Preview ($0.20/1M tokens).

In conclusion, Granite 3.3 8B Instruct is more expensive than QwQ-32B-Preview.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Tue May 12 2026 • llm-stats.com
IBM
Granite 3.3 8B Instruct
Input tokens$0.50
Output tokens$0.50
Best providerReplicate
Alibaba Cloud / Qwen Team
QwQ-32B-Preview
Input tokens$0.15
Output tokens$0.20
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

24.5B diff

QwQ-32B-Preview has 24.5B more parameters than Granite 3.3 8B Instruct, making it 306.3% larger.

IBM
Granite 3.3 8B Instruct
8.0Bparameters
Alibaba Cloud / Qwen Team
QwQ-32B-Preview
32.5Bparameters
8.0B
Granite 3.3 8B Instruct
32.5B
QwQ-32B-Preview

Context Window

Maximum input and output token capacity

Granite 3.3 8B Instruct accepts 128,000 input tokens compared to QwQ-32B-Preview's 32,768 tokens. QwQ-32B-Preview can generate longer responses up to 32,768 tokens, while Granite 3.3 8B Instruct is limited to 8,192 tokens.

IBM
Granite 3.3 8B Instruct
Input128,000 tokens
Output8,192 tokens
Alibaba Cloud / Qwen Team
QwQ-32B-Preview
Input32,768 tokens
Output32,768 tokens
Tue May 12 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

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

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

Granite 3.3 8B Instruct

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 Instruct

Apache 2.0

Open weights

QwQ-32B-Preview

Apache 2.0

Open weights

Release Timeline

When each model was launched

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

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

Granite 3.3 8B Instruct

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 Instruct 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 Instruct (2024-04-01).

Granite 3.3 8B Instruct

Apr 2024

QwQ-32B-Preview

Nov 2024

7 mo newer

Provider Availability

Granite 3.3 8B Instruct is available from Replicate. QwQ-32B-Preview is available from DeepInfra, Hyperbolic, Fireworks, Together.

Granite 3.3 8B Instruct

replicate logo
Replicate
Input Price:Input: $0.50/1MOutput Price:Output: $0.50/1M

QwQ-32B-Preview

deepinfra logo
Deepinfra
Input Price:Input: $0.15/1MOutput Price:Output: $0.60/1M
hyperbolic logo
Hyperbolic
Input Price:Input: $0.20/1MOutput Price:Output: $0.20/1M
fireworks logo
Fireworks
Input Price:Input: $0.89/1MOutput Price:Output: $0.89/1M
together logo
Together
Input Price:Input: $1.20/1MOutput Price:Output: $1.20/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Larger context window (128,000 tokens)
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

Less expensive input tokens
Less expensive output tokens
Higher MATH-500 score (90.6% vs 69.0%)

Detailed Comparison

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

FAQ

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

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

Both models are evenly matched across the benchmarks. Granite 3.3 8B Instruct 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 Instruct compare to QwQ-32B-Preview in benchmarks?

Granite 3.3 8B Instruct scores HumanEval: 89.7%, AttaQ: 88.5%, HumanEval+: 86.1%, AIME 2024: 81.2%, GSM8k: 80.9%. QwQ-32B-Preview scores MATH-500: 90.6%, GPQA: 65.2%, AIME 2024: 50.0%, LiveCodeBench: 50.0%.

Is Granite 3.3 8B Instruct cheaper than QwQ-32B-Preview?

QwQ-32B-Preview is 3.3x cheaper for input tokens. Granite 3.3 8B Instruct costs $0.50/M input and $0.50/M output via replicate. QwQ-32B-Preview costs $0.15/M input and $0.20/M output via deepinfra.

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

Granite 3.3 8B Instruct supports 128K 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 Instruct and QwQ-32B-Preview?

Key differences include context window (128K vs 33K), input pricing ($0.50 vs $0.15/M), multimodal support (yes vs no). See the full comparison above for benchmark-by-benchmark results.

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

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