Model Comparison

Qwen2-VL-72B-Instruct vs QwQ-32B-Preview

Comparing Qwen2-VL-72B-Instruct and QwQ-32B-Preview across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Qwen2-VL-72B-Instruct and QwQ-32B-Preview don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Thu Apr 16 2026 • llm-stats.com
Alibaba Cloud / Qwen Team
Qwen2-VL-72B-Instruct
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
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

40.9B diff

Qwen2-VL-72B-Instruct has 40.9B more parameters than QwQ-32B-Preview, making it 125.8% larger.

Alibaba Cloud / Qwen Team
Qwen2-VL-72B-Instruct
73.4Bparameters
Alibaba Cloud / Qwen Team
QwQ-32B-Preview
32.5Bparameters
73.4B
Qwen2-VL-72B-Instruct
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).

Alibaba Cloud / Qwen Team
Qwen2-VL-72B-Instruct
Input- tokens
Output- tokens
Alibaba Cloud / Qwen Team
QwQ-32B-Preview
Input32,768 tokens
Output32,768 tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen2-VL-72B-Instruct supports multimodal inputs, whereas QwQ-32B-Preview does not.

Qwen2-VL-72B-Instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.

Qwen2-VL-72B-Instruct

Text
Images
Audio
Video

QwQ-32B-Preview

Text
Images
Audio
Video

License

Usage and distribution terms

Qwen2-VL-72B-Instruct is licensed under tongyi-qianwen, while QwQ-32B-Preview uses Apache 2.0.

License differences may affect how you can use these models in commercial or open-source projects.

Qwen2-VL-72B-Instruct

tongyi-qianwen

Open weights

QwQ-32B-Preview

Apache 2.0

Open weights

Release Timeline

When each model was launched

Qwen2-VL-72B-Instruct was released on 2024-08-29, while QwQ-32B-Preview was released on 2024-11-28.

QwQ-32B-Preview is 3 months newer than Qwen2-VL-72B-Instruct.

Qwen2-VL-72B-Instruct

Aug 29, 2024

1.6 years ago

QwQ-32B-Preview

Nov 28, 2024

1.4 years ago

3mo newer

Knowledge Cutoff

When training data ends

Qwen2-VL-72B-Instruct has a knowledge cutoff of 2023-06-30, 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 Qwen2-VL-72B-Instruct (2023-06-30).

Qwen2-VL-72B-Instruct

Jun 2023

QwQ-32B-Preview

Nov 2024

1.4 yr newer

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Alibaba Cloud / Qwen Team

Qwen2-VL-72B-Instruct

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs
Alibaba Cloud / Qwen Team

QwQ-32B-Preview

View details

Alibaba Cloud / Qwen Team

Larger context window (32,768 tokens)

Detailed Comparison

AI Model Comparison Table
Feature
Alibaba Cloud / Qwen Team
Qwen2-VL-72B-Instruct
Alibaba Cloud / Qwen Team
QwQ-32B-Preview

FAQ

Common questions about Qwen2-VL-72B-Instruct vs QwQ-32B-Preview

Qwen2-VL-72B-Instruct (Alibaba Cloud / Qwen Team) and QwQ-32B-Preview (Alibaba Cloud / Qwen Team) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
Qwen2-VL-72B-Instruct scores DocVQAtest: 96.5%, VCR_en_easy: 91.9%, ChartQA: 88.3%, OCRBench: 87.7%, MMBench_test: 86.5%. QwQ-32B-Preview scores MATH-500: 90.6%, GPQA: 65.2%, AIME 2024: 50.0%, LiveCodeBench: 50.0%.
Qwen2-VL-72B-Instruct 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.
Key differences include multimodal support (yes vs no), licensing (tongyi-qianwen vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.