Model Comparison

DeepSeek-R1-0528 vs QvQ-72B-Preview

Comparing DeepSeek-R1-0528 and QvQ-72B-Preview across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek-R1-0528 and QvQ-72B-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
Sat May 02 2026 • llm-stats.com
DeepSeek
DeepSeek-R1-0528
Input tokens$0.50
Output tokens$2.15
Best providerDeepinfra
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

597.6B diff

DeepSeek-R1-0528 has 597.6B more parameters than QvQ-72B-Preview, making it 814.2% larger.

DeepSeek
DeepSeek-R1-0528
671.0Bparameters
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
73.4Bparameters
671.0B
DeepSeek-R1-0528
73.4B
QvQ-72B-Preview

Context Window

Maximum input and output token capacity

Only DeepSeek-R1-0528 specifies input context (131,072 tokens). Only DeepSeek-R1-0528 specifies output context (131,072 tokens).

DeepSeek
DeepSeek-R1-0528
Input131,072 tokens
Output131,072 tokens
Alibaba Cloud / Qwen Team
QvQ-72B-Preview
Input- tokens
Output- tokens
Sat May 02 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

QvQ-72B-Preview supports multimodal inputs, whereas DeepSeek-R1-0528 does not.

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

DeepSeek-R1-0528

Text
Images
Audio
Video

QvQ-72B-Preview

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek-R1-0528 is licensed under MIT, while QvQ-72B-Preview uses Qwen.

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

DeepSeek-R1-0528

MIT

Open weights

QvQ-72B-Preview

Qwen

Open weights

Release Timeline

When each model was launched

DeepSeek-R1-0528 was released on 2025-05-28, while QvQ-72B-Preview was released on 2024-12-25.

DeepSeek-R1-0528 is 5 months newer than QvQ-72B-Preview.

DeepSeek-R1-0528

May 28, 2025

11 months ago

5mo newer
QvQ-72B-Preview

Dec 25, 2024

1.4 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
Larger context window (131,072 tokens)
Alibaba Cloud / Qwen Team

QvQ-72B-Preview

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek-R1-0528
Alibaba Cloud / Qwen Team
QvQ-72B-Preview

FAQ

Common questions about DeepSeek-R1-0528 vs QvQ-72B-Preview

DeepSeek-R1-0528 (DeepSeek) and QvQ-72B-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.
DeepSeek-R1-0528 scores MMLU-Redux: 93.4%, SimpleQA: 92.3%, AIME 2024: 91.4%, AIME 2025: 87.5%, MMLU-Pro: 85.0%. QvQ-72B-Preview scores MathVista: 71.4%, MMMU: 70.3%, MathVision: 35.9%, OlympiadBench: 20.4%.
DeepSeek-R1-0528 supports 131K tokens and QvQ-72B-Preview supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (no vs yes), licensing (MIT vs Qwen). See the full comparison above for benchmark-by-benchmark results.
DeepSeek-R1-0528 is developed by DeepSeek and QvQ-72B-Preview is developed by Alibaba Cloud / Qwen Team.