Model Comparison

DeepSeek R1 Distill Qwen 32B vs Qwen2-VL-72B-Instruct

Comparing DeepSeek R1 Distill Qwen 32B and Qwen2-VL-72B-Instruct across benchmarks, pricing, and capabilities.

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek R1 Distill Qwen 32B and Qwen2-VL-72B-Instruct 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
Mon Mar 30 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Distill Qwen 32B
Input tokens$0.12
Output tokens$0.18
Best providerDeepinfra
Alibaba Cloud / Qwen Team
Qwen2-VL-72B-Instruct
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue

Model Size

Parameter count comparison

40.6B diff

Qwen2-VL-72B-Instruct has 40.6B more parameters than DeepSeek R1 Distill Qwen 32B, making it 123.8% larger.

DeepSeek
DeepSeek R1 Distill Qwen 32B
32.8Bparameters
Alibaba Cloud / Qwen Team
Qwen2-VL-72B-Instruct
73.4Bparameters
32.8B
DeepSeek R1 Distill Qwen 32B
73.4B
Qwen2-VL-72B-Instruct

Context Window

Maximum input and output token capacity

Only DeepSeek R1 Distill Qwen 32B specifies input context (128,000 tokens). Only DeepSeek R1 Distill Qwen 32B specifies output context (128,000 tokens).

DeepSeek
DeepSeek R1 Distill Qwen 32B
Input128,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen2-VL-72B-Instruct
Input- tokens
Output- tokens
Mon Mar 30 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Qwen2-VL-72B-Instruct supports multimodal inputs, whereas DeepSeek R1 Distill Qwen 32B does not.

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

DeepSeek R1 Distill Qwen 32B

Text
Images
Audio
Video

Qwen2-VL-72B-Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Distill Qwen 32B is licensed under MIT, while Qwen2-VL-72B-Instruct uses tongyi-qianwen.

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

DeepSeek R1 Distill Qwen 32B

MIT

Open weights

Qwen2-VL-72B-Instruct

tongyi-qianwen

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 32B was released on 2025-01-20, while Qwen2-VL-72B-Instruct was released on 2024-08-29.

DeepSeek R1 Distill Qwen 32B is 5 months newer than Qwen2-VL-72B-Instruct.

DeepSeek R1 Distill Qwen 32B

Jan 20, 2025

1.2 years ago

4mo newer
Qwen2-VL-72B-Instruct

Aug 29, 2024

1.6 years ago

Knowledge Cutoff

When training data ends

Qwen2-VL-72B-Instruct has a documented knowledge cutoff of 2023-06-30, while DeepSeek R1 Distill Qwen 32B's cutoff date is not specified.

We can confirm Qwen2-VL-72B-Instruct's training data extends to 2023-06-30, but cannot make a direct comparison without DeepSeek R1 Distill Qwen 32B's cutoff date.

DeepSeek R1 Distill Qwen 32B

Qwen2-VL-72B-Instruct

Jun 2023

Outputs Comparison

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Key Takeaways

Larger context window (128,000 tokens)
Alibaba Cloud / Qwen Team

Qwen2-VL-72B-Instruct

View details

Alibaba Cloud / Qwen Team

Supports multimodal inputs

Detailed Comparison

FAQ

Common questions about DeepSeek R1 Distill Qwen 32B vs Qwen2-VL-72B-Instruct

DeepSeek R1 Distill Qwen 32B (DeepSeek) and Qwen2-VL-72B-Instruct (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 Distill Qwen 32B scores MATH-500: 94.3%, AIME 2024: 83.3%, GPQA: 62.1%, LiveCodeBench: 57.2%. Qwen2-VL-72B-Instruct scores DocVQAtest: 96.5%, VCR_en_easy: 91.9%, ChartQA: 88.3%, OCRBench: 87.7%, MMBench_test: 86.5%.
DeepSeek R1 Distill Qwen 32B supports 128K tokens and Qwen2-VL-72B-Instruct 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 tongyi-qianwen). See the full comparison above for benchmark-by-benchmark results.
DeepSeek R1 Distill Qwen 32B is developed by DeepSeek and Qwen2-VL-72B-Instruct is developed by Alibaba Cloud / Qwen Team.