Model Comparison

DeepSeek R1 Distill Qwen 32B vs DeepSeek VL2

Comparing DeepSeek R1 Distill Qwen 32B and DeepSeek VL2 across benchmarks, pricing, and capabilities.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

DeepSeek R1 Distill Qwen 32B and DeepSeek VL2 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 Apr 13 2026 • llm-stats.com
DeepSeek
DeepSeek R1 Distill Qwen 32B
Input tokens$0.12
Output tokens$0.18
Best providerDeepinfra
DeepSeek
DeepSeek VL2
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

5.8B diff

DeepSeek R1 Distill Qwen 32B has 5.8B more parameters than DeepSeek VL2, making it 21.5% larger.

DeepSeek
DeepSeek R1 Distill Qwen 32B
32.8Bparameters
DeepSeek
DeepSeek VL2
27.0Bparameters
32.8B
DeepSeek R1 Distill Qwen 32B
27.0B
DeepSeek VL2

Context Window

Maximum input and output token capacity

DeepSeek VL2 accepts 129,280 input tokens compared to DeepSeek R1 Distill Qwen 32B's 128,000 tokens. DeepSeek VL2 can generate longer responses up to 129,280 tokens, while DeepSeek R1 Distill Qwen 32B is limited to 128,000 tokens.

DeepSeek
DeepSeek R1 Distill Qwen 32B
Input128,000 tokens
Output128,000 tokens
DeepSeek
DeepSeek VL2
Input129,280 tokens
Output129,280 tokens
Mon Apr 13 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

DeepSeek VL2 supports multimodal inputs, whereas DeepSeek R1 Distill Qwen 32B does not.

DeepSeek VL2 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

DeepSeek VL2

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek R1 Distill Qwen 32B is licensed under MIT, while DeepSeek VL2 uses deepseek.

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

DeepSeek R1 Distill Qwen 32B

MIT

Open weights

DeepSeek VL2

deepseek

Open weights

Release Timeline

When each model was launched

DeepSeek R1 Distill Qwen 32B was released on 2025-01-20, while DeepSeek VL2 was released on 2024-12-13.

DeepSeek R1 Distill Qwen 32B is 1 month newer than DeepSeek VL2.

DeepSeek R1 Distill Qwen 32B

Jan 20, 2025

1.2 years ago

1mo newer
DeepSeek VL2

Dec 13, 2024

1.3 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

Provider Availability

DeepSeek R1 Distill Qwen 32B is available from DeepInfra. DeepSeek VL2 is available from Replicate.

DeepSeek R1 Distill Qwen 32B

deepinfra logo
Deepinfra
Input Price:Input: $0.12/1MOutput Price:Output: $0.18/1M

DeepSeek VL2

replicate logo
Replicate
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

DeepSeek
DeepSeek
Larger context window (129,280 tokens)
Supports multimodal inputs

Detailed Comparison

AI Model Comparison Table
Feature
DeepSeek
DeepSeek R1 Distill Qwen 32B
DeepSeek
DeepSeek VL2

FAQ

Common questions about DeepSeek R1 Distill Qwen 32B vs DeepSeek VL2

DeepSeek R1 Distill Qwen 32B (DeepSeek) and DeepSeek VL2 (DeepSeek) 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%. DeepSeek VL2 scores DocVQA: 93.3%, ChartQA: 86.0%, TextVQA: 84.2%, AI2D: 81.4%, OCRBench: 81.1%.
DeepSeek R1 Distill Qwen 32B supports 128K tokens and DeepSeek VL2 supports 129K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include context window (128K vs 129K), multimodal support (no vs yes), licensing (MIT vs deepseek). See the full comparison above for benchmark-by-benchmark results.