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DeepSeek VL2 Tiny vs Qwen2 72B Instruct

Qwen2 72B Instruct leads the LLM Stats Score 5.8 to -4.7.

DeepSeek · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Qwen2 72B Instruct leads the overall LLM Stats Score 5.8 to -4.7, ranking #294 overall.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose DeepSeek VL2 Tiny

  • you want the most recent training data — it shipped Dec 2024

Choose Qwen2 72B Instruct

  • overall performance matters — it scores 5.8 and ranks #294 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes

At a glance

The differences that matter most.

Core performance indexes
-4.7
#351
5.8
#294
-12.7
#362
5.7
#290
Cost, coverage & limits
Benchmark wins
Input price
— / M
— / M
Output price
— / M
— / M
Context window

Individual benchmarks

14 reported for DeepSeek VL2 Tiny · 17 for Qwen2 72B Instruct

No common benchmarks found

DeepSeek VL2 Tiny and Qwen2 72B Instructdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Human preference

Blind head-to-head votes and playground preference scores

Model Size

Parameter count comparison

69.0B diff

Qwen2 72B Instruct has 69.0B more parameters than DeepSeek VL2 Tiny, making it 2300.0% larger.

DeepSeek
DeepSeek VL2 Tiny
3.0Bparameters
Alibaba Cloud / Qwen Team
Qwen2 72B Instruct
72.0Bparameters
3.0B
DeepSeek VL2 Tiny
72.0B
Qwen2 72B Instruct

Input capabilities

Documented input modalities across available providers

DeepSeek VL2 Tiny supports multimodal inputs, whereas Qwen2 72B Instruct does not.

DeepSeek VL2 Tiny can handle both text and other forms of data like images, making it suitable for multimodal applications.

DeepSeek VL2 Tiny

Text
Images
Audio
Video

Qwen2 72B Instruct

Text
Images
Audio
Video

License

Usage and distribution terms

DeepSeek VL2 Tiny is licensed under deepseek, while Qwen2 72B Instruct uses tongyi-qianwen.

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

DeepSeek VL2 Tiny

deepseek

Open weights

Qwen2 72B Instruct

tongyi-qianwen

Open weights

Release Timeline

When each model was launched

DeepSeek VL2 Tiny was released on 2024-12-13, while Qwen2 72B Instruct was released on 2024-07-23.

DeepSeek VL2 Tiny is 5 months newer than Qwen2 72B Instruct.

DeepSeek VL2 Tiny

Dec 13, 2024

1.8 years ago

4mo newer
Qwen2 72B Instruct

Jul 23, 2024

2.2 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

Judge for yourself.

Run your own prompts against DeepSeek VL2 Tiny and Qwen2 72B Instruct side-by-side, then vote on the output you prefer.

DeepSeek VL2 Tiny
✓ Preferred
Qwen2 72B Instruct
Open in Playground

FAQ

Common questions about DeepSeek VL2 Tiny vs Qwen2 72B Instruct.

Which is better, DeepSeek VL2 Tiny or Qwen2 72B Instruct?

Qwen2 72B Instruct leads the LLM Stats Score 5.8 to -4.7. DeepSeek VL2 Tiny is made by DeepSeek and Qwen2 72B Instruct is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their capability indexes, individual benchmarks, pricing, and limits above.

How does DeepSeek VL2 Tiny compare to Qwen2 72B Instruct in benchmarks?

DeepSeek VL2 Tiny scores DocVQA: 88.9%, ChartQA: 81.0%, OCRBench: 80.9%, TextVQA: 80.7%, AI2D: 71.6%. Qwen2 72B Instruct scores GSM8k: 91.1%, CMMLU: 90.1%, HellaSwag: 87.6%, HumanEval: 86.0%, Winogrande: 85.1%.

What are the main differences between DeepSeek VL2 Tiny and Qwen2 72B Instruct?

Key differences include LLM Stats Score (-4.7 vs 5.8), multimodal support (yes vs no), licensing (deepseek vs tongyi-qianwen). See the full comparison above for benchmark-by-benchmark results.

Who makes DeepSeek VL2 Tiny and Qwen2 72B Instruct?

DeepSeek VL2 Tiny is developed by DeepSeek and Qwen2 72B Instruct is developed by Alibaba Cloud / Qwen Team.