The AI arena is free today

Open Superagent

Qwen2-VL-72B-Instruct vs Qwen3.8-27B

Qwen3.8-27B significantly outperforms across most benchmarks.

Alibaba Cloud / Qwen Team · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Qwen2-VL-72B-Instruct outperforms in 0 benchmarks, while Qwen3.8-27B is better at 1 benchmark (RealWorldQA). Qwen3.8-27B significantly outperforms across most benchmarks.

Based on current benchmark, pricing, and model metadata for 2026.

Choose Qwen2-VL-72B-Instruct

  • you are already invested in the Alibaba Cloud / Qwen Team ecosystem

Choose Qwen3.8-27B

  • you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Benchmark wins
0 of 1
1 of 1
Input price
— / M
— / M
Output price
— / M
— / M
Context window
Released
Aug 2024
Aug 2026
License
tongyi-qianwen
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

Qwen2-VL-72B-Instruct outperforms in 0 benchmarks, while Qwen3.8-27B is better at 1 benchmark (RealWorldQA).

Qwen3.8-27B significantly outperforms across most benchmarks.

Tue Aug 25 2026 • llm-stats.com

Arena Performance

Playground indexes and blind preference scores

Model Size

Parameter count comparison

45.6B diff

Qwen2-VL-72B-Instruct has 45.6B more parameters than Qwen3.8-27B, making it 164.2% larger.

Alibaba Cloud / Qwen Team
Qwen2-VL-72B-Instruct
73.4Bparameters
Alibaba Cloud / Qwen Team
Qwen3.8-27B
27.8Bparameters
73.4B
Qwen2-VL-72B-Instruct
27.8B
Qwen3.8-27B

Input Capabilities

Supported data types and modalities

Both Qwen2-VL-72B-Instruct and Qwen3.8-27B support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Qwen2-VL-72B-Instruct

Text
Images
Audio
Video

Qwen3.8-27B

Text
Images
Audio
Video

License

Usage and distribution terms

Qwen2-VL-72B-Instruct is licensed under tongyi-qianwen, while Qwen3.8-27B 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

Qwen3.8-27B

Apache 2.0

Open weights

Release Timeline

When each model was launched

Qwen2-VL-72B-Instruct was released on 2024-08-29, while Qwen3.8-27B was released on 2026-08-14.

Qwen3.8-27B is 24 months newer than Qwen2-VL-72B-Instruct.

Qwen2-VL-72B-Instruct

Aug 29, 2024

2.0 years ago

Qwen3.8-27B

Aug 14, 2026

1 weeks ago

2.0yr newer

Knowledge Cutoff

When training data ends

Qwen2-VL-72B-Instruct has a documented knowledge cutoff of 2023-06-30, while Qwen3.8-27B'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 Qwen3.8-27B's cutoff date.

Qwen2-VL-72B-Instruct

Jun 2023

Qwen3.8-27B

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Qwen2-VL-72B-Instruct and Qwen3.8-27B side-by-side, then vote on the output you prefer.

Qwen2-VL-72B-Instruct
✓ Preferred
Qwen3.8-27B
Open in Playground

FAQ

Common questions about Qwen2-VL-72B-Instruct vs Qwen3.8-27B.

Which is better, Qwen2-VL-72B-Instruct or Qwen3.8-27B?

Qwen3.8-27B significantly outperforms across most benchmarks. Qwen2-VL-72B-Instruct is made by Alibaba Cloud / Qwen Team and Qwen3.8-27B is made by Alibaba Cloud / Qwen Team. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Qwen2-VL-72B-Instruct compare to Qwen3.8-27B in benchmarks?

Qwen2-VL-72B-Instruct scores DocVQAtest: 96.5%, VCR_en_easy: 91.9%, ChartQA: 88.3%, OCRBench: 87.7%, MMBench: 86.5%. Qwen3.8-27B scores MathVision: 94.6%, OmniDocBench 1.5: 91.1%, LiveCodeBench v6: 90.3%, CharXiv-R: 90.2%, GPQA: 89.2%.

What are the main differences between Qwen2-VL-72B-Instruct and Qwen3.8-27B?

Key differences include licensing (tongyi-qianwen vs Apache 2.0). See the full comparison above for benchmark-by-benchmark results.