Model Comparison

GPT-4o vs Qwen3 VL 32B Thinking

Qwen3 VL 32B Thinking shows notably better performance in the majority of benchmarks.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

3 benchmarks

GPT-4o outperforms in 0 benchmarks, while Qwen3 VL 32B Thinking is better at 2 benchmarks (GPQA, MMLU-Pro).

Qwen3 VL 32B Thinking shows notably better performance in the majority of benchmarks.

Thu Jun 04 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only GPT-4o specifies input context (128,000 tokens). Only GPT-4o specifies output context (4,096 tokens).

GPT-4o

Input128,000 tokens

Output4,096 tokens

Qwen3 VL 32B Thinking

Input- tokens

Output- tokens

Thu Jun 04 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both GPT-4o and Qwen3 VL 32B Thinking support multimodal inputs.

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

GPT-4o

Text

Images

Audio

Video

Qwen3 VL 32B Thinking

Text

Images

Audio

Video

License

Usage and distribution terms

GPT-4o is licensed under a proprietary license, while Qwen3 VL 32B Thinking uses Apache 2.0.

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

GPT-4o

Proprietary

Closed source

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

GPT-4o was released on 2024-05-13, while Qwen3 VL 32B Thinking was released on 2025-09-22.

Qwen3 VL 32B Thinking is 17 months newer than GPT-4o.

GPT-4o

May 13, 2024

2.1 years ago

Qwen3 VL 32B Thinking

Sep 22, 2025

8 months ago

1.4yr newer

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

GPT-4o

View details

OpenAI

Larger context window (128,000 tokens)

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Has open weights

Higher GPQA score (73.1% vs 53.6%)

Higher MMLU-Pro score (82.1% vs 72.6%)

GPT-4o

Qwen3 VL 32B Thinking

Compare in Playground

Detailed Comparison

AI Model Comparison Table
Feature	GPT-4o	Qwen3 VL 32B Thinking

FAQ

Common questions about GPT-4o vs Qwen3 VL 32B Thinking.

Which is better, GPT-4o or Qwen3 VL 32B Thinking?

Qwen3 VL 32B Thinking shows notably better performance in the majority of benchmarks. GPT-4o is made by OpenAI and Qwen3 VL 32B Thinking 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 GPT-4o compare to Qwen3 VL 32B Thinking in benchmarks?

GPT-4o scores MGSM: 90.5%, HumanEval: 90.2%, MMLU: 88.7%, DROP: 83.4%, MATH: 76.6%. Qwen3 VL 32B Thinking scores DocVQAtest: 96.1%, ScreenSpot: 95.7%, MMLU-Redux: 91.9%, MMBench-V1.1: 90.8%, CharXiv-D: 90.2%.

What are the context window sizes for GPT-4o and Qwen3 VL 32B Thinking?

GPT-4o supports 128K tokens and Qwen3 VL 32B Thinking supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between GPT-4o and Qwen3 VL 32B Thinking?

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

Who makes GPT-4o and Qwen3 VL 32B Thinking?

GPT-4o is developed by OpenAI and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.