Model Comparison

GPT-4.1 nano vs Qwen3 VL 32B ThinkingWhich is better in 2026?

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Verdict: GPT-4.1 nano vs Qwen3 VL 32B Thinking — which is better?

GPT-4.1 nano (by OpenAI) and Qwen3 VL 32B Thinking (by Alibaba Cloud / Qwen Team) are two of the AI models people compare most. Here is how they stack up on benchmarks, price and capabilities, and which one to pick in 2026.

GPT-4.1 nano outperforms in 0 benchmarks, while Qwen3 VL 32B Thinking is better at 6 benchmarks (CharXiv-D, CharXiv-R, GPQA, IFEval, MMLU, Multi-IF). Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Choose GPT-4.1 nano if…

  • you want predictable pricing at $0.10/M input and $0.40/M output

Choose Qwen3 VL 32B Thinking if…

  • you want the strongest raw capability — it leads on 6 of 6 shared benchmarks
  • you want the most recent training data — it shipped Sep 2025
  • you need open weights you can self-host or fine-tune

Performance Benchmarks

Comparative analysis across standard metrics

6 benchmarks

GPT-4.1 nano outperforms in 0 benchmarks, while Qwen3 VL 32B Thinking is better at 6 benchmarks (CharXiv-D, CharXiv-R, GPQA, IFEval, MMLU, Multi-IF).

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Sat Jul 18 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only GPT-4.1 nano specifies input context (1,047,576 tokens). Only GPT-4.1 nano specifies output context (32,768 tokens).

OpenAI
GPT-4.1 nano
Input1,047,576 tokens
Output32,768 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Sat Jul 18 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both GPT-4.1 nano and Qwen3 VL 32B Thinking support multimodal inputs.

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

GPT-4.1 nano

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

GPT-4.1 nano 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-4.1 nano

Proprietary

Closed source

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

GPT-4.1 nano was released on 2025-04-14, while Qwen3 VL 32B Thinking was released on 2025-09-22.

Qwen3 VL 32B Thinking is 5 months newer than GPT-4.1 nano.

GPT-4.1 nano

Apr 14, 2025

1.3 years ago

Qwen3 VL 32B Thinking

Sep 22, 2025

9 months ago

5mo newer

Knowledge Cutoff

When training data ends

GPT-4.1 nano has a documented knowledge cutoff of 2024-05-31, while Qwen3 VL 32B Thinking's cutoff date is not specified.

We can confirm GPT-4.1 nano's training data extends to 2024-05-31, but cannot make a direct comparison without Qwen3 VL 32B Thinking's cutoff date.

GPT-4.1 nano

May 2024

Qwen3 VL 32B Thinking

—

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

OpenAI
Larger context window (1,047,576 tokens)
Alibaba Cloud / Qwen Team

Qwen3 VL 32B Thinking

View details

Alibaba Cloud / Qwen Team

Has open weights
Higher CharXiv-D score (90.2% vs 73.9%)
Higher CharXiv-R score (65.2% vs 40.5%)
Higher GPQA score (73.1% vs 50.3%)
Higher IFEval score (87.8% vs 74.5%)
Higher MMLU score (88.7% vs 80.1%)
Higher Multi-IF score (78.0% vs 57.2%)

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against GPT-4.1 nano and Qwen3 VL 32B Thinking side-by-side, then vote on the output you prefer.

GPT-4.1 nano
✓ Preferred
Qwen3 VL 32B Thinking
Open in Playground
AI Model Comparison Table
Feature
OpenAI
GPT-4.1 nano
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking

FAQ

Common questions about GPT-4.1 nano vs Qwen3 VL 32B Thinking.

Which is better, GPT-4.1 nano or Qwen3 VL 32B Thinking?

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks. GPT-4.1 nano 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-4.1 nano compare to Qwen3 VL 32B Thinking in benchmarks?

GPT-4.1 nano scores MMLU: 80.1%, IFEval: 74.5%, CharXiv-D: 73.9%, MMMLU: 66.9%, Multi-IF: 57.2%. 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-4.1 nano and Qwen3 VL 32B Thinking?

GPT-4.1 nano supports 1.0M 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-4.1 nano 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-4.1 nano and Qwen3 VL 32B Thinking?

GPT-4.1 nano is developed by OpenAI and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.

Related comparisons