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Phi-4-multimodal-instruct vs Qwen3 VL 32B Thinking

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

Microsoft · Alibaba Cloud / Qwen Team · Updated for 2026

Which is better?

Phi-4-multimodal-instruct outperforms in 0 benchmarks, while Qwen3 VL 32B Thinking is better at 4 benchmarks (AI2D, BLINK, MMMU-Pro, OCRBench). Qwen3 VL 32B Thinking significantly outperforms across most benchmarks.

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

Choose Phi-4-multimodal-instruct

  • you want predictable pricing at $0.05/M input and $0.10/M output

Choose Qwen3 VL 32B Thinking

  • you want the strongest raw capability — it leads on 4 of 4 shared benchmarks
  • you want the most recent training data — it shipped Sep 2025

At a glance

The differences that matter most.

Benchmark wins
0 of 4
4 of 4
Input price
$0.05 / M
— / M
Output price
$0.10 / M
— / M
Context window
128,000
Released
Feb 2025
Sep 2025
License
MIT
Apache 2.0

Performance Benchmarks

Comparative analysis across standard metrics

4 benchmarks

Phi-4-multimodal-instruct outperforms in 0 benchmarks, while Qwen3 VL 32B Thinking is better at 4 benchmarks (AI2D, BLINK, MMMU-Pro, OCRBench).

Qwen3 VL 32B Thinking 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

27.4B diff

Qwen3 VL 32B Thinking has 27.4B more parameters than Phi-4-multimodal-instruct, making it 489.3% larger.

Microsoft
Phi-4-multimodal-instruct
5.6Bparameters
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
33.0Bparameters
5.6B
Phi-4-multimodal-instruct
33.0B
Qwen3 VL 32B Thinking

Context Window

Maximum input and output token capacity

Only Phi-4-multimodal-instruct specifies input context (128,000 tokens). Only Phi-4-multimodal-instruct specifies output context (128,000 tokens).

Microsoft
Phi-4-multimodal-instruct
Input128,000 tokens
Output128,000 tokens
Alibaba Cloud / Qwen Team
Qwen3 VL 32B Thinking
Input- tokens
Output- tokens
Tue Aug 25 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Phi-4-multimodal-instruct and Qwen3 VL 32B Thinking support multimodal inputs.

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

Phi-4-multimodal-instruct

Text
Images
Audio
Video

Qwen3 VL 32B Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Phi-4-multimodal-instruct is licensed under MIT, 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.

Phi-4-multimodal-instruct

MIT

Open weights

Qwen3 VL 32B Thinking

Apache 2.0

Open weights

Release Timeline

When each model was launched

Phi-4-multimodal-instruct was released on 2025-02-01, while Qwen3 VL 32B Thinking was released on 2025-09-22.

Qwen3 VL 32B Thinking is 8 months newer than Phi-4-multimodal-instruct.

Phi-4-multimodal-instruct

Feb 1, 2025

1.6 years ago

Qwen3 VL 32B Thinking

Sep 22, 2025

11 months ago

7mo newer

Knowledge Cutoff

When training data ends

Phi-4-multimodal-instruct has a documented knowledge cutoff of 2024-06-01, while Qwen3 VL 32B Thinking's cutoff date is not specified.

We can confirm Phi-4-multimodal-instruct's training data extends to 2024-06-01, but cannot make a direct comparison without Qwen3 VL 32B Thinking's cutoff date.

Phi-4-multimodal-instruct

Jun 2024

Qwen3 VL 32B Thinking

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion

Judge for yourself.

Run your own prompts against Phi-4-multimodal-instruct and Qwen3 VL 32B Thinking side-by-side, then vote on the output you prefer.

Phi-4-multimodal-instruct
✓ Preferred
Qwen3 VL 32B Thinking
Open in Playground

FAQ

Common questions about Phi-4-multimodal-instruct vs Qwen3 VL 32B Thinking.

Which is better, Phi-4-multimodal-instruct or Qwen3 VL 32B Thinking?

Qwen3 VL 32B Thinking significantly outperforms across most benchmarks. Phi-4-multimodal-instruct is made by Microsoft 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 Phi-4-multimodal-instruct compare to Qwen3 VL 32B Thinking in benchmarks?

Phi-4-multimodal-instruct scores ScienceQA Visual: 97.5%, DocVQA: 93.2%, MMBench: 86.7%, POPE: 85.6%, OCRBench: 84.4%. 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 Phi-4-multimodal-instruct and Qwen3 VL 32B Thinking?

Phi-4-multimodal-instruct 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 Phi-4-multimodal-instruct and Qwen3 VL 32B Thinking?

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

Who makes Phi-4-multimodal-instruct and Qwen3 VL 32B Thinking?

Phi-4-multimodal-instruct is developed by Microsoft and Qwen3 VL 32B Thinking is developed by Alibaba Cloud / Qwen Team.