Model Comparison

Gemini 2.0 Flash Thinking vs Phi-4-multimodal-instruct

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

Gemini 2.0 Flash Thinking outperforms in 1 benchmarks (MMMU), while Phi-4-multimodal-instruct is better at 0 benchmarks.

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Mon May 25 2026 • llm-stats.com

Arena Performance

Human preference votes

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).

Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Microsoft
Phi-4-multimodal-instruct
Input128,000 tokens
Output128,000 tokens
Mon May 25 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 2.0 Flash Thinking and Phi-4-multimodal-instruct support multimodal inputs.

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

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

Phi-4-multimodal-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 2.0 Flash Thinking is licensed under a proprietary license, while Phi-4-multimodal-instruct uses MIT.

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

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Phi-4-multimodal-instruct

MIT

Open weights

Release Timeline

When each model was launched

Gemini 2.0 Flash Thinking was released on 2025-01-21, while Phi-4-multimodal-instruct was released on 2025-02-01.

Phi-4-multimodal-instruct is 0 month newer than Gemini 2.0 Flash Thinking.

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.3 years ago

Phi-4-multimodal-instruct

Feb 1, 2025

1.3 years ago

1w newer

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a knowledge cutoff of 2024-08-01, while Phi-4-multimodal-instruct has a cutoff of 2024-06-01.

Gemini 2.0 Flash Thinking has more recent training data (up to 2024-08-01), making it potentially better informed about events through that date compared to Phi-4-multimodal-instruct (2024-06-01).

Gemini 2.0 Flash Thinking

Aug 2024

2 mo newer
Phi-4-multimodal-instruct

Jun 2024

Outputs Comparison

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Key Takeaways

Google
Higher MMMU score (75.4% vs 55.1%)
Microsoft
Larger context window (128,000 tokens)
Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 2.0 Flash Thinking
Microsoft
Phi-4-multimodal-instruct

FAQ

Common questions about Gemini 2.0 Flash Thinking vs Phi-4-multimodal-instruct.

Which is better, Gemini 2.0 Flash Thinking or Phi-4-multimodal-instruct?

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks. Gemini 2.0 Flash Thinking is made by Google and Phi-4-multimodal-instruct is made by Microsoft. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Gemini 2.0 Flash Thinking compare to Phi-4-multimodal-instruct in benchmarks?

Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%. Phi-4-multimodal-instruct scores ScienceQA Visual: 97.5%, DocVQA: 93.2%, MMBench: 86.7%, POPE: 85.6%, OCRBench: 84.4%.

What are the context window sizes for Gemini 2.0 Flash Thinking and Phi-4-multimodal-instruct?

Gemini 2.0 Flash Thinking supports an unknown number of tokens and Phi-4-multimodal-instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

What are the main differences between Gemini 2.0 Flash Thinking and Phi-4-multimodal-instruct?

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

Who makes Gemini 2.0 Flash Thinking and Phi-4-multimodal-instruct?

Gemini 2.0 Flash Thinking is developed by Google and Phi-4-multimodal-instruct is developed by Microsoft.