Model Comparison
Gemini 3.1 Pro vs Phi-4-multimodal-instructWhich is better in 2026?
Gemini 3.1 Pro significantly outperforms across most benchmarks. Phi-4-multimodal-instruct is 90.0x cheaper per token.
Verdict: Gemini 3.1 Pro vs Phi-4-multimodal-instruct — which is better?
Gemini 3.1 Pro (by Google) and Phi-4-multimodal-instruct (by Microsoft) 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.
Gemini 3.1 Pro outperforms in 1 benchmarks (MMMU-Pro), while Phi-4-multimodal-instruct is better at 0 benchmarks. Gemini 3.1 Pro significantly outperforms across most benchmarks.
On price, Phi-4-multimodal-instruct is roughly 90.0x cheaper per token on a blended 3:1 input/output basis, which adds up quickly at production volume.
Gemini 3.1 Pro also accepts a larger context window (1,048,576 input tokens), making it the stronger choice for long documents and large codebases.
Choose Gemini 3.1 Pro if…
- you want the strongest raw capability — it leads on 1 of 1 shared benchmarks
- you process long inputs — it offers a 1,048,576 token context window
- you want the most recent training data — it shipped Feb 2026
Choose Phi-4-multimodal-instruct if…
- cost matters — it's about 90.0x cheaper per token
- you need open weights you can self-host or fine-tune
Performance Benchmarks
Comparative analysis across standard metrics
Gemini 3.1 Pro outperforms in 1 benchmarks (MMMU-Pro), while Phi-4-multimodal-instruct is better at 0 benchmarks.
Gemini 3.1 Pro significantly outperforms across most benchmarks.
Arena Performance
Human preference votes
Pricing Analysis
Price comparison per million tokens
For input processing, Gemini 3.1 Pro ($2.50/1M tokens) is 50.0x more expensive than Phi-4-multimodal-instruct ($0.05/1M tokens).
For output processing, Gemini 3.1 Pro ($15.00/1M tokens) is 150.0x more expensive than Phi-4-multimodal-instruct ($0.10/1M tokens).
In conclusion, Gemini 3.1 Pro is more expensive than Phi-4-multimodal-instruct.*
* Using a 3:1 ratio of input to output tokens
Context Window
Maximum input and output token capacity
Gemini 3.1 Pro accepts 1,048,576 input tokens compared to Phi-4-multimodal-instruct's 128,000 tokens. Phi-4-multimodal-instruct can generate longer responses up to 128,000 tokens, while Gemini 3.1 Pro is limited to 65,536 tokens.
Input Capabilities
Supported data types and modalities
Both Gemini 3.1 Pro and Phi-4-multimodal-instruct support multimodal inputs.
They are both capable of processing various types of data, offering versatility in application.
Gemini 3.1 Pro
Phi-4-multimodal-instruct
License
Usage and distribution terms
Gemini 3.1 Pro 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.
Proprietary
Closed source
MIT
Open weights
Release Timeline
When each model was launched
Gemini 3.1 Pro was released on 2026-02-19, while Phi-4-multimodal-instruct was released on 2025-02-01.
Gemini 3.1 Pro is 13 months newer than Phi-4-multimodal-instruct.
Feb 19, 2026
3 months ago
1.0yr newerFeb 1, 2025
1.3 years ago
Knowledge Cutoff
When training data ends
Gemini 3.1 Pro has a knowledge cutoff of 2025-01-31, while Phi-4-multimodal-instruct has a cutoff of 2024-06-01.
Gemini 3.1 Pro has more recent training data (up to 2025-01-31), making it potentially better informed about events through that date compared to Phi-4-multimodal-instruct (2024-06-01).
Jan 2025
7 mo newerJun 2024
Provider Availability
Gemini 3.1 Pro is available from Google. Phi-4-multimodal-instruct is available from DeepInfra.
Gemini 3.1 Pro
Phi-4-multimodal-instruct
Outputs Comparison
Key Takeaways
Detailed Comparison
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FAQ
Common questions about Gemini 3.1 Pro vs Phi-4-multimodal-instruct.