Model Comparison

Gemini 1.0 Pro vs Phi-3.5-vision-instruct

Gemini 1.0 Pro significantly outperforms across most benchmarks.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Gemini 1.0 Pro outperforms in 2 benchmarks (MathVista, MMMU), while Phi-3.5-vision-instruct is better at 0 benchmarks.

Gemini 1.0 Pro significantly outperforms across most benchmarks.

Tue Mar 31 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Tue Mar 31 2026 • llm-stats.com
Google
Gemini 1.0 Pro
Input tokens$0.50
Output tokens$1.50
Best providerGoogle
Microsoft
Phi-3.5-vision-instruct
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Only Gemini 1.0 Pro specifies input context (32,760 tokens). Only Gemini 1.0 Pro specifies output context (8,192 tokens).

Google
Gemini 1.0 Pro
Input32,760 tokens
Output8,192 tokens
Microsoft
Phi-3.5-vision-instruct
Input- tokens
Output- tokens
Tue Mar 31 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Phi-3.5-vision-instruct supports multimodal inputs, whereas Gemini 1.0 Pro does not.

Phi-3.5-vision-instruct can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 1.0 Pro

Text
Images
Audio
Video

Phi-3.5-vision-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 1.0 Pro is licensed under a proprietary license, while Phi-3.5-vision-instruct uses MIT.

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

Gemini 1.0 Pro

Proprietary

Closed source

Phi-3.5-vision-instruct

MIT

Open weights

Release Timeline

When each model was launched

Gemini 1.0 Pro was released on 2024-02-15, while Phi-3.5-vision-instruct was released on 2024-08-23.

Phi-3.5-vision-instruct is 6 months newer than Gemini 1.0 Pro.

Gemini 1.0 Pro

Feb 15, 2024

2.1 years ago

Phi-3.5-vision-instruct

Aug 23, 2024

1.6 years ago

6mo newer

Knowledge Cutoff

When training data ends

Gemini 1.0 Pro has a documented knowledge cutoff of 2024-02-01, while Phi-3.5-vision-instruct's cutoff date is not specified.

We can confirm Gemini 1.0 Pro's training data extends to 2024-02-01, but cannot make a direct comparison without Phi-3.5-vision-instruct's cutoff date.

Gemini 1.0 Pro

Feb 2024

Phi-3.5-vision-instruct

—

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Google
Larger context window (32,760 tokens)
Higher MathVista score (46.6% vs 43.9%)
Higher MMMU score (47.9% vs 43.0%)
Microsoft
Supports multimodal inputs
Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 1.0 Pro
Microsoft
Phi-3.5-vision-instruct

FAQ

Common questions about Gemini 1.0 Pro vs Phi-3.5-vision-instruct

Gemini 1.0 Pro significantly outperforms across most benchmarks. Gemini 1.0 Pro is made by Google and Phi-3.5-vision-instruct is made by Microsoft. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Gemini 1.0 Pro scores BIG-Bench: 75.0%, MMLU: 71.8%, WMT23: 71.7%, EgoSchema: 55.7%, MMMU: 47.9%. Phi-3.5-vision-instruct scores ScienceQA: 91.3%, POPE: 86.1%, MMBench: 81.9%, ChartQA: 81.8%, AI2D: 78.1%.
Gemini 1.0 Pro supports 33K tokens and Phi-3.5-vision-instruct supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (no vs yes), licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.
Gemini 1.0 Pro is developed by Google and Phi-3.5-vision-instruct is developed by Microsoft.