Model Comparison

Gemini 2.0 Flash Thinking vs Phi-3.5-mini-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 (GPQA), while Phi-3.5-mini-instruct is better at 0 benchmarks.

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Wed Apr 01 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
Wed Apr 01 2026 • llm-stats.com
Google
Gemini 2.0 Flash Thinking
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Microsoft
Phi-3.5-mini-instruct
Input tokens$0.10
Output tokens$0.10
Best providerAzure
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Context Window

Maximum input and output token capacity

Only Phi-3.5-mini-instruct specifies input context (128,000 tokens). Only Phi-3.5-mini-instruct specifies output context (128,000 tokens).

Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Wed Apr 01 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.0 Flash Thinking supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.

Gemini 2.0 Flash Thinking can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

Phi-3.5-mini-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 2.0 Flash Thinking is licensed under a proprietary license, while Phi-3.5-mini-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-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

Gemini 2.0 Flash Thinking was released on 2025-01-21, while Phi-3.5-mini-instruct was released on 2024-08-23.

Gemini 2.0 Flash Thinking is 5 months newer than Phi-3.5-mini-instruct.

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.2 years ago

5mo newer
Phi-3.5-mini-instruct

Aug 23, 2024

1.6 years ago

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a documented knowledge cutoff of 2024-08-01, while Phi-3.5-mini-instruct's cutoff date is not specified.

We can confirm Gemini 2.0 Flash Thinking's training data extends to 2024-08-01, but cannot make a direct comparison without Phi-3.5-mini-instruct's cutoff date.

Gemini 2.0 Flash Thinking

Aug 2024

Phi-3.5-mini-instruct

—

Outputs Comparison

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Key Takeaways

Google
Supports multimodal inputs
Higher GPQA score (74.2% vs 30.4%)
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-3.5-mini-instruct

FAQ

Common questions about Gemini 2.0 Flash Thinking vs Phi-3.5-mini-instruct

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks. Gemini 2.0 Flash Thinking is made by Google and Phi-3.5-mini-instruct is made by Microsoft. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%. Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.
Gemini 2.0 Flash Thinking supports an unknown number of tokens and Phi-3.5-mini-instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (yes vs no), licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.
Gemini 2.0 Flash Thinking is developed by Google and Phi-3.5-mini-instruct is developed by Microsoft.