Model Comparison

Gemini 3.1 Pro vs Phi-3.5-mini-instructWhich is better in 2026?

Gemini 3.1 Pro significantly outperforms across most benchmarks. Phi-3.5-mini-instruct is 56.3x cheaper per token.

Verdict: Gemini 3.1 Pro vs Phi-3.5-mini-instruct — which is better?

Gemini 3.1 Pro (by Google) and Phi-3.5-mini-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 2 benchmarks (GPQA, MMMLU), while Phi-3.5-mini-instruct is better at 0 benchmarks. Gemini 3.1 Pro significantly outperforms across most benchmarks.

On price, Phi-3.5-mini-instruct is roughly 56.3x 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 2 of 2 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-3.5-mini-instruct if…

  • cost matters — it's about 56.3x cheaper per token
  • you need open weights you can self-host or fine-tune
Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Gemini 3.1 Pro outperforms in 2 benchmarks (GPQA, MMMLU), while Phi-3.5-mini-instruct is better at 0 benchmarks.

Gemini 3.1 Pro significantly outperforms across most benchmarks.

Sun Jun 07 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Phi-3.5-mini-instruct costs less

For input processing, Gemini 3.1 Pro ($2.50/1M tokens) is 25.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

For output processing, Gemini 3.1 Pro ($15.00/1M tokens) is 150.0x more expensive than Phi-3.5-mini-instruct ($0.10/1M tokens).

In conclusion, Gemini 3.1 Pro is more expensive than Phi-3.5-mini-instruct.*

* Using a 3:1 ratio of input to output tokens

Lowest available price from all providers
Sun Jun 07 2026 • llm-stats.com
Google
Gemini 3.1 Pro
Input tokens$2.50
Output tokens$15.00
Best providerGoogle
Microsoft
Phi-3.5-mini-instruct
Input tokens$0.10
Output tokens$0.10
Best providerAzure
Notice missing or incorrect data?Start an Issue→

Context Window

Maximum input and output token capacity

Gemini 3.1 Pro accepts 1,048,576 input tokens compared to Phi-3.5-mini-instruct's 128,000 tokens. Phi-3.5-mini-instruct can generate longer responses up to 128,000 tokens, while Gemini 3.1 Pro is limited to 65,536 tokens.

Google
Gemini 3.1 Pro
Input1,048,576 tokens
Output65,536 tokens
Microsoft
Phi-3.5-mini-instruct
Input128,000 tokens
Output128,000 tokens
Sun Jun 07 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 3.1 Pro supports multimodal inputs, whereas Phi-3.5-mini-instruct does not.

Gemini 3.1 Pro can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemini 3.1 Pro

Text
Images
Audio
Video

Phi-3.5-mini-instruct

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 3.1 Pro 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 3.1 Pro

Proprietary

Closed source

Phi-3.5-mini-instruct

MIT

Open weights

Release Timeline

When each model was launched

Gemini 3.1 Pro was released on 2026-02-19, while Phi-3.5-mini-instruct was released on 2024-08-23.

Gemini 3.1 Pro is 18 months newer than Phi-3.5-mini-instruct.

Gemini 3.1 Pro

Feb 19, 2026

3 months ago

1.5yr newer
Phi-3.5-mini-instruct

Aug 23, 2024

1.8 years ago

Knowledge Cutoff

When training data ends

Gemini 3.1 Pro has a documented knowledge cutoff of 2025-01-31, while Phi-3.5-mini-instruct's cutoff date is not specified.

We can confirm Gemini 3.1 Pro's training data extends to 2025-01-31, but cannot make a direct comparison without Phi-3.5-mini-instruct's cutoff date.

Gemini 3.1 Pro

Jan 2025

Phi-3.5-mini-instruct

—

Provider Availability

Gemini 3.1 Pro is available from Google. Phi-3.5-mini-instruct is available from Azure.

Gemini 3.1 Pro

google logo
Google
Input Price:Input: $2.50/1MOutput Price:Output: $15.00/1M

Phi-3.5-mini-instruct

azure logo
Azure
Input Price:Input: $0.10/1MOutput Price:Output: $0.10/1M
* Prices shown are per million tokens

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Google
Larger context window (1,048,576 tokens)
Supports multimodal inputs
Higher GPQA score (94.3% vs 30.4%)
Higher MMMLU score (92.6% vs 55.4%)
Microsoft
Less expensive input tokens
Less expensive output tokens
Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 3.1 Pro
Microsoft
Phi-3.5-mini-instruct

FAQ

Common questions about Gemini 3.1 Pro vs Phi-3.5-mini-instruct.

Which is better, Gemini 3.1 Pro or Phi-3.5-mini-instruct?

Gemini 3.1 Pro significantly outperforms across most benchmarks. Gemini 3.1 Pro 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.

How does Gemini 3.1 Pro compare to Phi-3.5-mini-instruct in benchmarks?

Gemini 3.1 Pro scores t2-bench: 99.3%, LiveCodeBench Pro: 96.2%, GPQA: 94.3%, MMMLU: 92.6%, BrowseComp: 85.9%. Phi-3.5-mini-instruct scores GSM8k: 86.2%, ARC-C: 84.6%, RULER: 84.1%, PIQA: 81.0%, OpenBookQA: 79.2%.

Is Gemini 3.1 Pro cheaper than Phi-3.5-mini-instruct?

Phi-3.5-mini-instruct is 25.0x cheaper for input tokens. Gemini 3.1 Pro costs $2.50/M input and $15.00/M output via google. Phi-3.5-mini-instruct costs $0.10/M input and $0.10/M output via azure.

What are the context window sizes for Gemini 3.1 Pro and Phi-3.5-mini-instruct?

Gemini 3.1 Pro supports 1.0M 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.

What are the main differences between Gemini 3.1 Pro and Phi-3.5-mini-instruct?

Key differences include context window (1.0M vs 128K), input pricing ($2.50 vs $0.10/M), multimodal support (yes vs no), licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 3.1 Pro and Phi-3.5-mini-instruct?

Gemini 3.1 Pro is developed by Google and Phi-3.5-mini-instruct is developed by Microsoft.

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