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Gemini 2.0 Flash Thinking vs Phi-3.5-mini-instruct

Gemini 2.0 Flash Thinking leads the LLM Stats Score 16.7 to -3.8.

Google · Microsoft · Updated for 2026

Which is better?

Gemini 2.0 Flash Thinking leads the overall LLM Stats Score 16.7 to -3.8, ranking #226 overall.

In the 1 individual benchmarks reported for both models, Gemini 2.0 Flash Thinking wins 1; this is a narrower head-to-head signal than the composite indexes.

Based on current LLM Stats indexes, shared benchmarks, pricing, and model metadata for 2026.

Choose Gemini 2.0 Flash Thinking

  • overall performance matters — it scores 16.7 and ranks #226 on LLM Stats
  • your work emphasizes reasoning — it leads those capability indexes
  • you value its reported benchmark strengths — it wins 1 of 1 exact shared results
  • you want the most recent training data — it shipped Jan 2025

Choose Phi-3.5-mini-instruct

  • you need open weights you can self-host or fine-tune

At a glance

The differences that matter most.

Gemini 2.0 Flash Thinking
Google
Phi-3.5-mini-instruct
Microsoft
Core performance indexes
LLM Stats Score
16.7
#226
-3.8
#353
Reasoning
17.0
#219
-4.7
#349
Cost, coverage & limits
Benchmark wins
1 of 1
0 of 1
Input price
— / M
$0.10 / M
Output price
— / M
$0.10 / M
Context window
—
128,000

Individual benchmarks

3 reported for Gemini 2.0 Flash Thinking · 31 for Phi-3.5-mini-instruct

1 shared

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.

Mon Sep 28 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

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
Mon Sep 28 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

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

1.7 years ago

5mo newer
Phi-3.5-mini-instruct

Aug 23, 2024

2.1 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

Notice missing or incorrect data?Start an Issue discussion→

Judge for yourself.

Run your own prompts against Gemini 2.0 Flash Thinking and Phi-3.5-mini-instruct side-by-side, then vote on the output you prefer.

Gemini 2.0 Flash Thinking
✓ Preferred
Phi-3.5-mini-instruct
Open in Playground

FAQ

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

Which is better, Gemini 2.0 Flash Thinking or Phi-3.5-mini-instruct?

Gemini 2.0 Flash Thinking leads the LLM Stats Score 16.7 to -3.8. 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 capability indexes, individual benchmarks, pricing, and limits above.

How does Gemini 2.0 Flash Thinking compare to Phi-3.5-mini-instruct in benchmarks?

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%.

What are the context window sizes for Gemini 2.0 Flash Thinking and Phi-3.5-mini-instruct?

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.

What are the main differences between Gemini 2.0 Flash Thinking and Phi-3.5-mini-instruct?

Key differences include LLM Stats Score (16.7 vs -3.8), multimodal support (yes vs no), licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.

Who makes Gemini 2.0 Flash Thinking and Phi-3.5-mini-instruct?

Gemini 2.0 Flash Thinking is developed by Google and Phi-3.5-mini-instruct is developed by Microsoft.

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