Model Comparison

Gemini 1.5 Pro vs Gemini 2.0 Flash ThinkingWhich is better in 2026?

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Verdict: Gemini 1.5 Pro vs Gemini 2.0 Flash Thinking — which is better?

Gemini 1.5 Pro (by Google) and Gemini 2.0 Flash Thinking (by Google) 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 1.5 Pro outperforms in 0 benchmarks, while Gemini 2.0 Flash Thinking is better at 2 benchmarks (GPQA, MMMU). Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Choose Gemini 1.5 Pro if…

  • you want predictable pricing at $2.50/M input and $10.00/M output

Choose Gemini 2.0 Flash Thinking if…

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks
  • you want the most recent training data — it shipped Jan 2025
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Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Gemini 1.5 Pro outperforms in 0 benchmarks, while Gemini 2.0 Flash Thinking is better at 2 benchmarks (GPQA, MMMU).

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Thu Jun 11 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only Gemini 1.5 Pro specifies input context (2,097,152 tokens). Only Gemini 1.5 Pro specifies output context (8,192 tokens).

Google
Gemini 1.5 Pro
Input2,097,152 tokens
Output8,192 tokens
Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Thu Jun 11 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 1.5 Pro and Gemini 2.0 Flash Thinking support multimodal inputs.

They are both capable of processing various types of data, offering versatility in application.

Gemini 1.5 Pro

Text
Images
Audio
Video

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

Gemini 1.5 Pro

Proprietary

Closed source

Gemini 2.0 Flash Thinking

Proprietary

Closed source

Release Timeline

When each model was launched

Gemini 1.5 Pro was released on 2024-05-01, while Gemini 2.0 Flash Thinking was released on 2025-01-21.

Gemini 2.0 Flash Thinking is 9 months newer than Gemini 1.5 Pro.

Gemini 1.5 Pro

May 1, 2024

2.1 years ago

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.4 years ago

8mo newer

Knowledge Cutoff

When training data ends

Gemini 1.5 Pro has a knowledge cutoff of 2023-11-01, while Gemini 2.0 Flash Thinking has a cutoff of 2024-08-01.

Gemini 2.0 Flash Thinking has more recent training data (up to 2024-08-01), making it potentially better informed about events through that date compared to Gemini 1.5 Pro (2023-11-01).

Gemini 1.5 Pro

Nov 2023

Gemini 2.0 Flash Thinking

Aug 2024

9 mo newer

Outputs Comparison

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Key Takeaways

Google
Larger context window (2,097,152 tokens)
Google
Higher GPQA score (74.2% vs 59.1%)
Higher MMMU score (75.4% vs 65.9%)

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemini 1.5 Pro
Google
Gemini 2.0 Flash Thinking

FAQ

Common questions about Gemini 1.5 Pro vs Gemini 2.0 Flash Thinking.

Which is better, Gemini 1.5 Pro or Gemini 2.0 Flash Thinking?

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks. Gemini 1.5 Pro is made by Google and Gemini 2.0 Flash Thinking is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.

How does Gemini 1.5 Pro compare to Gemini 2.0 Flash Thinking in benchmarks?

Gemini 1.5 Pro scores XSTest: 98.8%, FLEURS: 93.3%, HellaSwag: 93.3%, GSM8k: 90.8%, BIG-Bench Hard: 89.2%. Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%.

What are the context window sizes for Gemini 1.5 Pro and Gemini 2.0 Flash Thinking?

Gemini 1.5 Pro supports 2.1M tokens and Gemini 2.0 Flash Thinking supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

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