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Gemini 2.0 Flash Thinking vs Gemini 3.7 Flash

Comparing Gemini 2.0 Flash Thinking and Gemini 3.7 Flash across benchmarks, pricing, and capabilities.

Google · Google · Updated for 2026

Which is better?

Gemini 2.0 Flash Thinking and Gemini 3.7 Flash trade strengths across price, capabilities, and technical limits. The better choice depends on the workload.

Based on current benchmark, pricing, and model metadata for 2026.

Choose Gemini 2.0 Flash Thinking

  • you are already invested in the Google ecosystem

Choose Gemini 3.7 Flash

  • you want the most recent training data — it shipped Aug 2026

At a glance

The differences that matter most.

Gemini 2.0 Flash Thinking
Google
Gemini 3.7 Flash
Google
Benchmark wins
—
—
Input price
— / M
$0.75 / M
Output price
— / M
$3.75 / M
Context window
—
1,048,576
Released
Jan 2025
Aug 2026
License
Proprietary
Proprietary

Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Gemini 2.0 Flash Thinking and Gemini 3.7 Flashdon't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Playground indexes and blind preference scores

Context Window

Maximum input and output token capacity

Only Gemini 3.7 Flash specifies input context (1,048,576 tokens). Only Gemini 3.7 Flash specifies output context (65,536 tokens).

Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Google
Gemini 3.7 Flash
Input1,048,576 tokens
Output65,536 tokens
Mon Aug 24 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Both Gemini 2.0 Flash Thinking and Gemini 3.7 Flash support multimodal inputs.

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

Gemini 2.0 Flash Thinking

Text
Images
Audio
Video

Gemini 3.7 Flash

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 2.0 Flash Thinking

Proprietary

Closed source

Gemini 3.7 Flash

Proprietary

Closed source

Release Timeline

When each model was launched

Gemini 2.0 Flash Thinking was released on 2025-01-21, while Gemini 3.7 Flash was released on 2026-08-13.

Gemini 3.7 Flash is 19 months newer than Gemini 2.0 Flash Thinking.

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.6 years ago

Gemini 3.7 Flash

Aug 13, 2026

1 weeks ago

1.6yr newer

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a knowledge cutoff of 2024-08-01, while Gemini 3.7 Flash has a cutoff of 2026-03-31.

Gemini 3.7 Flash has more recent training data (up to 2026-03-31), making it potentially better informed about events through that date compared to Gemini 2.0 Flash Thinking (2024-08-01).

Gemini 2.0 Flash Thinking

Aug 2024

Gemini 3.7 Flash

Mar 2026

1.6 yr newer

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 Gemini 3.7 Flash side-by-side, then vote on the output you prefer.

Gemini 2.0 Flash Thinking
✓ Preferred
Gemini 3.7 Flash
Open in Playground

FAQ

Common questions about Gemini 2.0 Flash Thinking vs Gemini 3.7 Flash.

Which is better, Gemini 2.0 Flash Thinking or Gemini 3.7 Flash?

Gemini 2.0 Flash Thinking (Google) and Gemini 3.7 Flash (Google) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.

How does Gemini 2.0 Flash Thinking compare to Gemini 3.7 Flash in benchmarks?

Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%. Gemini 3.7 Flash scores MRCR v2 (8-needle): 97.0%, Harvey LAB-AA: 90.7%, CharXiv-R: 88.7%, Terminal-Bench 2.1: 85.8%, LVBench: 85.4%.

What are the context window sizes for Gemini 2.0 Flash Thinking and Gemini 3.7 Flash?

Gemini 2.0 Flash Thinking supports an unknown number of tokens and Gemini 3.7 Flash supports 1.0M tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.

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