Model Comparison

Gemini 2.0 Flash Thinking vs LongCat-Flash-LiteWhich is better in 2026?

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Verdict: Gemini 2.0 Flash Thinking vs LongCat-Flash-Lite — which is better?

Gemini 2.0 Flash Thinking (by Google) and LongCat-Flash-Lite (by Meituan) 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 2.0 Flash Thinking outperforms in 2 benchmarks (AIME 2024, GPQA), while LongCat-Flash-Lite is better at 0 benchmarks. Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Choose Gemini 2.0 Flash Thinking if…

  • you want the strongest raw capability — it leads on 2 of 2 shared benchmarks

Choose LongCat-Flash-Lite if…

  • you want the most recent training data — it shipped Feb 2026
  • you need open weights you can self-host or fine-tune

Performance Benchmarks

Comparative analysis across standard metrics

2 benchmarks

Gemini 2.0 Flash Thinking outperforms in 2 benchmarks (AIME 2024, GPQA), while LongCat-Flash-Lite is better at 0 benchmarks.

Gemini 2.0 Flash Thinking significantly outperforms across most benchmarks.

Wed Jul 29 2026 • llm-stats.com

Arena Performance

Human preference votes

Context Window

Maximum input and output token capacity

Only LongCat-Flash-Lite specifies input context (256,000 tokens). Only LongCat-Flash-Lite specifies output context (128,000 tokens).

Google
Gemini 2.0 Flash Thinking
Input- tokens
Output- tokens
Meituan
LongCat-Flash-Lite
Input256,000 tokens
Output128,000 tokens
Wed Jul 29 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemini 2.0 Flash Thinking supports multimodal inputs, whereas LongCat-Flash-Lite 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

LongCat-Flash-Lite

Text
Images
Audio
Video

License

Usage and distribution terms

Gemini 2.0 Flash Thinking is licensed under a proprietary license, while LongCat-Flash-Lite 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

LongCat-Flash-Lite

MIT

Open weights

Release Timeline

When each model was launched

Gemini 2.0 Flash Thinking was released on 2025-01-21, while LongCat-Flash-Lite was released on 2026-02-05.

LongCat-Flash-Lite is 13 months newer than Gemini 2.0 Flash Thinking.

Gemini 2.0 Flash Thinking

Jan 21, 2025

1.5 years ago

LongCat-Flash-Lite

Feb 5, 2026

5 months ago

1.0yr newer

Knowledge Cutoff

When training data ends

Gemini 2.0 Flash Thinking has a documented knowledge cutoff of 2024-08-01, while LongCat-Flash-Lite'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 LongCat-Flash-Lite's cutoff date.

Gemini 2.0 Flash Thinking

Aug 2024

LongCat-Flash-Lite

—

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Google
Supports multimodal inputs
Higher AIME 2024 score (73.3% vs 72.2%)
Higher GPQA score (74.2% vs 66.8%)
Meituan
Larger context window (256,000 tokens)
Has open weights

Detailed Comparison

Interactive Arena

Judge for yourself.

Run your own prompts against Gemini 2.0 Flash Thinking and LongCat-Flash-Lite side-by-side, then vote on the output you prefer.

Gemini 2.0 Flash Thinking
✓ Preferred
LongCat-Flash-Lite
Open in Playground
AI Model Comparison Table
Feature
Google
Gemini 2.0 Flash Thinking
Meituan
LongCat-Flash-Lite

FAQ

Common questions about Gemini 2.0 Flash Thinking vs LongCat-Flash-Lite.

Which is better, Gemini 2.0 Flash Thinking or LongCat-Flash-Lite?

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

How does Gemini 2.0 Flash Thinking compare to LongCat-Flash-Lite in benchmarks?

Gemini 2.0 Flash Thinking scores MMMU: 75.4%, GPQA: 74.2%, AIME 2024: 73.3%. LongCat-Flash-Lite scores MATH-500: 96.8%, MMLU: 85.5%, CMMLU: 82.5%, MMLU-Pro: 78.3%, Tau2 Retail: 73.1%.

What are the context window sizes for Gemini 2.0 Flash Thinking and LongCat-Flash-Lite?

Gemini 2.0 Flash Thinking supports an unknown number of tokens and LongCat-Flash-Lite supports 256K 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 LongCat-Flash-Lite?

Key differences include 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 LongCat-Flash-Lite?

Gemini 2.0 Flash Thinking is developed by Google and LongCat-Flash-Lite is developed by Meituan.

Related comparisons