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Gemini 2.0 Flash Thinking vs LongCat-Flash-Lite

Gemini 2.0 Flash Thinking and LongCat-Flash-Lite are closely matched at 16.7 and 18.7 on the LLM Stats Score.

Google · Meituan · Updated for 2026

Which is better?

Gemini 2.0 Flash Thinking and LongCat-Flash-Lite are closely matched on the overall LLM Stats Score at 16.7 and 18.7.

In the 2 individual benchmarks reported for both models, Gemini 2.0 Flash Thinking wins 2; 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

  • you value its reported benchmark strengths — it wins 2 of 2 exact shared results

Choose LongCat-Flash-Lite

  • you want the most recent training data — it shipped Feb 2026
  • 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
LongCat-Flash-Lite
Meituan
Core performance indexes
LLM Stats Score
16.7
#225
18.7
#216
Reasoning
17.0
#218
18.9
#209
Cost, coverage & limits
Benchmark wins
2 of 2
0 of 2
Input price
— / M
$0.10 / M
Output price
— / M
$0.40 / M
Context window
—
256,000

Individual benchmarks

3 reported for Gemini 2.0 Flash Thinking · 13 for LongCat-Flash-Lite

2 shared

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.

Sun Sep 27 2026 • llm-stats.com

Human preference

Blind head-to-head votes and playground preference scores

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
Sun Sep 27 2026 • llm-stats.com

Input capabilities

Documented input modalities across available providers

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

1.7 years ago

LongCat-Flash-Lite

Feb 5, 2026

7 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→

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

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 and LongCat-Flash-Lite are closely matched on the LLM Stats Score at 16.7 and 18.7. 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 capability indexes, individual benchmarks, pricing, and limits 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 LLM Stats Score (16.7 vs 18.7), 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.

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