Model Comparison

Gemma 3n E4B vs LongCat-Flash-Thinking

Comparing Gemma 3n E4B and LongCat-Flash-Thinking across benchmarks, pricing, and capabilities.

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Performance Benchmarks

Comparative analysis across standard metrics

No common benchmarks found

Gemma 3n E4B and LongCat-Flash-Thinking don't have any common benchmark datasets to compare. They may have been evaluated on different testing suites.

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Tue Apr 14 2026 • llm-stats.com
Google
Gemma 3n E4B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Meituan
LongCat-Flash-Thinking
Input tokens$0.30
Output tokens$1.20
Best providerMeituan
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

552.0B diff

LongCat-Flash-Thinking has 552.0B more parameters than Gemma 3n E4B, making it 6900.0% larger.

Google
Gemma 3n E4B
8.0Bparameters
Meituan
LongCat-Flash-Thinking
560.0Bparameters
8.0B
Gemma 3n E4B
560.0B
LongCat-Flash-Thinking

Context Window

Maximum input and output token capacity

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

Google
Gemma 3n E4B
Input- tokens
Output- tokens
Meituan
LongCat-Flash-Thinking
Input128,000 tokens
Output128,000 tokens
Tue Apr 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3n E4B supports multimodal inputs, whereas LongCat-Flash-Thinking does not.

Gemma 3n E4B can handle both text and other forms of data like images, making it suitable for multimodal applications.

Gemma 3n E4B

Text
Images
Audio
Video

LongCat-Flash-Thinking

Text
Images
Audio
Video

License

Usage and distribution terms

Gemma 3n E4B is licensed under a proprietary license, while LongCat-Flash-Thinking uses MIT.

License differences may affect how you can use these models in commercial or open-source projects.

Gemma 3n E4B

Proprietary

Closed source

LongCat-Flash-Thinking

MIT

Open weights

Release Timeline

When each model was launched

Gemma 3n E4B was released on 2025-06-26, while LongCat-Flash-Thinking was released on 2025-09-22.

LongCat-Flash-Thinking is 3 months newer than Gemma 3n E4B.

Gemma 3n E4B

Jun 26, 2025

9 months ago

LongCat-Flash-Thinking

Sep 22, 2025

6 months ago

2mo newer

Knowledge Cutoff

When training data ends

Gemma 3n E4B has a documented knowledge cutoff of 2024-06-01, while LongCat-Flash-Thinking's cutoff date is not specified.

We can confirm Gemma 3n E4B's training data extends to 2024-06-01, but cannot make a direct comparison without LongCat-Flash-Thinking's cutoff date.

Gemma 3n E4B

Jun 2024

LongCat-Flash-Thinking

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Outputs Comparison

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Key Takeaways

Google
Supports multimodal inputs
Meituan
Larger context window (128,000 tokens)
Has open weights

Detailed Comparison

AI Model Comparison Table
Feature
Google
Gemma 3n E4B
Meituan
LongCat-Flash-Thinking

FAQ

Common questions about Gemma 3n E4B vs LongCat-Flash-Thinking

Gemma 3n E4B (Google) and LongCat-Flash-Thinking (Meituan) each have strengths in different areas. Compare their benchmark scores, pricing, context windows, and capabilities above to determine which fits your needs.
Gemma 3n E4B scores ARC-E: 81.6%, BoolQ: 81.6%, PIQA: 81.0%, HellaSwag: 78.6%, Winogrande: 71.7%. LongCat-Flash-Thinking scores MATH-500: 99.2%, ZebraLogic: 95.5%, AIME 2024: 93.3%, AIME 2025: 90.6%, MMLU-Redux: 89.3%.
Gemma 3n E4B supports an unknown number of tokens and LongCat-Flash-Thinking supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (yes vs no), licensing (Proprietary vs MIT). See the full comparison above for benchmark-by-benchmark results.
Gemma 3n E4B is developed by Google and LongCat-Flash-Thinking is developed by Meituan.