Model Comparison

Gemma 3n E4B vs LongCat-Flash-Chat

LongCat-Flash-Chat significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

1 benchmarks

Gemma 3n E4B outperforms in 0 benchmarks, while LongCat-Flash-Chat is better at 1 benchmark (DROP).

LongCat-Flash-Chat significantly outperforms across most benchmarks.

Fri Apr 17 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Cost data unavailable.

Lowest available price from all providers
Fri Apr 17 2026 • llm-stats.com
Google
Gemma 3n E4B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Meituan
LongCat-Flash-Chat
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-Chat has 552.0B more parameters than Gemma 3n E4B, making it 6900.0% larger.

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

Context Window

Maximum input and output token capacity

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

Google
Gemma 3n E4B
Input- tokens
Output- tokens
Meituan
LongCat-Flash-Chat
Input128,000 tokens
Output128,000 tokens
Fri Apr 17 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3n E4B supports multimodal inputs, whereas LongCat-Flash-Chat 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-Chat

Text
Images
Audio
Video

License

Usage and distribution terms

Gemma 3n E4B is licensed under a proprietary license, while LongCat-Flash-Chat 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-Chat

MIT

Open weights

Release Timeline

When each model was launched

Gemma 3n E4B was released on 2025-06-26, while LongCat-Flash-Chat was released on 2025-08-29.

LongCat-Flash-Chat is 2 months newer than Gemma 3n E4B.

Gemma 3n E4B

Jun 26, 2025

9 months ago

LongCat-Flash-Chat

Aug 29, 2025

7 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-Chat'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-Chat's cutoff date.

Gemma 3n E4B

Jun 2024

LongCat-Flash-Chat

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Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Google
Supports multimodal inputs
Meituan
Larger context window (128,000 tokens)
Has open weights
Higher DROP score (79.1% vs 60.8%)

Detailed Comparison

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

FAQ

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

LongCat-Flash-Chat significantly outperforms across most benchmarks. Gemma 3n E4B is made by Google and LongCat-Flash-Chat is made by Meituan. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
Gemma 3n E4B scores ARC-E: 81.6%, BoolQ: 81.6%, PIQA: 81.0%, HellaSwag: 78.6%, Winogrande: 71.7%. LongCat-Flash-Chat scores MATH-500: 96.4%, MMLU: 89.7%, IFEval: 89.6%, ZebraLogic: 89.3%, HumanEval: 88.4%.
Gemma 3n E4B supports an unknown number of tokens and LongCat-Flash-Chat 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-Chat is developed by Meituan.