Model Comparison

ERNIE 4.5 vs Gemma 3n E2B Instructed LiteRT (Preview)

Gemma 3n E2B Instructed LiteRT (Preview) significantly outperforms across most benchmarks.

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Performance Benchmarks

Comparative analysis across standard metrics

9 benchmarks

ERNIE 4.5 outperforms in 1 benchmarks (GPQA), while Gemma 3n E2B Instructed LiteRT (Preview) is better at 8 benchmarks (ARC-C, ARC-E, DROP, HellaSwag, MMLU, MMLU-Pro, PIQA, Winogrande).

Gemma 3n E2B Instructed LiteRT (Preview) significantly outperforms across most benchmarks.

Thu Apr 16 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
Thu Apr 16 2026 • llm-stats.com
Baidu
ERNIE 4.5
Input tokens$0.40
Output tokens$4.00
Best providerNovita
Google
Gemma 3n E2B Instructed LiteRT (Preview)
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

19.1B diff

ERNIE 4.5 has 19.1B more parameters than Gemma 3n E2B Instructed LiteRT (Preview), making it 999.5% larger.

Baidu
ERNIE 4.5
21.0Bparameters
Google
Gemma 3n E2B Instructed LiteRT (Preview)
1.9Bparameters
21.0B
ERNIE 4.5
1.9B
Gemma 3n E2B Instructed LiteRT (Preview)

Context Window

Maximum input and output token capacity

Only ERNIE 4.5 specifies input context (128,000 tokens). Only ERNIE 4.5 specifies output context (65,536 tokens).

Baidu
ERNIE 4.5
Input128,000 tokens
Output65,536 tokens
Google
Gemma 3n E2B Instructed LiteRT (Preview)
Input- tokens
Output- tokens
Thu Apr 16 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3n E2B Instructed LiteRT (Preview) supports multimodal inputs, whereas ERNIE 4.5 does not.

Gemma 3n E2B Instructed LiteRT (Preview) can handle both text and other forms of data like images, making it suitable for multimodal applications.

ERNIE 4.5

Text
Images
Audio
Video

Gemma 3n E2B Instructed LiteRT (Preview)

Text
Images
Audio
Video

License

Usage and distribution terms

ERNIE 4.5 is licensed under a proprietary license, while Gemma 3n E2B Instructed LiteRT (Preview) uses Gemma.

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

ERNIE 4.5

Proprietary

Closed source

Gemma 3n E2B Instructed LiteRT (Preview)

Gemma

Open weights

Release Timeline

When each model was launched

ERNIE 4.5 was released on 2025-06-25, while Gemma 3n E2B Instructed LiteRT (Preview) was released on 2025-05-20.

ERNIE 4.5 is 1 month newer than Gemma 3n E2B Instructed LiteRT (Preview).

ERNIE 4.5

Jun 25, 2025

9 months ago

1mo newer
Gemma 3n E2B Instructed LiteRT (Preview)

May 20, 2025

11 months ago

Knowledge Cutoff

When training data ends

Gemma 3n E2B Instructed LiteRT (Preview) has a documented knowledge cutoff of 2024-06-01, while ERNIE 4.5's cutoff date is not specified.

We can confirm Gemma 3n E2B Instructed LiteRT (Preview)'s training data extends to 2024-06-01, but cannot make a direct comparison without ERNIE 4.5's cutoff date.

ERNIE 4.5

—

Gemma 3n E2B Instructed LiteRT (Preview)

Jun 2024

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Baidu
Larger context window (128,000 tokens)
Higher GPQA score (74.0% vs 24.8%)
Google
Supports multimodal inputs
Has open weights
Higher ARC-C score (51.7% vs 40.6%)
Higher ARC-E score (75.8% vs 60.7%)
Higher DROP score (53.9% vs 28.6%)
Higher HellaSwag score (72.2% vs 33.0%)
Higher MMLU score (60.1% vs 41.9%)
Higher MMLU-Pro score (40.5% vs 16.0%)
Higher PIQA score (78.9% vs 55.2%)
Higher Winogrande score (66.8% vs 51.3%)

Detailed Comparison

AI Model Comparison Table
Feature
Baidu
ERNIE 4.5
Google
Gemma 3n E2B Instructed LiteRT (Preview)

FAQ

Common questions about ERNIE 4.5 vs Gemma 3n E2B Instructed LiteRT (Preview)

Gemma 3n E2B Instructed LiteRT (Preview) significantly outperforms across most benchmarks. ERNIE 4.5 is made by Baidu and Gemma 3n E2B Instructed LiteRT (Preview) is made by Google. The best choice depends on your use case — compare their benchmark scores, pricing, and capabilities above.
ERNIE 4.5 scores GPQA: 74.0%, ARC-E: 60.7%, PIQA: 55.2%, Winogrande: 51.3%, CLUEWSC: 48.6%. Gemma 3n E2B Instructed LiteRT (Preview) scores PIQA: 78.9%, BoolQ: 76.4%, ARC-E: 75.8%, HellaSwag: 72.2%, Winogrande: 66.8%.
ERNIE 4.5 supports 128K tokens and Gemma 3n E2B Instructed LiteRT (Preview) supports an unknown number of tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include multimodal support (no vs yes), licensing (Proprietary vs Gemma). See the full comparison above for benchmark-by-benchmark results.
ERNIE 4.5 is developed by Baidu and Gemma 3n E2B Instructed LiteRT (Preview) is developed by Google.