Model Comparison

ERNIE 4.5 vs Gemma 3n E2B

Gemma 3n E2B significantly outperforms across most benchmarks.

Want to compare interactively?Try the playground

Performance Benchmarks

Comparative analysis across standard metrics

6 benchmarks

ERNIE 4.5 outperforms in 0 benchmarks, while Gemma 3n E2B is better at 6 benchmarks (ARC-C, ARC-E, DROP, HellaSwag, PIQA, Winogrande).

Gemma 3n E2B significantly outperforms across most benchmarks.

Tue Apr 14 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
Tue Apr 14 2026 • llm-stats.com
Baidu
ERNIE 4.5
Input tokens$0.40
Output tokens$4.00
Best providerNovita
Google
Gemma 3n E2B
Input tokens$0.00
Output tokens$0.00
Best providerUnknown Organization
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

13.0B diff

ERNIE 4.5 has 13.0B more parameters than Gemma 3n E2B, making it 162.5% larger.

Baidu
ERNIE 4.5
21.0Bparameters
Google
Gemma 3n E2B
8.0Bparameters
21.0B
ERNIE 4.5
8.0B
Gemma 3n E2B

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
Input- tokens
Output- tokens
Tue Apr 14 2026 • llm-stats.com

Input Capabilities

Supported data types and modalities

Gemma 3n E2B supports multimodal inputs, whereas ERNIE 4.5 does not.

Gemma 3n E2B 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

Text
Images
Audio
Video

License

Usage and distribution terms

Both models are licensed under proprietary licenses.

Both models have usage restrictions defined by their respective organizations.

ERNIE 4.5

Proprietary

Closed source

Gemma 3n E2B

Proprietary

Closed source

Release Timeline

When each model was launched

ERNIE 4.5 was released on 2025-06-25, while Gemma 3n E2B was released on 2025-06-26.

Gemma 3n E2B is 0 month newer than ERNIE 4.5.

ERNIE 4.5

Jun 25, 2025

9 months ago

Gemma 3n E2B

Jun 26, 2025

9 months ago

1d newer

Knowledge Cutoff

When training data ends

Gemma 3n E2B 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'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

Jun 2024

Outputs Comparison

Notice missing or incorrect data?Start an Issue discussion→

Key Takeaways

Baidu
Larger context window (128,000 tokens)
Google
Supports multimodal inputs
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 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

FAQ

Common questions about ERNIE 4.5 vs Gemma 3n E2B

Gemma 3n E2B significantly outperforms across most benchmarks. ERNIE 4.5 is made by Baidu and Gemma 3n E2B 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 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 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). See the full comparison above for benchmark-by-benchmark results.
ERNIE 4.5 is developed by Baidu and Gemma 3n E2B is developed by Google.