Model Comparison

ERNIE 4.5 vs Llama 3.2 3B Instruct

Llama 3.2 3B Instruct significantly outperforms across most benchmarks. Llama 3.2 3B Instruct is 104.0x cheaper per token.

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Performance Benchmarks

Comparative analysis across standard metrics

6 benchmarks

ERNIE 4.5 outperforms in 1 benchmarks (GPQA), while Llama 3.2 3B Instruct is better at 5 benchmarks (ARC-C, GSM8k, HellaSwag, MATH, MMLU).

Llama 3.2 3B Instruct significantly outperforms across most benchmarks.

Thu Apr 16 2026 • llm-stats.com

Arena Performance

Human preference votes

Pricing Analysis

Price comparison per million tokens

Llama 3.2 3B Instruct costs less

For input processing, ERNIE 4.5 ($0.40/1M tokens) is 40.0x more expensive than Llama 3.2 3B Instruct ($0.01/1M tokens).

For output processing, ERNIE 4.5 ($4.00/1M tokens) is 200.0x more expensive than Llama 3.2 3B Instruct ($0.02/1M tokens).

In conclusion, ERNIE 4.5 is more expensive than Llama 3.2 3B Instruct.*

* Using a 3:1 ratio of input to output tokens

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
Meta
Llama 3.2 3B Instruct
Input tokens$0.01
Output tokens$0.02
Best providerDeepinfra
Notice missing or incorrect data?Start an Issue→

Model Size

Parameter count comparison

17.8B diff

ERNIE 4.5 has 17.8B more parameters than Llama 3.2 3B Instruct, making it 554.2% larger.

Baidu
ERNIE 4.5
21.0Bparameters
Meta
Llama 3.2 3B Instruct
3.2Bparameters
21.0B
ERNIE 4.5
3.2B
Llama 3.2 3B Instruct

Context Window

Maximum input and output token capacity

Both models have the same input context window of 128,000 tokens. Llama 3.2 3B Instruct can generate longer responses up to 128,000 tokens, while ERNIE 4.5 is limited to 65,536 tokens.

Baidu
ERNIE 4.5
Input128,000 tokens
Output65,536 tokens
Meta
Llama 3.2 3B Instruct
Input128,000 tokens
Output128,000 tokens
Thu Apr 16 2026 • llm-stats.com

License

Usage and distribution terms

ERNIE 4.5 is licensed under a proprietary license, while Llama 3.2 3B Instruct uses Llama 3.2 Community License.

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

ERNIE 4.5

Proprietary

Closed source

Llama 3.2 3B Instruct

Llama 3.2 Community License

Open weights

Release Timeline

When each model was launched

ERNIE 4.5 was released on 2025-06-25, while Llama 3.2 3B Instruct was released on 2024-09-25.

ERNIE 4.5 is 9 months newer than Llama 3.2 3B Instruct.

ERNIE 4.5

Jun 25, 2025

9 months ago

9mo newer
Llama 3.2 3B Instruct

Sep 25, 2024

1.6 years ago

Knowledge Cutoff

When training data ends

Neither model specifies a knowledge cutoff date.

Unable to compare the recency of their training data.

No cutoff dates available

Provider Availability

ERNIE 4.5 is available from Novita. Llama 3.2 3B Instruct is available from DeepInfra.

ERNIE 4.5

novita logo
Novita
Input Price:Input: $0.40/1MOutput Price:Output: $4.00/1M

Llama 3.2 3B Instruct

deepinfra logo
Deepinfra
Input Price:Input: $0.01/1MOutput Price:Output: $0.02/1M
* Prices shown are per million tokens

Outputs Comparison

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Key Takeaways

Baidu
Higher GPQA score (74.0% vs 32.8%)
Meta
Less expensive input tokens
Less expensive output tokens
Has open weights
Higher ARC-C score (78.6% vs 40.6%)
Higher GSM8k score (77.7% vs 25.2%)
Higher HellaSwag score (69.8% vs 33.0%)
Higher MATH score (48.0% vs 12.4%)
Higher MMLU score (63.4% vs 41.9%)

Detailed Comparison

AI Model Comparison Table
Feature
Baidu
ERNIE 4.5
Meta
Llama 3.2 3B Instruct

FAQ

Common questions about ERNIE 4.5 vs Llama 3.2 3B Instruct

Llama 3.2 3B Instruct significantly outperforms across most benchmarks. ERNIE 4.5 is made by Baidu and Llama 3.2 3B Instruct is made by Meta. 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%. Llama 3.2 3B Instruct scores NIH/Multi-needle: 84.7%, ARC-C: 78.6%, GSM8k: 77.7%, IFEval: 77.4%, HellaSwag: 69.8%.
Llama 3.2 3B Instruct is 40.0x cheaper for input tokens. ERNIE 4.5 costs $0.40/M input and $4.00/M output via novita. Llama 3.2 3B Instruct costs $0.01/M input and $0.02/M output via deepinfra.
ERNIE 4.5 supports 128K tokens and Llama 3.2 3B Instruct supports 128K tokens. A larger context window lets you process longer documents, conversations, or codebases in a single request.
Key differences include input pricing ($0.40 vs $0.01/M), licensing (Proprietary vs Llama 3.2 Community License). See the full comparison above for benchmark-by-benchmark results.
ERNIE 4.5 is developed by Baidu and Llama 3.2 3B Instruct is developed by Meta.