Baidu: ERNIE 4.5 300B A47B✓ Catalog verified
ERNIE-4.5-300B-A47B is a 300B parameter Mixture-of-Experts (MoE) language model developed by Baidu as part of the ERNIE 4.5 series. It activates 47B parameters per token and supports text generation in both English and Chinese. Optimized for high-throughput inference and efficient scaling, it uses a heterogeneous MoE structure with advanced routing and quantization strategies, including FP8 and 2-bit formats. This version is fine-tuned for language-only tasks and supports reasoning, tool parameters, and extended context lengths up to 131k tokens. Suitable for general-purpose LLM applications with high reasoning and throughput demands.
Overview
ERNIE-4.5-300B-A47B is a 300B parameter Mixture-of-Experts (MoE) language model developed by Baidu as part of the ERNIE 4.5 series. It activates 47B parameters per token and supports text generation in both English and Chinese. Optimized for high-throughput inference and efficient scaling, it uses a heterogeneous MoE structure with advanced routing and quantization strategies, including FP8 and 2-bit formats. This version is fine-tuned for language-only tasks and supports reasoning, tool parameters, and extended context lengths up to 131k tokens. Suitable for general-purpose LLM applications with high reasoning and throughput demands.
Access: available through the official Baidu API. Context window: 123K.
Specifications
| API identifier | baidu/ernie-4.5-300b-a47b |
| Provider | Baidu |
| Model type | Text LLM |
| Context window | 123K catalog |
| Input modalities | text |
| Output modalities | text |
| Released | Jun 30, 2025 |
| Moderated | No |
| Architecture modality | text->text |
Pricing
Live pricing components for baidu/ernie-4.5-300b-a47b as published in the catalog.
| Pricing component | Raw unit price | Normalized | Unit |
|---|---|---|---|
| Prompt tokens | $2.8e-7 | $0.28 / 1M | Per input token |
| Completion tokens | $0.0000011 | $1.10 / 1M | Per output token |
| Request fee | N/A | N/A | Per request |
| Image fee | N/A | N/A | Per image unit |
| Web search fee | N/A | N/A | Per search request |
Cost calculator
Estimate monthly spend from your own token volumes.
Estimated Total Cost
$0.36Calculated from current list pricing in the ModelsAtlas catalog.Capabilities
| Capability | Status | What it means |
|---|---|---|
| Visual Understanding | Not advertised | Image and document analysis support |
| Audio Processing | Not advertised | Speech and voice aligned flows |
| Tool Calling | Not advertised | Supports tools / function calling |
| Self-Hosting | Not advertised | Deploy outside managed APIs |
Capability signals
Directional signals derived from model metadata and capability tags — not official benchmark submissions.
Quick start
Call Baidu: ERNIE 4.5 300B A47B through an OpenAI-compatible client.
from openai import OpenAI
client = OpenAI(
base_url="https://openrouter.ai/api/v1",
api_key="YOUR_API_KEY"
)
response = client.chat.completions.create(
model="baidu/ernie-4.5-300b-a47b",
messages=[{"role": "user", "content": "Explain quantum physics."}]
)
print(response.choices[0].message.content)Alternatives
Closest models by context window from a different provider.
| Model | Provider | Context | Input /1M | Actions |
|---|---|---|---|---|
| Perplexity: Sonar | Perplexity | 127K | $1.00 | View → |
| AllenAI: Olmo 2 32B Instruct | Allenai | 128K | $0.05 | View → |
Sources & attribution
Pricing and metadata are maintained in the ModelsAtlas catalog.
Frequently asked questions
How much does Baidu: ERNIE 4.5 300B A47B cost?
$0.28 per 1M input tokens and $1.10 per 1M output tokens on the official Baidu API.
What is the context window of Baidu: ERNIE 4.5 300B A47B?
Baidu: ERNIE 4.5 300B A47B supports up to 123K tokens of context.
Does Baidu: ERNIE 4.5 300B A47B support tool / function calling?
Baidu: ERNIE 4.5 300B A47B does not advertise first-class tool/function calling. Check vendor docs for the latest capabilities.
How do I access Baidu: ERNIE 4.5 300B A47B?
Use the official Baidu API with the model id `baidu/ernie-4.5-300b-a47b`. See the Quick start section above for code examples.