ERNIE 5.5 Flash Preview
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ERNIE 5.5 Flash Preview

ernie-5.5-flash-previewllms.txt
Baidu
New
ERNIE 5.5 Flash Preview is the latest foundational model in Baidu's Wenxin (ERNIE) series, comprehensively optimized for programming, agents, knowledge work, and deep research. It has 280 billion total parameters, activates 14 billion parameters per token, and supports a 1M-token context window.

Pricing

TierPricingCache Read
Off-peak · 14:00–00:00 UTC
$0.147$0.588
$0.0147/M tokens
Peak · 00:00–14:00 UTC
$0.2942$1.1768
$0.0294/M tokens

Input Modalities

    Output Modalities

    • Text

    Providers

    Baidu ernie-5.5-flash-preview
    Off-peak14:00–00:00 UTC
    Pricing$0.147$0.588
    Cache Read$0.0147/M tokens
    Peak00:00–14:00 UTC
    Pricing$0.2942$1.1768
    Cache Read$0.0294/M tokens
    PrecisionⓘNot Disclosed
    Latency-
    Throughput-
    Uptime
    0.00% uptime 2 days ago
    0.00% uptime yesterday
    100.00% uptime today

    Performance for ernie-5.5-flash-preview

    Uptime is the percentage of requests that succeeded over the past 72 hours. AIHubMix continuously monitors every provider and automatically retries with the next-best provider when one returns an error or responds too slowly; Latency is total round-trip time (lower is better); Throughput is how fast the model writes (tokens per second, higher is better).

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    Try this model

    Python
    import os
    from openai import OpenAI
    
    client = OpenAI(
        api_key=os.environ["AIHUBMIX_API_KEY"],
        base_url="https://aihubmix.com/v1",
    )
    
    response = client.chat.completions.create(
        model="ernie-5.5-flash-preview",
        messages=[
          {
            "role": "user",
            "content": "Hello, how are you?"
          }
        ],
        max_tokens=1024,
        stream=False,
    )
    
    print(response.choices[0].message.content)

    Frequently asked questions

    What is ERNIE 5.5 Flash Preview?

    ERNIE 5.5 Flash Preview is the latest foundational model in Baidu's Wenxin (ERNIE) series, comprehensively optimized for programming, agents, knowledge work, and deep research. It has 280 billion total parameters, activates 14 billion parameters per token, and supports a 1M-token context window.