Model Cost Profile

Baidu: ERNIE 4.5 21B A3B Thinking

Developer: baidu

Pricing updated Mar 11, 2026

Input rank: #66Output rank: #80

Live Pricing

Input: $0.0700

Output: $0.2800

Pricing via OpenRouter API ยท Last synced Mar 11, 2026

Baidu's ERNIE 4.5 21B A3B Thinking model features an extensive context window of 131,072 tokens, making it suitable for complex applications such as long-form content generation, detailed report analysis, and comprehensive conversational agents. With an input price of $0.07 per 1 million tokens and an output price of $0.28 per 1 million tokens, teams can effectively manage costs while leveraging the model for high-volume tasks. This pricing structure allows organizations to scale their usage according to project needs, optimizing budget allocations for AI-driven solutions.

๐Ÿง  Reasoning

Context Window

131,072

Tokens

Input Price / 1M

$0.0700

Prompt tokens

Output Price / 1M

$0.2800

Completion tokens

Intelligence (MMLU)

Benchmark Pending

Massive Multitask Language Understanding

Price History

Baidu: ERNIE 4.5 21B A3B Thinking Pricing Trend

Input / 1M tokens0.0%Output / 1M tokens0.0%
Mar 7 โ€” Mar 11
$0.0700$0.1750$0.2800Mar 7Mar 8Mar 9Mar 10Mar 11

Current Input / 1M

$0.0700

Current Output / 1M

$0.2800

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FAQ

Common pricing and benchmark questions for Baidu: ERNIE 4.5 21B A3B Thinking.

How much does Baidu: ERNIE 4.5 21B A3B Thinking cost per 1M input tokens?

Baidu: ERNIE 4.5 21B A3B Thinking input pricing is $0.0700 per 1M tokens based on the latest synced provider data.

How much does Baidu: ERNIE 4.5 21B A3B Thinking cost per 1M output tokens?

Baidu: ERNIE 4.5 21B A3B Thinking output pricing is $0.2800 per 1M tokens based on the latest synced provider data.

What context window does Baidu: ERNIE 4.5 21B A3B Thinking support?

Baidu: ERNIE 4.5 21B A3B Thinking supports a context window of 131,072 tokens.

How can I compare Baidu: ERNIE 4.5 21B A3B Thinking with cheaper alternatives?

Use the comparison links on this page to open direct model-vs-model pricing and benchmark pages, then evaluate monthly spend projections for your workload.