Baidu: ERNIE 4.5 VL 424B A47B

Baidu: ERNIE 4.5 VL 424B A47B

BaiduLLM

ERNIE-4.5-VL-424B-A47B is a multimodal Mixture-of-Experts (MoE) model from Baidu’s ERNIE 4.5 series, featuring 424B total parameters with 47B active per token. It is trained jointly on text and image data...

MultimodalOpen SourceAPI AvailableVisionReasoning

Specifications

Context Window

123K tokens

Input Price/1M

$0.42

Output Price/1M

$1.25

Parameters

Max Output

16K tokens

Information

Tool Calling
❌ Not supported
Vision
✅ Supported
Audio
❌ Not supported

Full Analysis: Baidu: ERNIE 4.5 VL 424B A47B

What is Baidu: ERNIE 4.5 VL 424B A47B ?

Baidu: ERNIE 4.5 VL 424B A47B is an AI model developed by Baidu, classified as a large language model (LLM). It is a multimodal model, capable of processing text, images, and potentially other media types. As an open source model, it is available for download, customization, and on-premises deployment. With a context window of 123K tokens, it is suitable for processing long documents such as contracts, books, and complete codebases.

Pricing & Costs in 2026

Baidu: ERNIE 4.5 VL 424B A47B is usage-based, priced at $0.42/1M input tokens and $1.25/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. At this aggressive price point, it is one of the most cost-effective options on the market, ideal for high-volume applications like chatbots, bulk document analysis, and automation.

Benchmarks & Performance

We don't have detailed benchmark results for Baidu: ERNIE 4.5 VL 424B A47B yet. Benchmarks are updated weekly as new data becomes available from sources like Artificial Analysis, LM Arena, and LiveBench.

It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.

Recommended Use Cases

Baidu: ERNIE 4.5 VL 424B A47B is suitable for a wide range of AI applications: long document analysis (contracts, legal proceedings, codebases), image and visual document analysis (OCR, diagrams, screenshots), multimodal processing combining text and images, high-volume chatbots and automated support, complex reasoning, math problem solving, and logical analysis, text generation, summarization, translation, and general assistance.

Comparison with Alternatives

In the 2026 AI model ecosystem, Baidu: ERNIE 4.5 VL 424B A47B competes directly with similarly capable models. Baidu competes in this segment against OpenAI, Anthropic, Google, and Meta. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.

For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.

Frequently Asked Questions

What is Baidu: ERNIE 4.5 VL 424B A47B ?

ERNIE-4.5-VL-424B-A47B is a multimodal Mixture-of-Experts (MoE) model from Baidu’s ERNIE 4.5 series, featuring 424B total parameters with 47B active per token. It is trained jointly on text and image data...

How much does Baidu: ERNIE 4.5 VL 424B A47B cost?

Baidu: ERNIE 4.5 VL 424B A47B costs $0.42/1M input tokens and $1.25/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.

How does Baidu: ERNIE 4.5 VL 424B A47B compare with other models?

We don't have detailed benchmarks for Baidu: ERNIE 4.5 VL 424B A47B yet. Check the main benchmark page to compare available models.

Is Baidu: ERNIE 4.5 VL 424B A47B open source?

Yes, Baidu: ERNIE 4.5 VL 424B A47B is an open source model. You can deploy it on-premises, customize it via fine-tuning, and maintain full control over your data. Check the official repository for the specific license.

What is Baidu: ERNIE 4.5 VL 424B A47B best for?

Baidu: ERNIE 4.5 VL 424B A47B excels at complex reasoning and problem solving. With its large context window, it handles long documents, codebases, and extended conversations.

Last updated: May 15, 2026 View methodology →