ERNIE 4.5 VL 424B A47B API

baidu/ernie-4.5-vl-424b-a47b
ERNIE 4.5 VL empowers developers and businesses to build intelligent systems that seamlessly integrate visual and textual information.
Context
123K tokens
Input
$0.429 / 1M tokens
Output
$1.29 / 1M tokens
Released
Jan 12, 2026

Overview

What is ERNIE 4.5 VL API?

ERNIE 4.5 VL is part of Baidu’s ERNIE 4.5 family, a suite of multimodal models capable of interpreting complex data across multiple modalities. The VL variant specifically merges vision and language processing into a unified framework, allowing the model to analyze images, comprehend text, and generate detailed responses that connect both modalities. This model is suitable for applications ranging from interactive visual Q&A and content generation to document interpretation and visual reasoning. As an open-source solution under the Apache 2.0 license, it offers broad flexibility for research, development, and commercial applications.

Why ERNIE 4.5 VL Stands Out

ERNIE 4.5 VL combines cutting-edge multimodal reasoning with practical deployment flexibility. It offers open-source accessibility under Apache 2.0, a scalable architecture that ranges from efficient 28B models to high-capacity 424B models, and the ability to handle extremely long contexts for complex tasks. Its ecosystem includes tools and frameworks that accelerate development and integration, making it one of the most versatile solutions for vision-language AI available today.

ERNIE 4.5 VL 424B A47B
  • Total parameters: ~424B
  • Activated parameters per token: ~47B
  • Architecture: MoE with modality‑isolated routing and high‑capacity expert layers
  • Context length: 123K
  • Tailored for high‑precision multimodal reasoning at scale.

Pricing

Input: $0.5577 per 1M tokens

Output: $1.677 per 1M tokens

Key Features

Multimodal understanding

  • Joint processing of text prompts and images for grounded Q&A, captioning, retrieval-augmented reasoning and visual explanation.​
  • Robust document and chart analysis: extraction of structure, trends, and insights from PDFs, reports, infographics and UI screenshots.​

Advanced reasoning

  • Thinking mode with explicit multi-step reasoning over visual content, including zooming into regions, cross-referencing elements and performing symbolic reasoning (e.g., math over charts).​
  • Strong performance on STEM-style tasks that integrate equations, diagrams and textual descriptions.

Use Cases

Multimodal Q&A and assistants

  • Knowledge assistants answering questions based on uploaded images, diagrams, dashboards, or scanned documents.
  • Technical support bots that read UI screenshots, error dialogs, schematics or machine photos to guide troubleshooting and repair workflows.

Document and data analysis

  • Automated analysis of PDF reports, financial statements, contracts and legal documents with embedded tables and figures.​
  • Insight extraction from business dashboards and charts: summarizing trends, highlighting anomalies, and generating executive briefs grounded in the visual data.​

E‑commerce and marketing

  • Product understanding from photos plus descriptions to generate detailed, attribute-rich listings and comparisons.​
  • Visual A/B testing analysis: understanding ad creatives, infographics or landing page screenshots and relating them to performance metrics described in text.

Education, research and STEM

  • Step-by-step reasoning over textbook figures, lab experiment photos, plots and math diagrams.​
  • Interactive tutoring agents that combine text explanations with visual references and annotations over images.

Comparison with Other Models

vs Qwen2.5 VL

  • Performance focus: ERNIE 4.5 VL 28B A3B matches or exceeds Qwen2.5 VL 7B and Qwen2.5 VL‑32B on many multimodal benchmarks while using fewer activated parameters, making it a strong choice where parameter efficiency matters.​
  • Reasoning mode: ERNIE’s explicit thinking mode and heterogeneous MoE routing emphasize structured visual reasoning, whereas Qwen2.5‑VL lines are more conventional dense or smaller MoE designs without the same “thinking with images” paradigm.

Uptime and performance

  • Uptime (7d): 50.0%

Measured on the AI/ML API gateway across live traffic. Figures are medians unless stated. Last updated 2026-10-12.

Use cases

Image, document, and chart understanding are documented use cases for ERNIE 4.5 VL 424B A47B, including visual reasoning and visual question answering.

It is also documented for cross-modal reasoning, with the model trained on text and image data to handle tasks that combine textual and visual information.

The model is documented as suitable for reasoning-centered multimodal tasks, with strong performance on benchmarks such as MathVista, MMMU, and VisualPuzzle.

It is also documented for perception-focused visual tasks, with good results on datasets such as CV-Bench and RealWorldQA.

How to use ERNIE 4.5 VL 424B A47B API

Install any OpenAI-compatible SDK, point it at api.aimlapi.com/v1, and set the model to baidu/ernie-4.5-vl-424b-a47b.
import requests

r = requests.post(
    "https://api.aimlapi.com/v1/chat/completions",
    headers={"Authorization": "Bearer " + AIMLAPI_KEY},
    json={
      "model": "baidu/ernie-4.5-vl-424b-a47b",
      "messages": [
        {
          "role": "user",
          "content": "Hello!"
        }
      ]
    },
)
print(r.json())
const r = await fetch("https://api.aimlapi.com/v1/chat/completions", {
  method: "POST",
  headers: {
    Authorization: `Bearer ${process.env.AIMLAPI_KEY}`,
    "Content-Type": "application/json",
  },
  body: JSON.stringify({
    "model": "baidu/ernie-4.5-vl-424b-a47b",
    "messages": [
      {
        "role": "user",
        "content": "Hello!"
      }
    ]
  }),
});
console.log(await r.json());
curl -X POST https://api.aimlapi.com/v1/chat/completions \
  -H "Authorization: Bearer $AIMLAPI_KEY" \
  -H "Content-Type: application/json" \
  -d '{"model":"baidu/ernie-4.5-vl-424b-a47b","messages":[{"role":"user","content":"Hello!"}]}'

OpenAI-compatible — swap the base URL and it works with your existing SDK.

ERNIE 4.5 VL 424B A47B API Pricing

TypePrice
Input
$0.429 / 1M tokens
Output
$1.29 / 1M tokens

ERNIE 4.5 VL 424B A47B vs other models

ModelInputOutputContextBest for
$0.429 / 1M tokens
$1.29 / 1M tokens
123K tokens
Coding + agents
$5 / 1M tokens
$30 / 1M tokens
1M tokens
Reasoning + agents
$2 / 1M tokens
$10 / 1M tokens
1M tokens
Balanced coding + agents
$3 / 1M tokens
$15 / 1M tokens
1M tokens
Long-context, multimodal & agentic workflows
$1.5 / 1M tokens
$9 / 1M tokens
1M tokens
Reasoning + agents

Frequently asked questions

ERNIE 4.5 VL 424B A47B has a 123,000 tokens context window and can return up to 123,000 tokens.

ERNIE 4.5 VL 424B A47B takes image, text as input and returns text.

Use baidu/ernie-4.5-vl-424b-a47b as the model id. Requests go to https://api.aimlapi.com/v1/chat/completions.

ERNIE 4.5 VL 424B A47B became available on January 12, 2026.

ERNIE 4.5 VL 424B A47B is priced at input $0.5577 / 1M tokens, output $1.677 / 1M tokens.

Yes, ERNIE 4.5 VL 424B A47B can stream responses as they are generated.

Yes, ERNIE 4.5 VL 424B A47B accepts image input alongside text.

ERNIE 4.5 VL 424B A47B was built by Baidu.

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