ERNIE-4.5-VL-424B-A47B

What is ERNIE-4.5-VL-424B-A47B?

ERNIE-4.5-VL-424B-A47B is Baidu’s largest open vision-language model in the ERNIE 4.5 family. A vision-language model can process language together with visual information, allowing it to answer questions about images, documents, charts, diagrams, and video. This model returns text rather than generating new images, video, or audio.

The name describes a very large mixture-of-experts, or MoE, model. It has 424 billion total parameters, but approximately 47 billion parameters are active for an individual input. In practical terms, the model retains a large total capacity while using only a subset of its parameters for each request. That architecture does not make deployment lightweight: the 424B model still requires substantial infrastructure and is intended mainly for serious research, evaluation, and self-hosted enterprise workloads.

Baidu provides the model as open weights and supporting deployment tools rather than documenting an official hosted per-token price for this exact checkpoint. It is available through Baidu’s ERNIE resources and related PaddlePaddle, FastDeploy, Hugging Face, and AI Studio artifacts.

Position in Baidu’s model lineup

Within the open ERNIE 4.5 family, ERNIE-4.5-VL-424B-A47B occupies the largest vision-language position described in the supplied materials. Its focus is multimodal understanding: the model is designed to interpret visual and textual information, reason over it, and produce a textual answer.

That positioning is important when evaluating it against other types of models. A smaller vision-language model may be easier and cheaper to serve, while a hosted multimodal API may remove the operational burden of managing GPUs. ERNIE-4.5-VL-424B-A47B is most differentiated by its open deployment path, very large MoE design, broad visual input support, and long context rather than by low latency or simple consumer hardware requirements.

Inputs, outputs, and context length

The published context length is 131,072 tokens. A token is a unit of text processed by a language model; the exact number of words or media content that fits depends on the input. A long context can be useful for large documents, collections of pages, extended visual analysis, or video workflows, but the usable limit also depends on how the serving stack represents images and video.

The supplied research does not verify a maximum generated output-token limit for the exact model. It also does not establish fixed image-resolution, frame-count, or media-size limits. Those details may vary by checkpoint, processor, and deployment implementation, so they should be checked in the documentation for the selected serving stack rather than assumed from the 131,072-token context figure.

Multimodal reasoning and practical use cases

ERNIE-4.5-VL-424B-A47B is intended for more than basic image captioning. Baidu’s reported evaluation coverage includes visual perception tasks such as CV-Bench and RealWorldQA, along with reasoning-oriented evaluations including MathVista, MMMU, and VisualPuzzle. These references describe the areas tested by the provider; they should not be interpreted as a guarantee of performance on every user workload.

The model supports both thinking and non-thinking modes. Thinking mode is intended for tasks where additional reasoning depth is useful, while non-thinking mode can reduce response overhead when a faster answer is preferable. The trade-off is workload-dependent: enabling deeper reasoning can increase latency and resource use, whereas disabling it may be more appropriate for straightforward extraction or classification.

• Document understanding: Analyze reports, forms, scanned pages, and other documents that combine text and layout.
• Chart and diagram interpretation: Answer questions about plotted data, visual relationships, diagrams, and structured page content.
• Video understanding: Examine video input and respond to questions about its content, subject to deployment-specific media limits.
• Visual question answering: Connect a user’s textual question with information contained in an image or document.
• Long-context analysis: Process large textual or multimodal inputs when the serving configuration can accommodate them.
• Research and evaluation: Run repeatable experiments using open weights instead of depending entirely on a proprietary hosted endpoint.

Deployment and hardware trade-offs

The model is available in multiple deployment-oriented forms. The supplied materials identify Hugging Face checkpoints, Paddle-format artifacts, AI Studio identifiers, and tooling based on PaddlePaddle, ERNIEKit, and FastDeploy. FastDeploy documentation describes an OpenAI-compatible API server, which can make an existing client integration easier, but an API-compatible serving layer is not the same thing as a Baidu-hosted commercial API.

Official deployment materials list BF16, WINT4, WINT8, and FP8 inference formats for the model family. Quantization can reduce memory requirements by representing weights with fewer bits, but it does not turn this 424B model into a lightweight local application. FastDeploy guidance describes substantial hardware requirements, including multi-GPU configurations. Production operators must account for tensor parallelism, quantization, context length, media limits, and the cost of keeping large model weights in memory.

Compared with a smaller open vision-language model, this model offers a much larger capacity ceiling but generally has a worse speed and infrastructure profile. Compared with a hosted multimodal service, it offers more control over deployment and data handling, but the operator becomes responsible for GPUs, scaling, upgrades, monitoring, and serving reliability.

Pricing and commercial availability

No official hosted input-token or output-token price for the exact ERNIE-4.5-VL-424B-A47B checkpoint was verified in the supplied research. Because the model is distributed as open weights, the direct usage cost is not represented by a standard published per-token rate in the available materials. The real cost of use depends on hardware acquisition or rental, quantization, utilization, power, storage, and the chosen inference framework.

The Apache 2.0 license permits research and commercial use subject to the license terms. Teams should still review the specific repository and checkpoint documentation before deployment, particularly when combining model weights with third-party software, datasets, or hosted infrastructure.

Capabilities that are not established

The research confirms text, image, and video input, text output, thinking and non-thinking modes, and open deployment tooling. It does not verify native image, video, audio, or music generation. It also does not establish a separate official JSON mode, guaranteed structured-output interface, native web search, or general-purpose tool and function calling for the exact model.

Coding may be possible as a consequence of text generation, but the supplied sources do not document a specialized coding capability or a coding-specific interface. Similarly, streaming is listed in the model data, but implementation details depend on the serving layer. Fine-tuning support is documented through ERNIEKit using SFT-LoRA configurations, while the scope and resource requirements of a particular fine-tuning project still need to be verified against the relevant documentation.

Limitations to consider

The primary limitation is operational scale. A model with 424 billion total parameters can be difficult to run economically, especially when long contexts, high-resolution images, or video are involved. Quantization helps, but it does not remove the need for careful capacity planning. Latency is also likely to be less suitable for highly interactive applications than a smaller model or a managed service; this is a practical deployment consideration rather than a provider-published latency guarantee.

The model’s exact knowledge cutoff is not documented in the reviewed materials. Maximum output length, media limits, web access, structured-output guarantees, and hosted pricing are likewise unverified. Applications that depend on any of these properties should test the selected checkpoint and server directly instead of relying on assumptions from the family name or context window.

When to choose this model

Choose ERNIE-4.5-VL-424B-A47B when the priority is high-capacity multimodal understanding and the organization can operate substantial GPU infrastructure. It is a particularly relevant candidate for document and chart analysis, complex visual reasoning, long-context research, video understanding, and experiments that benefit from open weights and control over deployment.

A smaller open model is likely more appropriate when latency, hardware cost, or simpler operations matter more than maximum model scale. A hosted multimodal API may be preferable when a team needs predictable service operations and a published commercial interface rather than managing multi-GPU inference. A specialized image, video, or audio generation model should be selected when the required output is media rather than text. ERNIE-4.5-VL-424B-A47B is best understood as a large, open, text-output vision-language model for demanding analysis—not as a low-cost general-purpose endpoint or a native media-generation system.