ERNIE 4.5 Turbo VL

What is ERNIE 4.5 Turbo VL?

ERNIE 4.5 Turbo VL is a multimodal model from Baidu. The “VL” designation refers to vision-language capability: the model can work with language together with visual information. Baidu's current Qianfan documentation identifies ernie-4.5-turbo-vl as the canonical model endpoint and describes support for text, image, and video inputs.

In practical terms, the model is intended to interpret content rather than create media. It can examine an image, analyze a video, read text from a document or screenshot, and answer in text. Its primary uses include visual question answering, OCR, document understanding, translation, and multimodal analysis. It can also assist with code-related tasks when code appears in text, documents, screenshots, or other supported inputs.

The model was released on April 24, 2025, and the supplied research lists it as current and available. Earlier or alternate identifiers include ernie-4.5-turbo-vl-32k, ernie-4.5-turbo-vl-preview, and ERNIE-4.5-Turbo-VL-Latest. These should generally be treated as deployment or lifecycle identifiers rather than separate model entities.

Where it fits in Baidu's catalog

ERNIE 4.5 Turbo VL sits within Baidu's ERNIE 4.5 Turbo family and is exposed through Baidu's developer-oriented Qianfan services. Its role is narrower and more practical than a general-purpose media platform: it is built to interpret multimodal input and produce a textual answer.

This positioning matters when comparing it with Baidu's broader consumer AI offerings. Baidu's consumer service, 文心, advertises features such as image and video creation, voice interaction, and task automation. Those product capabilities should not be attributed to ERNIE 4.5 Turbo VL itself. The model's verified output type is text, while its multimodal capability concerns the material it can receive and understand.

Supported inputs and outputs

Examples of suitable prompts include asking the model to extract text from a photographed form, summarize a long document, identify information in a chart, translate text visible in an image, explain what happens in a video, or answer questions about a screenshot. Because the output is text, a separate generation system is needed when the application must create an image, video, voice recording, or other media.

Context window and maximum output

The structured specification lists a 128,000-token context window. A context window is the amount of input and generated text that can be handled within a request or conversation. For a multimodal model, the practical capacity also depends on how images and video are represented and processed by the service, so the raw token figure should not be interpreted as a promise that every combination of very large files will behave identically.

The current product overview lists a maximum output capacity of 16,000 tokens. Some Baidu API documentation reports an output range of up to 12,288 tokens instead. The supplied research preserves the 16K value as the current structured product-overview figure while noting the documentation discrepancy. Developers should confirm the limit exposed by the specific Qianfan service or deployment they use rather than assuming that every interface has the same ceiling.

Pricing and cost position

The listed Qianfan prices are:

• Input: ¥0.003 per 1,000 tokens
• Output: ¥0.009 per 1,000 tokens

These are token-based prices for the 128K Qianfan model endpoint. Input and output are priced separately, and generated tokens cost more than input tokens at the listed rates. Actual charges may vary according to service mode, promotions, billing arrangements, or changes to Baidu's pricing. Image and video processing may also have interface-specific accounting details that should be checked in the active Qianfan documentation.

On the supplied comparative evaluation, ERNIE 4.5 Turbo VL receives an editorial cost score of 8 and speed score of 8, both on an internal comparative scale rather than a Baidu-published benchmark. Those scores reflect the model's apparent positioning as a relatively fast and economical option, not a guaranteed latency or quality level for every workload.

Reasoning, coding, and tool support

The model has an editorial reasoning score of 7 out of 10 and an editorial coding score of 7 out of 10. These are comparative estimates, not vendor benchmark results. They suggest that the model may be suitable for structured analysis, interpreting evidence across text and visuals, explaining code, and handling ordinary code-related tasks, while not necessarily being the best choice for the most demanding reasoning or software-engineering workloads.

Tool use is listed as supported, and the model supports streaming responses. Streaming allows an application to display generated text as it arrives instead of waiting for the complete response. Batch API support is also listed. The supplied research does not verify a distinct JSON mode, structured-output guarantee, caching feature, or fine-tuning availability, so applications that require strict machine-readable output should verify those features in the exact Qianfan interface before depending on them.

Main strengths and limitations

Strengths

• Broad visual input: It can process text, images, and video in one model workflow.
• Useful document capabilities: OCR, document comprehension, summarization, and translation are central use cases.
• Large context: The listed 128K context window is useful for long documents and multimodal tasks involving substantial supporting material.
• Cost and speed positioning: The listed token rates and editorial scores make it appealing for high-volume analysis where a premium frontier model would be unnecessarily expensive.
• Developer features: Tool use, streaming, and batch inference support can help integrate the model into automated processing pipelines.

Limitations

• Text-only output: It does not provide verified native image, video, audio, music, or speech generation.
• Unclear knowledge cutoff: No authoritative knowledge-cutoff date was verified for this exact model. External search or retrieval, if available through a platform, should not be confused with the model's underlying knowledge.
• Documentation variation: Baidu's documentation reports different maximum-output figures in different places, so the deployed interface should be checked.
• Unverified structured-output details: JSON mode, caching, and fine-tuning are not confirmed in the supplied research.
• Not necessarily frontier reasoning: The editorial reasoning and coding scores indicate useful general capability but do not establish top performance on difficult mathematical, research, or large-scale software tasks.

When to choose ERNIE 4.5 Turbo VL

Choose this model when the central problem is understanding visual or video content at a controlled token cost. It is a good candidate for document intake, invoice or form reading, screenshot interpretation, image-based customer support, video summarization, multilingual visual content, OCR pipelines, and applications that need text answers grounded in visual evidence.

Its combination of 128K context and multimodal input is especially useful when a request combines a long written brief with images or video. Streaming and batch support may also make it suitable for interactive tools and larger asynchronous processing jobs.

Another model or service may be more appropriate when the application must generate media rather than analyze it. A dedicated image, video, speech, or audio-generation system is needed for those outputs. A model with a verified stronger reasoning or coding track record may be preferable for difficult autonomous programming, advanced mathematical reasoning, or tasks where maximum accuracy matters more than price and speed. Similarly, applications requiring a documented knowledge cutoff, strict JSON guarantees, or confirmed fine-tuning support should select an option whose documentation explicitly provides those features.

Bottom line

ERNIE 4.5 Turbo VL is best understood as a cost-conscious multimodal analysis model in Baidu's Qianfan lineup. It accepts text, images, and video, offers a listed 128K context window, and returns text suited to OCR, document work, visual question answering, translation, and related analysis. Its main trade-off is clear: it prioritizes practical multimodal understanding, speed, and price over native media generation and verified frontier-level reasoning. Before production deployment, developers should confirm the endpoint's current output limit, pricing rules, and any structured-output behavior in the active Baidu documentation.