What is Amazon Nova Pro?
Amazon Nova Pro is a multimodal foundation model provided by Amazon and accessed primarily through Amazon Bedrock. Within the Amazon Nova family, it is positioned as a balanced option for applications that need a combination of accuracy, response speed, cost control, and visual understanding rather than a model focused exclusively on one narrow capability.
The model’s canonical Bedrock identifier is amazon.nova-pro-v1:0. It was released on December 5, 2024, and the supplied model information lists it as active. Amazon describes Nova Pro as suitable for enterprise workloads involving text, images, and video, including assistants, document-processing systems, retrieval-augmented generation, and agentic applications.
“Multimodal” refers to the model’s ability to process more than text. Nova Pro can analyze images and video alongside written prompts, but its native response format is text. It is therefore an understanding and generation model, not an image, video, audio, or speech-generation model.
Input, output, and technical limits
Nova Pro accepts text, images, and video as input and returns text. This makes it useful when an application needs an explanation, extraction, classification, summary, or answer based on visual material. For example, a workflow could provide a written question and a document image, or ask the model to summarize information contained in a supported video.
| Specification | Amazon Nova Pro |
|---|---|
| Input modalities | Text, images, and video |
| Output modality | Text |
| Context window | 300,000 tokens |
| Maximum output | 5,000 tokens |
| Model ID | amazon.nova-pro-v1:0 |
| Knowledge cutoff | October 2024 |
The 300,000-token context window is substantially larger than the maximum response length. In practical terms, the model can consider a large collection of source material in one request, but it cannot produce an equally long answer in a single response. This is useful for summarizing long documents, comparing multiple sources, or extracting specific information from a large context while keeping the requested answer focused.
The October 2024 knowledge cutoff applies to the underlying model. Supplying newer documents or other external context can help an application work with current information, but it does not change the model’s original training cutoff. Nova Pro also does not provide a separately documented native web-search capability as part of the base model; current-information workflows need to supply external information through an application or connected tool.
What Nova Pro can do
Visual and document understanding
Nova Pro’s main distinction is its ability to reason over text, images, and video in the same application. It can support document analysis, visual question answering, content analysis, and video understanding. These capabilities are relevant for workflows such as asking questions about an image, extracting or summarizing information from visual documents, reviewing media, or combining written instructions with visual evidence.
The supplied research identifies video understanding as a supported capability through Amazon Bedrock invocation and batch-inference workflows. The model should not be confused with a video-generation system: it analyzes video and responds with text rather than creating a new video.
Long-context processing
With a 300,000-token context window, Nova Pro is designed for tasks where the input is too large for a short-context model. Possible applications include long-document summarization, enterprise knowledge analysis, comparison of several source files, and retrieval-augmented generation where relevant passages are provided with the user’s question.
A large context window does not guarantee that every detail will be equally useful or that the model will never make mistakes. It gives the application more room to provide relevant material, but prompts should still identify the task clearly and distinguish authoritative source content from instructions or untrusted text.
Tool use and enterprise workflows
Nova Pro supports function and tool calling. Tool calling allows the model to request an action from an application, such as retrieving information from a business system or invoking a defined operation. The external application remains responsible for deciding whether to execute that request, validating arguments, enforcing permissions, and handling sensitive actions.
Amazon Bedrock also supports response streaming, batch inference, prompt caching, model customization, provisioned throughput, agents, guardrails, and prompt management for Nova Pro. These are deployment and workflow options around the model rather than additional native output modalities. They can help organizations build repeatable applications, reduce repeated-input processing, or manage larger-scale workloads.
Pricing and access through Amazon Bedrock
Standard on-demand pricing supplied for Nova Pro is $0.80 per 1 million input tokens and $3.20 per 1 million output tokens. These are token-processing prices rather than a recurring consumer subscription. The total cost of an application depends on how much text or visual material it submits, how long responses are, how often requests are made, and which Bedrock service option is selected.
AWS also lists priority, flex, latency-oriented, cross-Region, and cached-input options whose pricing or availability may differ from standard on-demand inference. Prompt caching can change the effective cost when the same large input context is reused, while cross-Region inference can affect operational behavior and regional routing. Organizations should check the current Amazon Bedrock pricing documentation before estimating production expenses.
Nova Pro is available in multiple AWS Regions through Amazon Bedrock, using the in-Region model ID or supported geographic inference profiles. Availability, customization options, and service characteristics can vary by Region.
Customization and deployment options
The supplied AWS documentation identifies fine-tuning support for Nova Pro’s 300,000-token contextual variant in the US East (N. Virginia) Region. Fine-tuning can be relevant when a general-purpose model needs to behave more consistently for a specialized domain or task, although it introduces additional data, evaluation, governance, and operational requirements.
Nova Pro can also be used with provisioned throughput, model distillation workflows, agents, guardrails, and prompt management in Amazon Bedrock. These options address different needs: predictable capacity, deployment efficiency, controlled tool-based behavior, or centralized prompt and safety management. They do not remove the need to evaluate outputs for accuracy, security, and suitability for the intended business process.
Reasoning, coding, speed, and cost trade-offs
Nova Pro is intended as a balanced model rather than a specialist system built solely for deep reasoning or software development. The supplied editorial data assigns it a reasoning score of 7, a coding score of 7, a speed score of 8, and a cost score of 8. These are comparative editorial estimates, not scores published by Amazon and not standardized benchmark results.
In practical terms, Nova Pro’s balance makes it a candidate for applications that need useful reasoning and coding assistance alongside visual inputs, long context, and relatively efficient inference. Its 5,000-token output limit may be adequate for answers, summaries, structured extractions, and tool decisions, but it can be restrictive for tasks that require very long generated documents or extensive code in one response.
Its cost advantage depends on the workload. Large image, video, or document inputs can consume substantial context even when the final answer is short. Prompt caching may be useful when an application repeatedly supplies the same long instructions or reference material. For latency-sensitive workloads, AWS’s available service tiers may offer different trade-offs between responsiveness, cost, and capacity.
Best use cases for Amazon Nova Pro
- Document analysis: summarize, compare, classify, or answer questions about large collections of text and visual documents.
- Visual question answering: respond to questions about images while considering written instructions or surrounding context.
- Video understanding: analyze supported video inputs and return textual descriptions, answers, or summaries.
- Enterprise assistants: combine long-context reasoning with retrieval, tool calls, guardrails, and business-system integration.
- Retrieval-augmented generation: provide relevant enterprise content to the model and ask for grounded answers or synthesis.
- Agentic workflows: let the model select from approved functions while the application controls execution and permissions.
- Content and media review: inspect text and visual material at scale and produce human-readable findings.
Limitations and when another option may be better
Nova Pro’s most important limitation is that it produces text only. It cannot natively generate images, audio, video, speech, music, or embeddings. An application needing those outputs must use another model or a separate service in addition to Nova Pro.
The model also has no separately documented built-in web-search capability. If an application needs live web information, it must connect an approved search or retrieval tool and provide the results to the model. The October 2024 knowledge cutoff is another reason to use external sources for changing information.
Another option may be more appropriate when the primary requirement is specialized image or video generation, speech interaction, embeddings, extremely long generated output, or a different balance between latency and reasoning depth. A smaller or more speed-focused model may be preferable for simple classification or high-volume short requests, while a specialist reasoning or coding model may be preferable when complex multi-step analysis or code generation is more important than multimodal input and broad enterprise integration.
Nova Pro is a sensible choice when one model needs to connect text generation with image and video understanding, long context, and Bedrock workflow features. It is less suitable when the application’s defining requirement is a non-text output, live search built into the base model, or highly specialized generation.
Overall assessment
Amazon Nova Pro occupies a practical middle position in Amazon’s model catalog. Its combination of text, image, and video input; 300,000-token context; tool calling; customization; and Bedrock deployment options makes it suitable for enterprise applications that need more than text-only generation. The model’s trade-off is equally clear: it returns text only, has a 5,000-token output ceiling, and requires external tools for current web information and non-text generation.
For teams already using Amazon Bedrock, Nova Pro is best evaluated as a balanced multimodal foundation for document, media, retrieval, and tool-using workflows. Its advertised token prices and broad deployment features can make it attractive for production use, but the right choice still depends on input volume, latency targets, required output modality, and the level of reasoning or specialization demanded by the application.