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openai-search-preview

GPT

Access the most up to date information on the web using openai search preview; get verifiable, cited, and instant answers powered by an AI search engine.

Avg Run Time: 8.000s

Model Slug: openai-search-preview

Playground

Input

Advanced Controls

Output

Example Result

Preview and download your result.

{
"output":""As of June 23, 2025, a notable positive news story is the release of the first images from the Vera C. Rubin Observatory. This observatory, located in Chile, has begun its mission to conduct a 10-year survey of the southern sky, aiming to enhance our understanding of dark matter, dark energy, and the formation of galaxies. ([en.wikipedia.org](https://en.wikipedia.org/wiki/2025_in_science?utm_source=openai)) In other recent positive developments, conservation efforts have led to significant environmental successes. For instance, on Anna Maria Island in Florida, a record 546 sea turtle nests were recorded, breaking a 42-year-old record. Additionally, the least tern, a threatened bird species, returned to the island for the first time in 15 years, highlighting the positive impact of ongoing coastal preservation initiatives. ([homeplanet.grove.co](https://homeplanet.grove.co/blog-posts/positive-environmental-news-stories-that-give-us-hope-in-2025?utm_source=openai)) Furthermore, in London, approximately 62 kilometers of rivers have been restored since 2000 through initiatives such as wetland creation and reedbed installation. These efforts aim to inspire the public to protect and celebrate the capital's waterways. ([conservationoptimism.org](https://conservationoptimism.org/7-stories-of-optimism-this-week-26-05-25-02-06-25/?utm_source=openai)) These stories underscore the ongoing advancements in both scientific research and environmental conservation.""
}
Cost is calculated based on input and output tokens. 1 input token costs $0.000003, 1 output token costs $0.000010. For 250 input tokens and 780 output tokens, total cost will be $0.008425. For $1 you can run this model approximately 118 times.

API & SDK

Create a Prediction

Send a POST request to create a new prediction. This will return a prediction ID that you'll use to check the result. The request should include your model inputs and API key.

Get Prediction Result

Poll the prediction endpoint with the prediction ID until the result is ready. The API uses long-polling, so you'll need to repeatedly check until you receive a success status.

Readme

Table of Contents
Overview
Technical Specifications
Key Considerations
Tips & Tricks
Capabilities
What Can I Use It For?
Things to Be Aware Of
Limitations

Overview

openai-search-preview — Text-to-Text AI Model

Developed by OpenAI as part of the gpt family, openai-search-preview is a text-to-text AI model that delivers verifiable, cited answers by integrating real-time web search into its responses, solving the problem of outdated or hallucinated information in standard language models. This OpenAI text-to-text capability, known as GPT-4o Search Preview, empowers developers and creators seeking instant, up-to-date insights without manual research. Users searching for "openai-search-preview API" or "OpenAI search preview" will find it excels at grounding responses in current web data, providing links to sources for transparency and reliability.

Technical Specifications

What Sets openai-search-preview Apart

openai-search-preview stands out in the text-to-text AI model landscape through its native integration of web search, delivering fewer hallucinations and more accurate results compared to non-search-enabled models like standard GPT variants. This enables users to get fact-checked answers on dynamic topics such as current events or shopping queries, with improved formatting for quick comprehension. Unlike generic text-to-text models, it supports adjustable context size and location parameters, optimizing for real-time data retrieval in applications like GPT for Sheets.

  • Real-time web grounding with citations: Pulls live information from the web and includes source links; this allows developers building "OpenAI text-to-text" apps to trust outputs for business-critical decisions without verification steps.
  • Enhanced factuality and shopping intent detection: Reduces errors in factual responses and surfaces products precisely when needed; ideal for e-commerce tools where users query "AI search preview for products," ensuring focused, actionable results.
  • Preview efficiency on GPT-4o architecture: Leverages GPT-4o's 128,000-token input context for handling complex queries with search; supports text inputs/outputs with average response times balancing speed and depth, outperforming older models in STEM and non-English tasks.

Key Considerations

  • The model automatically selects the closest supported aspect ratio if an unsupported one is requested
  • Higher resolutions and quality settings increase both cost and generation time
  • For best results, provide clear, detailed prompts and use iterative refinement for complex edits
  • Inpainting and outpainting require precise mask definition for targeted changes
  • Batch generation (up to 8 images per call) is ideal for creative exploration and A/B testing
  • Outputs include C2PA provenance metadata for traceability
  • Prompt engineering is crucial: descriptive, unambiguous instructions yield more accurate results

Tips & Tricks

How to Use openai-search-preview on Eachlabs

Access openai-search-preview seamlessly through Eachlabs' Playground for instant testing, API for production integration, or SDK for custom apps. Provide text prompts specifying search needs, adjust context size and location via parameters, and receive cited text outputs grounded in real-time web data. Expect high-accuracy responses with source links, optimized for developers seeking reliable OpenAI text-to-text performance.

---

Capabilities

  • Generates high-quality images from textual prompts with strong stylistic control
  • Supports inpainting, outpainting, and style editing of existing images
  • Handles multiple aspect ratios and resolutions, including print-ready formats
  • Maintains spatial and stylistic coherence across edits and refinements
  • Scales efficiently for batch generation and automated workflows
  • Embeds provenance metadata for transparency and content tracking

What Can I Use It For?

Use Cases for openai-search-preview

Developers integrating real-time search APIs can use openai-search-preview to power apps that answer queries like "latest stock prices for tech companies with analysis," combining GPT-4o reasoning with web data for cited financial insights, streamlining data pipelines without external scrapers.

Marketers searching for "openai-search-preview API" benefit from its shopping intent detection by inputting prompts such as "best laptops under $1000 for video editing with current reviews," receiving formatted product recommendations backed by live web sources, perfect for dynamic campaign content.

Researchers and educators leverage its factuality improvements for academic workflows; for instance, a prompt like "explain quantum entanglement with recent experiments and citations" yields comprehensive, hallucination-free explanations grounded in up-to-date papers, enhancing teaching materials with verifiable depth.

Content creators building knowledge tools use its location-aware search for localized info, such as "current weather impacts on Paris fashion week events," generating tailored reports that integrate global data, supporting multilingual outputs superior to base GPT models.

Things to Be Aware Of

  • Some experimental features (e.g., advanced inpainting) may behave unpredictably in edge cases, as noted in community discussions
  • Users report that prompt specificity greatly affects output quality; vague prompts yield generic results
  • High-resolution outputs require significant computational resources and may have longer generation times
  • Consistency across multiple generations can vary, especially for highly detailed or abstract prompts
  • Positive feedback highlights the model's ease of use, quality of outputs, and flexible editing capabilities
  • Common concerns include occasional artifacts in complex edits and the need for iterative refinement to achieve desired results

Limitations

  • The model's internal parameters and detailed architecture are not publicly disclosed, limiting transparency for some technical users
  • May not perform optimally for highly specialized or photorealistic tasks requiring domain-specific training data
  • Generation speed and cost can become significant at the highest resolutions and quality settings

Pricing

Pricing Detail

This model is charged at $0.0000025 per input token and $0.00001 per output token per execution.

The average execution time is 8 seconds, but this may vary depending on your input data and complexity.

Pricing Type: Input Token and Output Token

This model uses token-based pricing. This means that the text you provide (input tokens), any images you include, and the content generated by the model (output tokens) determine the total number of tokens used in the process, which affects the cost. There is no fixed fee; the price varies based on the total tokens consumed. Additionally, choices like quality, background type, image size, and number of images are factors that influence pricing. Depending on these selections, token usage and cost may vary.