API Need
OGPT-Image-2.5 Sunburst API
gpt-image-2.5-sunburst
GPT-Image-2.5 Sunburst is OpenAI's quality-focused image model for generating visuals and making precise, reference-guided edits.
Use GPT-Image-2.5 Sunburst when image quality, detailed instruction following, and preservation of important reference details matter more than minimum generation time. Through APINEED, GPT-Image-2.5 Sunburst accepts text and image inputs and returns image output in a synchronous workflow, making it suitable for controlled asset generation, product imagery, campaign concepts, design iteration, and targeted image transformations.
MODALITIES
Playground
Generate with gpt-image-2.5-sunburst, review the output, and use the same request shape in your application.
Output Ready
Request preview
{
"model": "gpt-image-2.5-sunburst",
"messages": [
{
"role": "user",
"content": "A refined product photograph with natural lighting, realistic materials and a clean background."
}
],
"stream": false
}Provider
Current API Need routing.
Pricing
Current selling prices in USD. Channel and group adjustments are already included. A range means available routes have different prices.
Image output$31.515 / 1M image tokens
Input$5.2525 / 1M
Text output$0 / 1M
Image input$8.404 / 1M image tokens
Web search$10.505 / 1K calls
Availability
Recent public performance data for this model.
No model-specific availability history is published yet. A successful request is not an uptime guarantee.
Gallery
Quick Start
Call gpt-image-2.5-sunburst with the endpoint and request format shown below.
Create an API key
Store your APINEED key server-side as API_NEED_API_KEY.
export API_NEED_API_KEY=sk-apineed-v1-...Send the request
Use gpt-image-2.5-sunburst with POST /v1/chat/completions.
curl https://apineed.com/v1/chat/completions \
-H "Authorization: Bearer $API_NEED_API_KEY" \
-H "Content-Type: application/json" \
-d '{
"model": "gpt-image-2.5-sunburst",
"messages": [
{
"role": "user",
"content": "A refined product photograph with natural lighting, realistic materials and a clean background."
}
],
"stream": false
}'Parse the image
Read choices[0].message.content and detect Markdown or raw image data URLs.
const content = response.choices[0].message.content;gpt-image-2.5-sunburst API Endpoint
Submit text and optional image content blocks through the Chat Completions-compatible route.
POST
/v1/chat/completions- Authorization
- Bearer $API_NEED_API_KEY
- Content-Type
- application/json
- HTTP-Referer
- optional - your site URL, for rankings
- Model
- gpt-image-2.5-sunburst
Parameters
Use the supported parameters below. Leave optional fields out to use the service defaults.
| Name | Type | Values | Description |
|---|---|---|---|
model | string | Required | Use gpt-image-2.5-sunburst. |
messages | array | Required | Conversation messages sent to the model. |
stream | boolean | false | The playground reads the completed image response. |
GPT-Image-2.5 Sunburst API Introduction
A researched guide to GPT-Image-2.5 Sunburst, its capabilities, integration fit, and operating considerations.
What is GPT-Image-2.5 Sunburst?
GPT-Image-2.5 Sunburst is the quality-oriented base model in OpenAI's GPT Image 2.5 series. It generates new images from text and edits existing images using text and image inputs. OpenAI positions it for demanding work where precise editing and high output quality take priority over the shortest response time. Compared with the speed-oriented Flare variant, GPT-Image-2.5 Sunburst is intended to offer an additional level of precision for detailed creative production, although it can take longer to generate an image. Choose GPT-Image-2.5 Sunburst for work in which subject identity, composition, texture, lighting, or localized changes must survive refinement. Treat GPT-Image-2.5 Sunburst as a generative system rather than a deterministic renderer: identical requests may produce different pixels, so production applications should support review, regeneration, and selection. [1][2][4]
Input and output behavior with GPT-Image-2.5 Sunburst
GPT-Image-2.5 Sunburst accepts text and image inputs and produces image output. A text-only request can describe a new scene, while an image-assisted request can supply a source, visual reference, or editing target. GPT-Image-2.5 Sunburst does not provide ordinary text as its primary output, and OpenAI's model documentation does not advertise streaming, function calling, structured outputs, or fine-tuning for this model. In the supplied APINEED contract, GPT-Image-2.5 Sunburst runs synchronously through POST /v1/chat/completions using the executable identifier `gpt-image-2.5-sunburst`. Applications should therefore expect the request to remain open until processing finishes and should configure realistic client and proxy timeouts. The contract advertises text-to-image and image-edit workflows, but developers should not assume that every control documented for OpenAI's native image interfaces is automatically exposed through this integration. [1][3]
Best workloads for GPT-Image-2.5 Sunburst
GPT-Image-2.5 Sunburst is best evaluated for polished assets that benefit from careful composition and editing precision. Promising workloads include product-scene variations, campaign concepts, presentation visuals, editorial illustrations, branded creative exploration, reference-led character transformations, and revisions that alter one element while retaining the surrounding scene. OpenAI reports improvements in subject preservation, natural lighting, textures, detailed instruction following, and consistency across iterative edits for the broader Images 2.5 generation. GPT-Image-2.5 Sunburst is specifically positioned above the Flare variant when demanding quality requirements justify longer processing. It is less compelling when throughput and low latency dominate and a faster model already meets acceptance criteria. Before standardizing on GPT-Image-2.5 Sunburst, test whether its quality advantage is visible in the exact image categories, languages, dimensions, and edit types your users request. [2][4][1]
Prompting GPT-Image-2.5 Sunburst effectively
Prompts for GPT-Image-2.5 Sunburst should begin with the concrete image needed, then define the subject, setting, composition, visual treatment, and non-negotiable constraints. For example, specify camera position, framing, lighting direction, material properties, background, color relationships, and the intended destination rather than relying on a vague style label. When text must appear inside an image, provide the exact wording, placement, hierarchy, and surrounding design context, then verify spelling visually. GPT-Image-2.5 Sunburst benefits from instructions that separate required elements from optional creative freedom. Avoid packing conflicting art directions into one request. OpenAI recommends refining one aspect at a time and inspecting each result, an approach that also makes failures easier to diagnose. With GPT-Image-2.5 Sunburst, concise structure and explicit constraints generally create a more reproducible evaluation process than repeatedly adding unsorted adjectives. [4][3]
Editing images with GPT-Image-2.5 Sunburst
For an edit, tell GPT-Image-2.5 Sunburst both what must change and what must remain unchanged. Identify the target region or object, describe the replacement or transformation, and preserve anchors such as subject identity, pose, crop, perspective, lighting, typography, palette, and background geometry where relevant. GPT-Image-2.5 Sunburst is designed for precise editing and stronger preservation of reference subjects, but those improvements do not guarantee pixel-perfect isolation. Masks and reference images can provide additional control when supported by the integration, yet the supplied APINEED execution profile does not enumerate optional image parameters. Validate those fields against the rendered API documentation before depending on them. When a revision is broad or ambiguous, GPT-Image-2.5 Sunburst may reinterpret details outside the intended area. Keep source assets, prompts, generated candidates, and approval decisions so an accepted composition can be reconstructed or audited. [1][2][3][4]
Evaluating GPT-Image-2.5 Sunburst
Evaluate GPT-Image-2.5 Sunburst with a representative prompt suite rather than a few showcase examples. Score instruction adherence, reference-subject preservation, unwanted changes, text accuracy, composition, visual defects, brand fit, safety outcomes, response time, and reviewer effort. For edits, compare both the requested change and protected regions that should remain stable. Because GPT-Image-2.5 Sunburst is nondeterministic, generate multiple samples for important prompt classes and record variance instead of judging a single favorable result. Use blinded human review where aesthetic preference matters, and create automated checks for measurable requirements such as dimensions, transparency, file integrity, or expected text regions. Compare GPT-Image-2.5 Sunburst against a faster alternative on the same inputs. OpenAI specifically recommends retaining Sunburst only when its quality advantage is necessary, since performance depends on prompts, references, output dimensions, and quality settings. [4][2]
Latency and operations for GPT-Image-2.5 Sunburst
GPT-Image-2.5 Sunburst favors precision over minimum latency, and OpenAI explicitly describes longer generation times than its Flare counterpart. APINEED's supplied execution profile is synchronous, so production clients should use timeouts appropriate for image generation, avoid immediate duplicate retries, and attach application-level request identifiers for reconciliation. A network timeout does not necessarily prove that GPT-Image-2.5 Sunburst failed before producing an asset; retry logic should therefore be idempotency-aware where the surrounding application supports it. Limit concurrency, queue burst traffic, and monitor latency percentiles separately by image dimensions, number of inputs, and edit complexity. Cache approved outputs rather than regenerating stable assets on every page load. If GPT-Image-2.5 Sunburst becomes a user-facing dependency, provide progress feedback and a recoverable job state even though the advertised API call itself is synchronous. Consult the page's live Pricing section when modeling operational cost. [1][4][2]
Safety and governance for GPT-Image-2.5 Sunburst
GPT-Image-2.5 Sunburst should sit behind product-level safeguards, human review, and clear rules for sensitive imagery. OpenAI identifies heightened realism as a safety concern because it can enable more convincing deceptive or harmful depictions of real people, events, and places. OpenAI also describes prompt and image checks, C2PA metadata, invisible watermarking, and additional mitigations for the Images 2.5 family. Those controls do not replace an application's own consent, identity, copyright, privacy, age-appropriate design, and abuse-response processes. Before deploying GPT-Image-2.5 Sunburst, define prohibited requests, escalation paths, retention rules, and review requirements for public-facing or high-impact assets. Preserve provenance metadata when transforming files, label synthetic media where context requires it, and do not represent an output as verified evidence. Teams should periodically retest GPT-Image-2.5 Sunburst against adversarial prompts and emerging misuse patterns. [2][5][7]
GPT-Image-2.5 Sunburst API Q&A
Practical answers for developers evaluating and integrating GPT-Image-2.5 Sunburst.
Which model ID should I use for gpt-image-2.5-sunburst?
Use gpt-image-2.5-sunburst, exactly as returned by the API Need pricing API.
Which endpoint should I use?
Send a POST request to /v1/chat/completions using your APINEED API key. Follow the request example and parameter table on this page.
How is the API Need price calculated?
See Pricing for the current rates and discounts. Final charges use recorded usage.
How should I call GPT-Image-2.5 Sunburst through APINEED?
Use the supplied APINEED contract: send a synchronous POST request to `/v1/chat/completions` and set `model` to `gpt-image-2.5-sunburst`. GPT-Image-2.5 Sunburst can receive a textual instruction and, for editing, applicable image input. Follow APINEED's rendered request and response examples for asset encoding and result extraction; OpenAI's native Image or Responses API examples describe the underlying provider interfaces, not additional APINEED routes. [1][3]
Does GPT-Image-2.5 Sunburst support image editing?
Yes. GPT-Image-2.5 Sunburst is documented for both generation and editing from text and image inputs, while the APINEED contract advertises `image_edit` as a supported workflow. For better results, state the intended modification and separately list the features that must remain unchanged. GPT-Image-2.5 Sunburst improves editing precision and reference preservation, but every output still requires inspection for unintended changes. [1][3][4]
Is GPT-Image-2.5 Sunburst the fastest GPT Image 2.5 option?
No. OpenAI positions GPT-Image-2.5 Sunburst as the quality-focused option with greater precision and longer generation times, while Flare emphasizes speed. Benchmark end-to-end latency with your own prompts, source images, dimensions, and concurrency because a general comparison does not establish a fixed response time for a particular application. [2][4]
Can GPT-Image-2.5 Sunburst preserve a person or product from a reference?
GPT-Image-2.5 Sunburst is designed to preserve reference subjects more reliably than earlier models, making it relevant to identity-sensitive variations and product edits. Preservation is not guaranteed: inspect faces, logos, proportions, materials, colors, and background details. Obtain appropriate consent and rights before supplying identifiable people, protected designs, or customer assets. [2][4][5]
Can GPT-Image-2.5 Sunburst render text inside an image?
GPT-Image-2.5 Sunburst can generate designs containing text, and OpenAI reports improved accuracy for infographic and layout tasks across Images 2.5. Generated lettering can still contain spelling, punctuation, alignment, or consistency errors. Provide exact copy and visual hierarchy, inspect at full resolution, and use conventional typesetting when every character must be guaranteed. [2][6][4]
Does GPT-Image-2.5 Sunburst always return the same result?
No. GPT-Image-2.5 Sunburst is generative, so repeated requests can vary in composition and detail. Store accepted assets rather than expecting exact regeneration. For evaluations, retain the prompt, input images, requested settings, model identifier, and output; assess several samples per prompt category to understand both average quality and failure variance. [4][3]
What image controls are available for GPT-Image-2.5 Sunburst?
OpenAI documents provider-side controls for GPT-Image-2.5 Sunburst that include quality, size, background, output format, and compression choices. However, the supplied APINEED execution profile declares no optional parameters. Do not assume those provider controls pass through this endpoint; use only fields shown by APINEED's current parameter documentation and validate them before production use. [1][3][4]
How should I test GPT-Image-2.5 Sunburst before production?
Build a dataset representing real generation and editing requests, including difficult text, crowded compositions, varied reference quality, protected regions, and policy-sensitive edge cases. Evaluate GPT-Image-2.5 Sunburst for adherence, visual quality, preservation, safety, latency, and manual correction effort. Re-run the suite after model or application changes and retain human-approved regression examples. [4][6]
What safety precautions apply to GPT-Image-2.5 Sunburst?
Applications using GPT-Image-2.5 Sunburst should screen requests, protect personal data, obtain consent for identifiable subjects, review sensitive outputs, and preserve provenance indicators. OpenAI warns that increased realism can amplify deepfake risks involving real people or events. Provider safeguards help, but developers remain responsible for product rules, user reporting, access controls, and incident handling. [5][7][2]
Are context length and maximum completion tokens published for GPT-Image-2.5 Sunburst?
OpenAI's current model page does not publish a conventional context-window size or maximum text-completion length for GPT-Image-2.5 Sunburst. This catalog therefore records both fields as unknown rather than inferring language-model limits. Capacity planning should focus on documented image inputs, output dimensions, request constraints, latency, and the parameters exposed by the APINEED contract. [1][3]
How to deploy gpt-image-2.5-sunburst
Connect gpt-image-2.5-sunburst to your application through the APINEED media API.
Add credits
Add APINEED credits before submitting a billable generation.
Create an API key
Create and securely store an APINEED API key.
Configure the endpoint
Send gpt-image-2.5-sunburst requests to /v1/chat/completions.
Handle the result
Extract generated image data URLs from the completion response.