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OpenAI

GPT Image 2.5 Flare (max)

GPT Image 2.5 Flare (max) is an image generation model from OpenAI, offered as the "max" quality setting of the GPT Image 2.5 Flare line.

Input from
$4.00 / 1M tokens
on IteraCompute · 1 provider
Output from
$0.0000 / 1M tokens
on IteraCompute
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API Pricing

ProviderInput / 1MOutput / 1MCached / 1M
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IteraCompute
$4.00$0.0000$1.00

Prices updated daily. Last check: Oct 28, 2026

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GPT Image 2.5 Flare (max) pricing by provider

Input, output, and batch rates, plus alternatives, for one provider at a time.

Model Details

General

Creator
OpenAI
Modalities
Text

Capabilities

Strengths & Limitations

Strengths

  • Published by OpenAI, so it is typically reachable through the same API tooling and SDKs teams already use for other OpenAI endpoints
  • "max" tier targets the highest rendering effort available for the Flare variant, aimed at final-quality output rather than drafts
  • Part of a tiered line, letting teams prototype on a cheaper effort setting and switch to max for production renders without changing prompt format
  • Image generation is billed per image rather than per token, which makes cost forecasting straightforward for fixed-volume workloads
  • Priced across multiple providers where available, so the pricing table on this page can be used to compare per-image rates

Limitations

  • Our database does not record confirmed maximum resolution, aspect ratio support, or editing/inpainting capabilities for this model
  • No published benchmark scores are tracked for it, so quality claims cannot be verified from our data
  • Highest-effort tiers generally carry longer generation latency and higher per-image cost than lower-effort settings in the same family
  • Model weights are not something we track as available for self-hosting, so usage depends on API providers
  • As an image generation model it is not usable for chat, reasoning, or text-completion workloads

Key Features

•Text-to-image generation from natural language prompts
•"max" effort tier within the GPT Image 2.5 Flare line
•Published by OpenAI
•Per-image billing model rather than token-based pricing
•API-accessed generation suitable for server-side integration
•Tiered quality settings allowing draft-to-final workflows within one model family

About GPT Image 2.5 Flare (max)

GPT Image 2.5 Flare (max) is an image generation model published by OpenAI. It sits within OpenAI's GPT Image family, which covers text-to-image generation and image editing accessed through an API rather than a chat completion endpoint. The "max" suffix identifies the highest-effort rendering tier of the Flare variant, as distinct from lower-effort settings of the same underlying model, which typically trade rendering detail and generation time against cost per image. Because this is an image generation entry rather than a chat model, the specifications that matter are different from those of a text LLM: output resolution and aspect ratio options, prompt adherence, text rendering inside images, and per-image latency rather than context window or tokens per second. Our catalog records the creator and model type for GPT Image 2.5 Flare (max); we do not currently track confirmed figures for its maximum resolution, supported edit modes, or published benchmark scores, so none are asserted here. Providers listing the model in the pricing table on this page are the authoritative source for the exact parameters they expose. In practice, a "max" quality tier is chosen when output fidelity matters more than throughput — final marketing assets, hero images, or detailed compositions — while lower-effort tiers of the same family are usually the better fit for drafts, thumbnails, and high-volume batch generation. Teams comparing GPT Image 2.5 Flare (max) against alternatives generally evaluate prompt adherence and generation time on their own prompt set, since image model quality is difficult to summarize in a single score.

Common Use Cases

GPT Image 2.5 Flare (max) suits work where a single image needs to be as clean as the model can produce: marketing and ad creative, editorial illustration, product mockups, storyboard key frames, and app or website hero imagery. The max effort setting is generally the wrong choice for high-volume pipelines such as bulk thumbnail generation, A/B creative sweeps, or interactive prototyping loops, where a lower-effort tier of the same family delivers faster turnaround at lower per-image cost. A common pattern is to iterate on prompts at a cheaper setting and re-render the chosen prompts at max for delivery. Because specific resolution and editing capabilities are not recorded in our data, teams with hard requirements on output size or inpainting should confirm those details with the provider before committing.

Frequently Asked Questions

How much does GPT Image 2.5 Flare (max) cost?

Image models are usually billed per generated image, and the rate varies by provider and by the quality or effort tier selected. Check the pricing table on this page for current per-image rates from the providers we track.

What is GPT Image 2.5 Flare (max) best used for?

It is aimed at final-quality image generation — marketing creative, illustration, product mockups, and hero imagery — where output fidelity matters more than generation speed or per-image cost.

How does the "max" tier differ from other GPT Image 2.5 Flare settings?

The max suffix denotes the highest rendering effort for this variant. In tiered image models, higher effort settings generally spend more compute per image, which tends to mean longer latency and higher cost in exchange for more detailed output.

Can GPT Image 2.5 Flare (max) be used for chat or text tasks?

No. It is catalogued as an image generation model, so it produces images from prompts rather than text responses. For conversational or reasoning workloads you would use a chat model instead.

What resolutions and editing modes does it support?

Our database records the creator and model type but not confirmed resolution limits or editing capabilities such as inpainting. Consult the documentation of the provider you plan to use for those specifics.

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