GPT Image 2 vs GPT Image 1.5 vs GPT Image 1: An OpenAI-Compatible Migration Guide

Choosing among GPT Image 2, GPT Image 1.5, and GPT Image 1 is less about picking the largest version number and more about controlling migration risk. For a new integration, GPT Image 2 is the sensible first evaluation target. For an existing production workflow, compare it against the version already in use with a fixed test set before switching traffic.

The EvoLink GPT Image family page presents GPT Image 2, GPT Image 1.5, and GPT Image 1 as options available through one API. That makes it useful for a first-pass family comparison. OneMux provides a practical OpenAI-compatible route for GPT Image 2, along with model access, routing, API keys, spend visibility, credit top-ups, and pay-as-you-go usage.

The decision in one minute

• Starting a new product: Evaluate GPT Image 2 first. It is the focus model for this guide and the GPT Image model listed in the supplied OneMux catalogue.
• Migrating an existing image feature: Reproduce a sample of real requests on GPT Image 2, then compare outputs and operational behavior with your current GPT Image 1 or 1.5 workflow.
• Trying to reduce migration risk: Keep the model identifier configurable, preserve the old path during testing, and normalize image responses inside your own application.
• Comparing versions on one API: Use the EvoLink family page for the family-level comparison, then verify the exact request fields, file behavior, response format, and pricing for the route you intend to use.

The supplied source material does not provide benchmark results or a complete feature matrix for GPT Image 1.5 and GPT Image 1. That is a reason to test rather than declare an unsupported winner.

Compare the models on dimensions that affect your product

1. Prompt and output behavior

Build a fixed evaluation set from production-like requests, not only polished examples. Include short prompts, long structured prompts, brand terminology, multilingual copy, and requests that place readable text inside an image. Record whether the model follows composition, subject, style, aspect ratio, and exclusion instructions.

For marketers, the most useful test may be a batch of campaign variants with consistent brand constraints. For support teams, test whether an image workflow can reliably produce the same visual treatment across a set of help-center illustrations. For founders and operators, the important question is not only visual quality; it is whether the output is predictable enough to fit review and approval processes.

2. Editing and file workflows

The OneMux catalogue tags GPT Image 2 with Files and Vision and describes it as an image model for prompt-driven generation, editing, and visual design workflows. Treat those tags as a starting point for testing, not as a substitute for checking endpoint documentation.

Test the actual assets your application sends: file type, size, transparency, dimensions, and multiple-image inputs if your use case needs them. Compare how each family version handles an edit request versus a fresh generation. A migration can fail even when prompts are unchanged if your application assumes a particular image encoding, output field, or URL lifetime.

3. Operational behavior

Measure the parts users experience directly:

• request success and error categories;
• response time under realistic concurrency;
• retry behavior and duplicate-generation risk;
• output availability and download handling;
• moderation or validation steps in your own pipeline;
• per-request cost at your actual mix of generations and edits.

Do not add automatic retries until you know whether a failed request is safe to repeat. If a generation triggers a paid downstream action, use an internal request ID and store the result before marking the job complete.

A low-risk OpenAI-compatible migration pattern

The cleanest migration keeps provider-specific details at the edge of your application. Put the API base URL, key, model name, image size, output format, and timeout in configuration. The rest of the product should call an internal function such as create_image(prompt, assets, options).

An illustrative Python shape using an OpenAI-compatible client looks like this:

import os
from openai import OpenAI

client = OpenAI(
    api_key=os.environ['ONEMUX_API_KEY'],
    base_url=os.environ['ONEMUX_BASE_URL'],
)

response = client.images.generate(
    model='gpt-image-2',
    prompt='Editorial product illustration of a reusable water bottle on a clean desk'
)

print(response.data[0].url)

This is a migration pattern, not a guarantee of the exact OneMux endpoint or response fields. Set ONEMUX_BASE_URL according to the OneMux quickstart, and confirm the image route and response handling in the OneMux documentation. In production, normalize the response immediately so the rest of your code does not depend on provider-specific fields.

A practical rollout has four stages:

1. Shadow evaluation: Send a controlled sample to GPT Image 2 without changing the user-visible result.
2. Human review: Score prompt adherence, visual acceptability, text accuracy, and editing quality.
3. Limited traffic: Route a small, observable portion of requests to the new model.
4. Decision review: Compare quality, failure rates, processing time, and cost before changing the default.

Budget and spend controls

OneMux lists GPT Image 2 at $0.08 per request for input and $0.08 per request for output on its model page. Do not turn those two figures into a blanket per-image estimate without checking how the selected endpoint charges the request and what your application sends. Confirm current details on the OneMux pricing page.

For operators, request-based pricing makes workload shape important. Separate fresh generations from edits, retries, previews, and production exports in your own usage records. Set a budget for evaluation before running a large prompt sweep. OneMux’s spend visibility and credit top-up workflow can help keep model testing and production usage distinct, while its pay-as-you-go approach lets teams account for actual usage rather than assuming a fixed volume.

International teams should also document the billing currency, tax treatment, payment method, and top-up rules that apply to their account. Those details are operational requirements, not model-quality claims, so verify them directly before committing to a launch plan.

Who should choose which path?

• Developers: Choose GPT Image 2 as the first implementation target, but keep the model name configurable and write contract tests for request and response handling.
• Founders: Start with a small evaluation set tied to the product’s core image workflow. Avoid changing models solely because a newer label sounds better.
• Operators: Track retries, queue behavior, spend, and failure categories separately from subjective image ratings.
• Marketers: Test brand consistency, multilingual text, campaign variants, and approval time—not just whether individual images look attractive.
• Support teams: Validate repeatability, asset handling, and how easily generated files move into existing ticket or knowledge-base workflows.

Conclusion

GPT Image 2 should be the first model to evaluate for a new OneMux image integration, but GPT Image 1.5 and GPT Image 1 still have a role as controlled comparison points for existing workloads. The defensible choice comes from a representative test set, explicit operational measurements, and a migration design that keeps model-specific details configurable.

Use the EvoLink family page to frame the three-version comparison. Then use OneMux’s GPT Image 2 model page, documentation, and pricing information to validate the implementation and operating model before moving production traffic.

Sources

• EvoLink GPT Image family page — source for the family-level comparison of GPT Image 2, GPT Image 1.5, and GPT Image 1 on one EvoLink API.
• OneMux GPT Image 2 model page — source for the OneMux model description, Files and Vision tags, and listed request-based pricing.
• OneMux documentation and quickstart — implementation references for configuring an OpenAI-compatible integration.