GPT Image 2 Slow? Check Quality, Size and Latency

For the new models, see GPT Image 2.5 Flare vs Sunburst and the migration checklist. This article retains its GPT Image 2 scope.

If GPT Image 2 is slow, check quality and size first, then investigate the rest of the request path. In the September 7, 2026 Ofox test at 1024×1024, high took 117.1–132.1 seconds and low took 16.6–19.6 seconds. That makes quality a useful control to test, but it does not make high quality the cause of every slow request.

This guide is for developers choosing a generation tier and diagnosing API latency. The six saved responses and original client timing log were reviewed on September 8. No new paid benchmark was run.

GPT Image 2 quality: measured latency and output cost

The original test used the same prompt, a fixed 1024×1024 size and two requests per quality tier on openai/gpt-image-2 through Ofox’s /v1/images/generations route.

All six saved PNGs decode to 1024×1024. Both responses within each tier report the same image-token count. Costs use the recorded $30 per million image-output-token rate; the table excludes input costs. Each saved response also records 14 text input tokens.

OpenAI’s image generation cost table lists approximately $0.006, $0.053 and $0.211 for the three square-image tiers. These rounded values agree with the calculated image components above. Check the Ofox GPT Image 2 page for current provider details.

The high/low token ratio is 7,024 ÷ 196, or about 35.84×. The mean client-time ratio is about 6.88×. Those different ratios do not support the original article’s claim of almost-linear scaling between tokens and latency.

Two runs per tier show a useful local observation, not a latency distribution or service guarantee. The log measures client wall-clock time, which includes network and provider waiting time as well as generation.

What the omitted quality parameter returned

One saved response from a request omitting both quality and size reported:

{
  "quality": "low",
  "size": "1322x1190",
  "usage": {
    "output_tokens": 201,
    "output_tokens_details": {"image_tokens": 201}
  }
}

The decoded PNG is also 1322×1190. This is direct evidence that this response reported low quality. It is not a controlled comparison with the 1024×1024 calls and does not establish the default across all prompts, routes or future requests.

For reproducible behavior, send both parameters explicitly and inspect the response. Do not infer a tier solely because its token count is close to another sample.

How to reduce GPT Image 2 latency

Start with an explicit low-quality draft request:

curl -X POST https://api.ofox.run/v1/images/generations \
  -H "Authorization: Bearer $OFOX_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "openai/gpt-image-2",
    "prompt": "A single red apple on a white table",
    "size": "1024x1024",
    "quality": "low"
  }'

Use low for inexpensive iterations if its output suits the job. Evaluate medium for a candidate asset and high when the result benefits enough to justify the additional wait and output cost. This is a workflow recommendation, not a visual-quality benchmark: the test did not score image usefulness or preference.

If the call is still slow:

1. Record model, provider, prompt, size, quality, elapsed time and response status.
2. Check whether the request completed, timed out or returned an error before retrying. A retry can create another billable generation.
3. Compare equivalent successful requests across several runs. Separate failures from completed-call timings.
4. Examine prompt complexity, network conditions and provider incidents instead of attributing every delay to quality.

OpenAI’s image generation limitations note that complex prompts can take longer. Our fixed-prompt test does not establish that prompt length or complexity has negligible impact.

For a product that cannot keep a user waiting on one HTTP request, an application-owned background job can run the generation call and expose your own job status. This architecture does not imply that the Ofox images endpoint itself supports a native asynchronous job ID or polling API.

Comparing GPT Image 2 with Nano Banana 2

The Nano Banana 2 billing review records 1,120 image tokens for a 1K output. At the recorded rates, its $0.0672 image component falls between GPT Image 2 medium and high in this example.

That is a cost comparison under stated settings. It does not establish equivalent image quality or relative speed. Different models’ output tokens are not a common unit of generation time. Nano Banana 2 also supports thinking, so its total work and billing should not be described as universally constant.

Before selecting a production route, compare useful outputs, complete billed costs and client timings for your own prompts. Use the Nano Banana 2 model page to confirm route details rather than transferring GPT Image parameters to another model.

Related

• GPT Image 2 generation failures — troubleshoot errors separately from latency.
• GPT Image 2 release guide — review the broader parameter surface.

Correction, September 8, 2026: replaced the universal default-low claim with the actual omitted-parameter response, corrected its dimensions, and removed unsupported linear-latency and cross-model speed claims. Timing values remain historical measurements from one route on September 7; output costs exclude input.