GPT Image 1.5 API Production Guide: Pricing, Latency Patterns, and Scaling Architecture

Executive Summary: What GPT Image 1.5 Changes in Production

GPT Image 1.5 is positioned as OpenAI's most capable general-purpose text-to-image model at launch, with emphasis on:

• Instruction following: more reliable changes "down to small details."
• Editing & preservation: better at applying edits while keeping key elements consistent (including facial likeness and branded visuals across edits).
• Text rendering: improved ability to render dense text within images.
• Speed: generation speeds up to 4× faster (reported by OpenAI).

Competitive Landscape: GPT Image 1.5 vs Nano Banana Pro vs FLUX

Production Performance: Latency Patterns and Reliability

Common latency drivers across image models:

• Resolution & aspect ratio (larger outputs take longer)
• Prompt complexity and iterative edits
• Traffic spikes / queueing
• Retry loops after safety rejections or transient failures

• Use timeouts + idempotency keys (or your own request IDs)
• Add async job queues for long-running generations
• Implement graceful fallbacks (lower quality, smaller size, or alternative model)

Safety Filters: Plan for Rejections as a First-Class Outcome

OpenAI's image APIs enforce safety policies; prompts or edits may be rejected. In production you should treat "rejected" as a normal outcome:

• Show actionable UI feedback to users
• Log rejection categories (when available)
• Provide safe re-prompt suggestions
• Avoid retry storms (rate-limit retries)

Pricing: Official GPT Image 1.5 Costs (Per Image + Tokens)

OpenAI publishes both:

1. Per-image prices by quality and size
2. Image token prices (for image inputs/outputs in token accounting)

Per-image prices (official)

Image token prices (official)

• gpt-image-1.5: image tokens Input $8 / Output $32 per 1M tokens
• gpt-image-1: image tokens Input $10 / Output $40 per 1M tokens

Developer Experience: What You Should Architect Around

Even when the model is strong, shipping a reliable product requires engineering for:

1. Rate limits & backpressure (plan for 429s and queue requests)
2. Schema drift across providers (different parameters, error codes, response formats)
3. Observability (per-request cost, latency percentiles, failure reasons, fallback rates)

The EvoLink Angle: Unified API Patterns

A unified gateway approach can reduce operational burden by:

• Standardizing request/response formats across vendors
• Adding routing rules (e.g., choose GPT Image 1.5 for text-heavy posters; choose another model for photoreal scenes when acceptable)
• Implementing fallback strategies when a provider rejects or errors
• Providing centralized usage analytics for cost and performance tracking

Quick Start: GPT Image 1.5 via EvoLink

EvoLink provides a unified endpoint for GPT Image 1.5 that supports text-to-image, image-to-image, and image editing modes with asynchronous processing.

Example: Text-to-Image

curl --request POST \
  --url https://api.evolink.ai/v1/images/generations \
  --header 'Authorization: Bearer YOUR_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "gpt-image-1.5-lite",
    "prompt": "A professional product photo of a sleek smartwatch on a marble surface, soft studio lighting, 4K quality",
    "size": "1024x1024",
    "quality": "high"
  }'

Example: Image Editing

curl --request POST \
  --url https://api.evolink.ai/v1/images/generations \
  --header 'Authorization: Bearer YOUR_API_KEY' \
  --header 'Content-Type: application/json' \
  --data '{
    "model": "gpt-image-1.5-lite",
    "prompt": "Change the background to a sunset beach scene, keep the product unchanged",
    "image_urls": ["https://your-cdn.example.com/product-photo.jpg"],
    "size": "1024x1024",
    "quality": "high"
  }'

Response format

The API returns an async task. Poll the task status using the returned ID:

{
  "created": 1757156493,
  "id": "task-unified-1757156493-imcg5zqt",
  "model": "gpt-image-1.5-lite",
  "status": "pending",
  "progress": 0,
  "task_info": {
    "can_cancel": true,
    "estimated_time": 100
  },
  "usage": {
    "credits_reserved": 2.5
  }
}

Note: Generated images expire after 24 hours. Download and store them promptly.

Conclusion

To ship reliably at scale, treat images as an infra problem: measure latency distributions, budget with official per-image pricing, handle safety rejections gracefully, and design routing/fallback patterns that protect user experience and unit economics.