GPT-image-2.5 Launches: Flare Is Faster, Sunburst Is Sharper

Key Highlights

🚀 One generation, two models: gpt-image-2.5-flare is speed-first, gpt-image-2.5-sunburst is quality- and editing-first; they currently point to gpt-image-2.5-flare-2026-09-08 / gpt-image-2.5-sunburst-2026-09-08
⚡ Flare is faster: OpenAI says Flare delivers higher image quality than gpt-image-2 at up to 50% lower latency; early customers measured 2–4× the speed of gpt-image-2
🎯 Sunburst is sharper: OpenAI’s most capable image generation and editing model, better at preserving earlier edits and reference subjects across turns, with longer generation times than Flare
🎚️ Six quality tiers: quality gains xhigh / max on top of low / medium / high / auto; gpt-image-2 stops at high
💰 Same price: $5 text input, $8 image input, $30 image output per 1M tokens, item for item the same as gpt-image-2
🔌 Zero-change integration: APIYI’s official relay is live; swap the model name and you are done, in the Default / image2Enterprise / svip groups

Background

On September 8, 2026, OpenAI released ChatGPT Images 2.5. The official pitch is “sharper details, more precise editing, faster generation”: more natural lighting, richer textures, better handling of complex layouts and transparent backgrounds, stronger preservation of people and objects from reference images, and better memory of earlier changes across multi-turn edits. OpenAI also disclosed that ChatGPT Images and the GPT-Image API models together now produce more than 3 billion images a week. Unlike April’s gpt-image-2, which shipped as a single model, this generation is split in two on the API side. Flare is the small model, optimized for speed, and OpenAI positions it as “the default choice for most applications”. Sunburst is the base model, optimized for quality, built for premium visual workflows that need detailed editing. ChatGPT also gained Sketch (hand-drawn references) and image comments for localized edits, but those are ChatGPT product features and are not part of the API. APIYI completed the official-relay integration the day after release. Both models share the images API, the same parameter set and the same price list as gpt-image-2, so existing gpt-image-2 code only needs a model-name change.

Deep Dive

How the two models divide the work

gpt-image-2.5-flare · speed-first

The small model, optimized for speed. OpenAI says it beats gpt-image-2 on quality at up to 50% lower latency; early customer evaluations put it at 2–4× the speed of gpt-image-2. Suited to creator and social content, product imagery, visual search, rapid prototyping and high-volume generation.

gpt-image-2.5-sunburst · precision-first

The base model, optimized for quality, which OpenAI calls its “most capable model for image generation and editing”. Editing instructions are followed more precisely and multi-turn edits are more stable, suited to production-ready campaign creative and polished product imagery. Generation takes longer than Flare.

Model selection can stay simple: default to Flare, and switch to Sunburst only when editing precision falls short or you need production-grade large images. The two models share identical parameters, so switching is a one-field change and a single pipeline can pick per task.

Differences from gpt-image-2

Item	gpt-image-2	gpt-image-2.5-flare	gpt-image-2.5-sunburst
Positioning	Single flagship	Speed-first small model	Quality-first base model
Quality vs gpt-image-2	Baseline	Higher	Higher (highest of the two)
Latency vs gpt-image-2	Baseline	Up to 50% lower	Longer than Flare
`quality` tiers	`low` / `medium` / `high` / `auto`	Six, adds `xhigh` / `max`	Six, adds `xhigh` / `max`
Multi-turn edit consistency	—	Improved	Improved further
Price per 1M tokens	$5 / $8 / $30	Same	Same
Current snapshot	`gpt-image-2-2026-04-21`	`gpt-image-2.5-flare-2026-09-08`	`gpt-image-2.5-sunburst-2026-09-08`

Technical specs

Size rules carry over from gpt-image-2: presets 1024x1024 / 1536x1024 / 1024x1536; custom sizes must use multiples of 16, keep the aspect ratio between 1:3 and 3:1, cap the longest edge at 3840 pixels and stay between 0.65MP and 8.3MP total. Resolutions above 2560×1440 are still marked experimental. Output formats are png (default) / jpeg / webp, with output_compression available for jpeg / webp; transparent backgrounds need background: "transparent" with png or webp. Streaming via stream: true + partial_images (0–3) works as before. The official model pages list v1/images/generations and v1/images/edits as supported endpoints, with mask inpainting supported.

xhigh / max are new quality tiers with no official per-image price by size. APIYI measured the 2.5 models at 1024×1024 on 2026-09-09: low 196 / medium 439 / high 1,756 / xhigh 3,122 / max 7,024 output tokens, so max is what corresponds to gpt-image-2 high (7,024) and the same high yields a quarter of the tokens on 2.5. Do not carry quality over verbatim from gpt-image-2; check usage.output_tokens once first.

Practical Use

Code samples

Change the model in your existing gpt-image-2 code and leave everything else as is:

from openai import OpenAI
import base64

client = OpenAI(
    api_key="your-apiyi-api-key",
    base_url="https://api.apiyi.com/v1"
)

# Everyday batch generation: Flare
resp = client.images.generate(
    model="gpt-image-2.5-flare",
    prompt="A Kyoto alley on a late-autumn morning, cobblestones in thin mist, cinematic framing",
    size="1536x1024",
    quality="high",
    output_format="jpeg",
    output_compression=85
)
with open("flare.jpg", "wb") as f:
    f.write(base64.b64decode(resp.data[0].b64_json))

# Precision editing: Sunburst
resp = client.images.edit(
    model="gpt-image-2.5-sunburst",
    image=[open("product.png", "rb"), open("scene.png", "rb")],
    prompt="Place the product from image 1 on the desk in image 2, keep the label text, match the lighting of image 2",
    size="1024x1024",
    quality="xhigh"
)
with open("sunburst.png", "wb") as f:
    f.write(base64.b64decode(resp.data[0].b64_json))

Best practices

Production recommendations:

Default to gpt-image-2.5-flare; switch to gpt-image-2.5-sunburst for editing tasks or production-grade large images
Pin the dated snapshots gpt-image-2.5-flare-2026-09-08 / gpt-image-2.5-sunburst-2026-09-08 in production so a future alias change does not move you
Get quality="high" working first, then evaluate xhigh / max; budget the two new tiers from measured usage
Sunburst takes longer, so start client timeouts at the 360-second value proven on gpt-image-2
As with gpt-image-2, do not send input_fidelity; edits run at high fidelity by default

Pricing and Availability

Pricing

APIYI’s official relay prices both models at OpenAI’s official rates, identical item for item to gpt-image-2:

Item	Price (per 1M tokens)
Text input	$5.00 (cached $1.25)
Image input	$8.00 (cached $2.00)
Image output	$30.00

Because billing is per token, the low / medium / high per-image reference prices at 1K sizes match gpt-image-2 (about $0.006 / $0.053 / $0.211 at 1024×1024). The full cost table is in the gpt-image-2 official-relay guide. xhigh / max and 2K / 4K have no fixed per-image price; go by usage.output_tokens in each response.

Groups and the reverse-engineered sibling

Model	Route	Groups	Billing
`gpt-image-2.5-flare`	Official relay	`Default` / `image2Enterprise` (1.2x) / `svip`	Per token
`gpt-image-2.5-sunburst`	Official relay	`Default` / `image2Enterprise` (1.2x) / `svip`	Per token
`gpt-image-2.5-all`	Reverse-engineered (ChatGPT web app)	`Default`	$0.03 per image

The Default group works out of the box; production workloads that need higher stability can use the image2Enterprise group, which has steadier supply. Model names and endpoints are unchanged across groups. On the reverse-engineered side, gpt-image-2.5-all is sourced from the ChatGPT web app and priced the same as gpt-image-2-all; see the gpt-image-2-all guide.

Stack recharge promotions

Official-relay pricing matches OpenAI’s list; the discount comes from recharge bonuses, which bring the effective cost below direct OpenAI billing. See Recharge promotions.

Summary and Recommendations

The real change in GPT-image-2.5 is not “bigger” but split by scenario: Flare covers most everyday generation at lower latency, Sunburst pushes editing precision and the quality ceiling one notch higher, and both keep gpt-image-2 pricing. For teams already on gpt-image-2, this is close to a free upgrade.

✅ Batch generation, social content, product images: swap gpt-image-2 for gpt-image-2.5-flare, faster with no quality loss
✅ Multi-turn retouching, production-grade campaign creative: use gpt-image-2.5-sunburst for more precise edits and better subject preservation
✅ Budget-sensitive, size-agnostic: gpt-image-2.5-all at $0.03 per image is a good fit for prototyping
⚠️ Measure the new quality tiers first: xhigh / max have no official per-image price; verify with usage before production

Sources and dates:

OpenAI announcement: openai.com/index/introducing-chatgpt-images-2-5
OpenAI model pages: developers.openai.com/api/docs/models/gpt-image-2.5-flare, developers.openai.com/api/docs/models/gpt-image-2.5-sunburst
OpenAI image generation guide: developers.openai.com/api/docs/guides/image-generation
Data retrieved: September 9, 2026

Changelog

AI Radar

GPT-image-2.5 Launches: Flare Is Faster, Sunburst Is Sharper

Key Highlights

Background

Deep Dive

How the two models divide the work

gpt-image-2.5-flare · speed-first

gpt-image-2.5-sunburst · precision-first

Differences from gpt-image-2

Technical specs

Practical Use

Code samples

Best practices

Pricing and Availability

Pricing

Groups and the reverse-engineered sibling

Stack recharge promotions

Summary and Recommendations

​Key Highlights

​Background

​Deep Dive

​How the two models divide the work

gpt-image-2.5-flare · speed-first

gpt-image-2.5-sunburst · precision-first

​Differences from gpt-image-2

​Technical specs

​Practical Use

​Code samples

​Best practices

​Pricing and Availability

​Pricing

​Groups and the reverse-engineered sibling

​Stack recharge promotions

​Summary and Recommendations

Key Highlights

Background

Deep Dive

How the two models divide the work

Differences from gpt-image-2

Technical specs

Practical Use

Code samples

Best practices

Pricing and Availability

Pricing

Groups and the reverse-engineered sibling

Stack recharge promotions

Summary and Recommendations