GPT-Image-2.5 Sunburst
- Context
- Not published
- Max output
- Not published
- Input / MTok
- See native rates
- Cached / MTok
- —
- Output / MTok
- See native rates
- Input types
- text + image
OPENAI IMAGE GENERATION / VERIFIED 2026-10-12
GPT-Image-2.5 Sunburst
vs GPT-Image-2.5 Flare
Compare Sunburst and Flare on supported image workflow, published modality-specific token prices, editing capability and performance claims without assuming equal output token consumption.
LIVE VERIFIED FACTS
Current facts from the SXF model database.
GPT-Image-2.5 Flare
- Context
- Not published
- Max output
- Not published
- Input / MTok
- See native rates
- Cached / MTok
- —
- Output / MTok
- See native rates
- Input types
- text + image
Facts are generated from the canonical model catalog, not copied into this comparison.Current data ↗Change ledger ↗
SPECIALIST BUYER GUIDE
What matters for visual creation.
Published Image API token rates
Both models publish $8 image input, $2 cached image input and $30 image output per million image tokens in Standard pricing. Prompt text charges are separate ($5 input and $1.25 cached per 1M text tokens).
Different positioning
OpenAI describes Flare as its fastest model for high-quality everyday images. Sunburst targets high-fidelity image generation and precise editing. No independent SXF speed or quality benchmark is claimed.
Compare actual image cost
Equal token rates do not imply identical cost per picture: generated image-token consumption varies with chosen output size, quality and model implementation.
Practical choice
Try Flare for speed-sensitive interactive iteration, and Sunburst for complex high-precision image editing; evaluate samples on the same prompt.
DECISION FACTORS
What materially changes the choice.
Context capacity
Neither provider publishes a comparable token-based context limit for this video-generation service.
Input modalities
Both list image, text as input types.
Direct token cost
These specialist modalities do not have a directly comparable text-token cost. Check model-specific image, video, credit and service-token pricing instead.
HOW TO DECIDE
Specifications narrow the field. Your workload decides.
Context, modalities and direct token economics are comparable from official sources. Coding quality, latency, reliability and agent success should be measured on your own acceptance tests before production routing.
Replay real tasks
Use representative prompts, files, tools and expected outputs from the workload you plan to ship.
Measure task cost
Include retries, cached tokens, long-context rules and tool charges—not only headline input price.
Track failures
Record hallucinations, tool errors, timeout behavior and human corrections alongside pass rate.
Route by task
A portfolio can outperform a one-model policy when different task classes have different cost and capability needs.
INDEPENDENT EVALUATIONS
No directly comparable independent result yet.
SXF does not infer quality from specifications or compare benchmark scores across different evaluator versions/configurations.
WHAT CHANGED
Changes affecting this comparison.
No post-baseline factual changes recorded for GPT-Image-2.5 Sunburst, GPT-Image-2.5 Flare since 2026-10-12.
VERIFIED BASELINE2026-10-12Current facts remain aligned with the SXF ledger.
QUICK ANSWERS
Which has the larger context window: GPT-Image-2.5 Sunburst or GPT-Image-2.5 Flare?
Neither provider publishes a comparable token-based context limit for this video-generation service.
Which is cheaper: GPT-Image-2.5 Sunburst or GPT-Image-2.5 Flare?
These specialist modalities do not have a directly comparable text-token cost. Check model-specific image, video, credit and service-token pricing instead.
Does SXF declare an overall winner?
No. This page compares source-backed specifications and economics. Quality, latency and task success require workload-specific evaluation evidence.