GPT-Image-2
- Context
- Not published
- Max output
- Not published
- Input / MTok
- See native rates
- Cached / MTok
- —
- Output / MTok
- See native rates
- Input types
- text + image
IMAGE GENERATION · BUDGET VS PREMIUM / VERIFIED 2026-10-12
GPT-Image-2
vs GPT-Image-2.5 Flare
Compare GPT-Image-2 vs GPT-Image-2.5 Flare using official sources, cost units, supported workflows and provider access, with no fabricated benchmark winner.
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.
Official IMAGE-token pricing
GPT-Image-2 Standard image tokens are $4 input, $1 cached and $15 output per million. Flare costs $8 input, $2 cached and $30 output. Both separately charge prompt TEXT tokens; equal images may consume different tokens.
Choose by production needs
GPT-Image-2 emphasizes accessible image generation and editing. Flare emphasizes fast high-quality image output within the 2.5 line.
Measure effective per-image cost
Image resolution, number of references, input text and quality settings change the final bill; multiplying token rates alone is insufficient.
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, 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 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 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.