image model comparison
FLUX1.1 [pro] vs GPT Image 2.5 Flare
FLUX1.1 [pro] and GPT Image 2.5 Flare are both current image models, from Black Forest Labs and OpenAI. On Arena Text-to-Image, FLUX1.1 [pro] scores 1016 Elo and GPT Image 2.5 Flare 1398 Elo (Arena, measured October 7, 2026).
Data verified October 12, 2026 · Dataset 2026-10-12
Side by side
Standard-tier prices in effect today. The better value in each row is highlighted.
| Fact | FLUX1.1 [pro] | GPT Image 2.5 Flare |
|---|---|---|
| Price per image | $0.04 | Not reported |
| Price per 1M input tokens | Not reported | $5 |
| Arena Text-to-Image | 1016 Elo (#9 of 9) | 1398 Elo (#2 of 9) (better) |
| Arena Image Edit | Not measured | 1481 Elo (#2 of 8) |
| Provider | Black Forest Labs | OpenAI |
| Release date | Not reported | September 8, 2026 |
| Access | API only | API only |
| Context window | Not reported | Not reported |
| Max output | Not reported | Not reported |
| Input | text | text, image |
| Output | image | image |
Scores:Arena · 2026-10-12
When to choose each
FLUX1.1 [pro]
Best for
- Hosted FLUX text-to-image generation
Watch out for
- No exact compatible public benchmark snapshot is recorded
GPT Image 2.5 Flare
Best for
- Fast, high-volume everyday image generation
Watch out for
- OpenAI publishes no per-image price, and its GPT Image 2 calculator does not estimate 2.5 token use, so cost per image has to be measured
- No independent image-quality benchmark is recorded here
FLUX1.1 [pro] vs GPT Image 2.5 Flare FAQ
Which scores higher on independent benchmarks, FLUX1.1 [pro] or GPT Image 2.5 Flare?
GPT Image 2.5 Flare leads on Arena Text-to-Image, 1398 Elo to 1016 Elo. Scores are independent evaluations, not either vendor's launch figures.
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- Gemini 3 Pro Image vs GPT Image 2.5 Flare
- GPT Image 2.5 Flare vs GPT Image 2.5 Sunburst
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Production evaluation
Choosing between FLUX1.1 [pro] and GPT Image 2.5 Flare?
Public benchmarks narrow the shortlist. We run both on your own tasks, data and cost limits, then build the routing and review controls to run the winner in production.
- A head-to-head evaluation on your real workload
- Routing between models by cost and difficulty
