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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

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Side by side

Standard-tier prices in effect today. The better value in each row is highlighted.

FactFLUX1.1 [pro]GPT Image 2.5 Flare
Price per image$0.04Not reported
Price per 1M input tokensNot reported$5
Arena Text-to-Image1016 Elo (#9 of 9)1398 Elo (#2 of 9) (better)
Arena Image EditNot measured1481 Elo (#2 of 8)
ProviderBlack Forest LabsOpenAI
Release dateNot reportedSeptember 8, 2026
AccessAPI onlyAPI only
Context windowNot reportedNot reported
Max outputNot reportedNot reported
Inputtexttext, image
Outputimageimage

Scores:Arena · 2026-10-12

Open both in the interactive comparison

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
Full FLUX1.1 [pro] profile

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
Full GPT Image 2.5 Flare profile

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.

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