D/Discover AI Now
Benchmark protocol · September 2026

Same image job. Same constraints. Show the misses.

This page fixes the test before the outputs exist. We will not publish a winner, numeric score or claimed benchmark result until the controlled runs are completed and the evidence is shown.

Status: protocol publishedResults are intentionally blank. Tool versions, dates, prompts, outputs and revision attempts will be documented when the experiment is run.

Tools in the first run

ChatGPT Image, Midjourney, Ideogram, Adobe Firefly and FLUX are the planned comparison set. If access or product capabilities differ at run time, that limitation will be recorded rather than silently changing the test.

Five controlled tasks

01Product advertisement

Premium bottle on a controlled surface with defined lighting, negative space and no unrequested objects.

02Text-heavy poster

Exact headline and short supporting copy with hierarchy, spelling and layout constraints.

03Photorealistic scene

Natural materials, believable geometry, lighting and perspective under a fixed composition brief.

04Reference fidelity

Preserve defining identity, geometry and requested details from the same supplied reference.

05Controlled revision

Change one requested element while preserving already-approved composition and details.

What we will measure

DimensionEvidence we inspect
Instruction adherenceRequested objects, omissions, composition and explicit constraints
Text handlingExact wording, spelling, legibility and unintended text
Visual coherenceGeometry, anatomy where relevant, materials, perspective, lighting and artifacts
Reference fidelityWhether defining identity and geometry survive generation
Revision controlWhether the requested change occurs without unnecessary collateral changes
Cleanup burdenVisible defects or corrections needed before practical use

Run rules

  1. The task requirement is frozen before comparing tools.
  2. The same semantic requirement is used for every tool; syntax-only adaptation is documented.
  3. Default/current production model settings are recorded at test time.
  4. The number of first-pass generations and revision attempts is kept equal wherever product capabilities permit.
  5. No output is quietly regenerated until a preferred example appears.
  6. Every published conclusion links back to visible output evidence and known limitations.

Results

Not run yetNo benchmark winner has been selected.

This is deliberate. The page establishes the methodology first so later recommendations cannot be reverse-engineered around a preferred tool.

Prompt Lab is not this benchmark.Prompt Lab currently uses a transparent editorial fit model. Its qualitative labels should not be read as measured benchmark results.