Use image-specific quality criteria. The announcement supports wide and panoramic aspect ratios at 1K, 2K and 4K; output quality and billing still need checks on the real endpoint.
Image API and Responses image-generation tool behavior can differ. Record quality setting, output size, edits and total billing; a text-model token estimate is insufficient.
Use identical briefs, source images and output dimensions. Nano Banana 2.1 became generally available October 6, 2026; GPT Image 2.5 Flare launched September 8. Evaluate image outcomes rather than applying a text reasoning score.
Score the image against a written brief
Define required text, objects, composition and unchanged regions. Reject outputs with misspelled text or altered product details even when they look attractive. Preserve the complete sequence for conversational editing tests.
Separate output options from measured quality
Nano Banana 2.1 announces 1K, 2K and 4K plus panorama ratios. GPT Image 2.5 Flare adds xhigh and max quality options. Match comparable sizes and settings, and include every regeneration in cost.
Three criteria for this comparison
Prompt adherence and legible rendered text
Character consistency across edit turns
Spend and retries per accepted image at matched size
Run a fair comparison
Use the same representative inputs and a written answer rubric. Pin the model version and record the endpoint, thinking configuration, quantization when known, context length, output allowance and concurrency. Preserve native request differences in an adapter rather than silently dropping fields.
Measure full request time, error rate and reported usage. For tools, validate arguments and score the result after execution. For images, preserve resolution and preprocessing. A valid response format alone does not establish a correct answer.
Compare cost per accepted result
Calculate input cost plus all billed output, including reasoning where the provider counts it. Add retries and discarded answers. Divide the total by the number of results that pass your quality criteria. Keep API usage cost separate from deployment and engineering cost.
Artificial Analysis observations are refreshed by the integrated Radar. Differences in artifact, benchmark version or method prevent a score comparison. Read the evidence methodology →
YOUR WORKLOAD
Compare cost per accepted task.
Enter results from your own evaluation. A task should count once it meets your written quality criteria. Include failed attempts and retries in total spend. These inputs stay in this browser and are not a Flonno benchmark.
Nano Banana 2.1
Enter all four values. Attempted tasks must be at least one; accepted tasks cannot exceed attempted tasks.
GPT Image 2.5 Flare
Enter all four values. Attempted tasks must be at least one; accepted tasks cannot exceed attempted tasks.
Turn evidence into a deployment decision
A source benchmark can inform your shortlist, while your evaluation shows whether a model finishes the work at an acceptable cost. Keep the chosen endpoint, processing terms and output limits in the decision record. Inclusion in the Radar does not mean the model is served by Flonno.
