GPT Image 2.6 vs Nano Banana 2.1: Image Quality and Cost

Nano Banana 2.1 is the lower-cost starting point for routine images on official output estimates: $0.0504 for a 2K image against $0.10704 for either GPT Image 2.6 model at quality high. It drew the requested single spoon and kept one poster to four lines. Try Sunburst for warm glass; Flare had the lowest median time in this run but returned one product at 1254 px.

• All six posters spelled every word, price and date correctly; five still used five lines instead of four.
• Nano Banana 2.1 drew one spoon; Sunburst and Flare drew two each.
• Official 2048 px output estimates: $0.10704 for Sunburst or Flare, $0.0504 for Nano Banana 2.1.

GPT Image 2.6 ships as two models: Sunburst for detailed visual work and Flare for fast iteration.

How we tested

We ran 15 new still images on November 8, 2026 through OmniaKey's media API: the poster twice per model, then one object-count photo, one product shot and one edit per model. One fresh GPT Image 2 photo, made by a fourth model, served as the shared edit input and sits outside the comparison.

Requests asked for a 1:1 square at 2048×2048: GPT with size=2048x2048 and quality=high, Nano Banana 2.1 with image_size=2K. Fourteen of the 15 candidates came back 2048 square; the Flare product photo came back 1254 square. GPT files are PNG, as is Nano Banana 2.1's first poster, while its other four are JPEG. All three edits saw the same 1024 square JPEG.

Times run from the submit POST to the first finished poll on a 4-second interval, including upload, queue and network but not the download. Calls ran one at a time with the model order rotated, no seed, and no rerun to replace a weak image. The poster pair shows how far one prompt can move between runs; the other tasks have one result per model, so read them as examples and test your own prompts before changing a pipeline.

OmniaKey sells access to all three models; every price below is the provider's published rate, not ours.

Poster text: every word right, five layouts wrong

All six posters spelled REPAIR WEEKEND, the tagline, the date and the price correctly; five of the six still ran to five lines because the title split.

Prompt:

Create a flat square graphic poster, full bleed, straight-on, with no frame or mockup. Cream background, dark navy type, one small orange geometric wrench illustration below the text. Use a clear typographic hierarchy. Render exactly these four lines and no other text:

REPAIR WEEKEND FIX IT. KEEP IT. SAT 14 NOV / 10 AM ENTRY $5

Keep every character readable and spell every word exactly. No people, brand names, signatures or watermark.

• Sunburst (23.6 s, 28.6 s): every character correct. REPAIR WEEKEND breaks over two lines, so the poster carries five lines, and the wrench reads large rather than small.
• Flare (24.6 s, 23.7 s): the same clean spelling and five-line layout, navy type on cream, the date reversed out of a navy bar, a large orange wrench.
• Nano Banana 2.1 (35.4 s, 23.8 s): round 1 fit the title on one line, four lines in all, with a small double-ended wrench and more white space; round 2 split it too.

Verdict: Spelling is a three-way tie across these six posters. Nano Banana 2.1 alone kept four lines, once in two tries, and both GPT models split the title in both rounds; check the line count yourself for print.

Counting objects: one spoon against two

Nano Banana 2.1 drew one spoon while Sunburst and Flare drew two each, and all three placed four cups and three saucers.

Prompt:

Photorealistic overhead studio photograph of exactly seven ceramic pieces on a pale gray tabletop. Arrange four cobalt-blue espresso cups in one straight row across the upper half. Arrange three ivory saucers in one straight row below the cups. Leave clear gaps between all seven pieces; no cup sits on a saucer. Put one small brass teaspoon vertically at the far right of both rows, with its bowl pointing up. Nothing else in the scene. Soft shadows, no text, no logos.

• Sunburst (29.3 s): four blue cups and three ivory saucers in the right rows, but two brass teaspoons at the far right instead of one. The cups sit at an oblique angle, not straight overhead.
• Flare (34.9 s): the same four cups, three saucers and two teaspoons. Its cups read taller and rounder, and the camera is again off centre.
• Nano Banana 2.1 (30.4 s): four cups, three saucers and one spoon beside the rows, bowl up. The cup fronts show almost no opening, so the overhead look is not convincing here either.

Verdict: Nano Banana 2.1 on the count, one spoon against two. All three missed the straight-overhead viewpoint, so check the camera angle in your own scene.

Product photo: credible glass, three moods, one smaller file

All three returned a plausible bottle, pump, linen cloth and stone; Flare's file arrived at 1254 px for a 2048 px request.

Prompt:

Square luxury product photograph of one unbranded frosted amber-glass soap dispenser with a brushed stainless-steel pump, standing on a pale travertine slab. A folded ivory linen cloth sits behind it to the left. Morning window light from the left creates a soft bottle shadow to the right. Show subtle glass translucency, realistic pump geometry and the porous stone texture. Camera at bottle height, three-quarter view. Neutral warm wall, generous empty space above. No label, lettering, logo, watermark or extra bottles.

• Sunburst (46.4 s): a wide amber bottle with the most vivid light through the glass near the base, an open steel nozzle, clear linen folds and visible stone pores. The window pattern on the left is stronger and the space above is tighter.
• Flare (27.5 s): a narrower bottle, coherent pump and stone, more room above than Sunburst. It returned 1254×1254 for the 2048×2048 request, so its detail is softer than the other two files.
• Nano Banana 2.1 (31.8 s): a slender frosted-amber bottle with a readable brushed-steel pump, fine stone pores, darker amber, softer contrast and generous space above.

Verdict: All three satisfy the material and scene instructions. Sunburst is our pick for a warm hero image; Nano Banana 2.1 gives a quieter catalog look at the full 2048 square. Check the returned width and height, and ask for another file when 2048 is mandatory.

Photo edit: composition held, fabric and colour drifted

All three kept the chair, its four legs and the whole room; none returned a pixel-identical recolour, and the fabric changed in every case.

Prompt:

Change only the armchair upholstery from mustard yellow to deep cobalt blue. Keep the chair shape, fabric texture, walnut legs, camera position, room lighting, shadows, floor, white lamp, oak table, green mug and framed print exactly as in the reference image. Do not add or remove anything.

• Sunburst (38.9 s): the brightest cobalt and a clean chair contour, with the room close to the source. The fine, fuzzy upholstery becomes coarser and wavier.
• Flare (34.0 s): slightly darker cobalt, the same room and four legs, and a coarser weave too. Fastest of the three edits.
• Nano Banana 2.1 (40.9 s): a muted grey-blue rather than vivid cobalt, a finer and more even dotted weave, with some lighting and fabric texture redrawn.

Verdict: Composition is a tie: all three kept the chair, its four visible legs, the lamp, the oak table and mug, the red print, the window and the plant. Sunburst came closest to the saturated cobalt we asked for, Flare was fastest, Nano Banana 2.1 gave the quieter blue.

Cost per image and speed

Both vendors bill image output at $30 per 1M tokens, so the bill follows tokens per image. OpenAI's cost calculator, used on November 8, 2026, gives GPT Image 2.6 3,568 output tokens for a 2048×2048 image at quality high: 3,568 × $30 ÷ 1,000,000 = $0.10704. Google's published token counts give 1,680 tokens for a 2K image, which is $0.0504.

These are output-only estimates. Input and thinking tokens come on top: GPT text input costs $5 per 1M tokens and image input $8; Nano Banana 2.1 charges $1.50 per 1M input tokens for text, image or video and $7.50 per 1M for text and thinking output.

Nine of the ten GPT outputs reported zero usage, which means the counts never arrived; zero is not a free image. Where counts arrived, the Flare product photo reported 105 text input and 2,058 image output tokens, $0.062265 at OpenAI's rates, on a 1254 square file, while Nano Banana 2.1's output counts moved from 1,276 to 2,013. Those numbers cover other sizes, so the table uses published per-image figures.

Median time over the five outputs per model was 29.3 s for Sunburst (23.6–46.4 s), 27.5 s for Flare (23.7–34.9 s) and 31.8 s for Nano Banana 2.1 (23.8–40.9 s). A few seconds apart is not enough to call a speed ranking.

Independent results and what changed in 2.6

Artificial Analysis collects blind pairwise human preference votes and fits them to Elo scores; the image methodology explains how. On November 8, 2026 its text-to-image leaderboard (v2.1) showed GPT Image 2.6 Sunburst at max quality on 1259 (±15), Flare at 1228 (±12) and Nano Banana 2.1 on 1176 (±12). Its editing leaderboard (v2.1) showed 1209 (±14), 1206 (±13) and 1152 (±13). Those GPT runs use max quality, while ours used high.

The Sunburst and Flare editing intervals overlap, so that board shows no clear editing winner. Higher Elo means more preferred in blind votes, not a percentage of quality.

Both GPT models arrived on November 4, 2026 with adjustable complexity, SVG output and animated GIF output, none used by our still-image tasks; the GPT Image 2.6 review covers them. Nano Banana 2.1 reached general availability on October 6 as an update to Nano Banana 2, and Google lists gemini-3.1-flash-image as deprecated, with no shutdown date announced. For 2.5 against Nano Banana 2.1, see GPT Image 2.5 vs Nano Banana 2.1.

Which model for which job

• Routine catalog images at volume: Nano Banana 2.1. About half the official 2K output estimate, a full 2048 file in all five runs, and the cleanest object count.
• Warm, lit product heroes: Sunburst. The best glass translucency in our product scene, at the same output estimate as Flare.
• Fast drafts: Flare. The lowest median time here, 27.5 s, with the caveat that one product file came back at 1254 px.
• Posters with a fixed line count: any of the three, then check the layout. Five of six results split the title.

One request, three model IDs

Same model, settings and prompt as the round 1 Sunburst poster; the Idempotency-Key is illustrative. The API returns a task id; poll until succeeded, then fetch the asset.

curl https://api.omniakey.com/v1/media/tasks \
  -H "Authorization: Bearer $OMNIAKEY_API_KEY" \
  -H "Content-Type: application/json" \
  -H "Idempotency-Key: repair-weekend-poster" \
  -d '{
    "model": "gpt-image-2.6-sunburst",
    "type": "image",
    "operation": "generate",
    "input": {
      "prompt": "Create a flat square graphic poster, full bleed, straight-on, with no frame or mockup. Cream background, dark navy type, one small orange geometric wrench illustration below the text. Use a clear typographic hierarchy. Render exactly these four lines and no other text:\nREPAIR WEEKEND\nFIX IT. KEEP IT.\nSAT 14 NOV / 10 AM\nENTRY $5\nKeep every character readable and spell every word exactly. No people, brand names, signatures or watermark."
    },
    "parameters": {"size": "2048x2048", "quality": "high"}
  }'

The reply is a queued task with an id. Poll GET https://api.omniakey.com/v1/media/tasks/{id} every few seconds; when status is succeeded, download assets[0].url. For Flare, change model to gpt-image-2.6-flare; for Nano Banana 2.1, change it to gemini-nano-banana-2.1 and the parameters to {"aspect_ratio": "1:1", "image_size": "2K"}. For an edit, set operation to edit and send the photo as input.image in the shape {"data": "<base64>", "mime_type": "image/jpeg"}. Use a new Idempotency-Key for each image.

To repeat this without code, open the AI Image Generator. The Sunburst, Flare and Nano Banana 2.1 model pages list current prices.

Frequently asked questions

Is GPT Image 2.6 better than Nano Banana 2.1?

Neither wins overall across our four tasks. Sunburst gave the warmest product light and the most vivid cobalt in the edit; Nano Banana 2.1 cost less, drew one spoon instead of two and kept one poster to four lines.

Is Nano Banana 2.1 cheaper than GPT Image 2.6?

On official output estimates at 2048 square, yes: $0.0504 against $0.10704, so 100 images come to about $5.04 against $10.70. Input and thinking tokens are extra on both.

Why did one Flare image come back at 1254 px?

We asked for 2048×2048 and got 1254×1254 on the product photo, while its other four outputs were 2048 square. Check the returned size before publishing; the $0.10704 estimate assumes a full 2048 square.

Do the edits keep the original pixels?

No. All three kept the room layout and the chair, but repainted the fabric and shifted light and colour, and Sunburst's blue came closest to the cobalt we asked for. Check colour and texture at full size before using an edit as a catalog retouch.

Sources checked

• OpenAI API changelog
• GPT Image 2.6 Sunburst model page
• GPT Image 2.6 Flare model page
• OpenAI image generation guide
• OpenAI API pricing
• Gemini image generation guide
• Gemini Developer API pricing
• Gemini API changelog
• Artificial Analysis text-to-image leaderboard
• Artificial Analysis image editing leaderboard
• Artificial Analysis image methodology

Updated November 8, 2026. We will rerun the poster and product tests if either provider changes its defaults or prices.