Muse Image vs GPT Image 2.5: Text, Charts and Layout Test

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Quick answer: Muse Image in Fuser spelled all 501 requested characters across our poster, chart and seed-packet tests, the same as GPT Image 2.5, at about a quarter of GPT Image's cost at High quality. GPT Image 2.5 led where accuracy goes past spelling: its chart bars matched the numbers, its three-reference composite sat more naturally in the scene, and its one-change edit left more of the photo untouched. Muse kept every punctuation mark as typed in these tests and followed the brief literally. Both run as nodes on the same Fuser canvas, among 133 image models, so one prompt can feed each.

How we ran it

• Models: Muse Image from Meta Superintelligence Labs, and GPT Image 2.5 Flare, the default model in Fuser's GPT Image node, which OpenAI describes as "fast, high-quality everyday image generation" (OpenAI). In Fuser the GPT Image node calls OpenAI directly; for this test we generated both with the same model versions Fuser uses.

• Settings: Muse has no quality or resolution setting and renders about 2.5 megapixels: 1280 × 1920 at 2:3 and 1920 × 1280 at 3:2, as PNG. GPT Image 2.5 ran at High quality at the node's 1024 × 1536 and 1536 × 1024 sizes, and at Auto size for the edit. We also ran GPT Image at Medium, where it costs about the same as Muse, on the chart and the seed packet.

• Inputs: the reference photos and the photo we edited come from our own stock library, made by neither model. Prompts and inputs were identical for both models.

• One run per model per task, no retries, on 23 October 2026.

• Judging: we read every output at full resolution and checked every requested character (spaces excluded). We measured each chart's bars in pixels against its own gridlines, and compared each edit with the source pixel by pixel.

Test 1: a poster with several type sizes

Prompt: "A Swiss-style screen-printed poster for a riverside open-air swimming pool, flat two-colour print in turquoise and black on off-white paper, with one bold geometric illustration of a diving board over a turquoise pool seen from above. Text, exactly as written, each line once: a huge headline "LIDO ALDER" across the top; under it, in a medium weight, "Open-air pool · Season 2027"; at the bottom left, three lines of small text: "Opens Saturday 29 May", "Daily 07:00–20:00", "Lane swimming before 09:00"; at the bottom right, tiny: "Riverside Road, Alder Mill". No other text."

• Muse Image got 112 of 112 characters right, with the middle dot and the en dash exactly as typed and a clear hierarchy from headline to footer. The artwork is the weak part: a stretched pool tank with the board at the top end, ringed by loose black shards that read as noise.

• GPT Image 2.5 also got 112 of 112, and set the middle dot as a larger bullet. Its diving board over rippling turquoise water is a clean, flat screen-print image that holds the poster together.

Verdict: a tie on text, GPT Image 2.5 on the artwork.

Test 2: a chart with real numbers

Prompt: "A clean editorial bar chart printed as a two-colour risograph on cream paper in ink blue and fluorescent pink, photographed flat and square-on. Title at the top left: "Swifts counted over the mill pond, 2026". Six vertical bars, one per month, labelled underneath "APR", "MAY", "JUN", "JUL", "AUG", "SEP", with these exact values printed above the bars in order: 12, 48, 131, 96, 27, 3. Bar heights are drawn to scale against a vertical axis marked 0, 50, 100 and 150, with faint horizontal gridlines. The tallest bar, JUN, is pink; the others are blue. A small note at the bottom right: "Source: evening counts, 7 surveys a month". No other text."

• Muse Image spelled all 107 characters, from the title to the source note, and coloured June pink. The bars are not to scale. Its gridlines for 0, 50 and 100 are evenly spaced, but the step from 100 to 150 is twice as tall, with an unlabelled line where 150 should be. Read against that axis, the bars come to about 17, 74, 143, 110, 37 and 5, not 12, 48, 131, 96, 27 and 3. July's 96 bar rises above the 100 line.

• GPT Image 2.5 spelled all 107 characters on an evenly spaced axis, and every bar measured within about one unit of its value (12.9, 47.7, 130.3, 97.1, 27.1 and 4.1). At Medium quality it still drew the chart to scale, within about three units.

Verdict: GPT Image 2.5. Muse's labels read; its bars don't. When we drew the chart exactly in code and asked Muse to restyle it, it kept every bar's height. That workflow is in the Muse Image prompt guide.

Test 3: three references, one scene

Inputs: a moulded plywood armchair, a speckled stoneware vase and a foggy dune landscape, connected in that order. Prompt: "Image 1 is a moulded plywood armchair, Image 2 is a speckled stoneware bottle vase, Image 3 is a foggy landscape of pale sand dunes. Place the armchair from Image 1 in the dunes from Image 3, its legs sunk a little into the sand and turned at a slight angle, with the vase from Image 2 standing on its seat. Keep the chair's honey-toned plywood, curved arms and slender legs, and the vase's oatmeal glaze, dark speckles and blue-grey brushstrokes exactly as in their images. Match the soft, flat fog light of Image 3: no hard shadows, the chair and vase slightly softened by the haze, the dry scrub from Image 3 in the foreground. A wide, quiet 35mm documentary photograph."

• Muse Image placed all three: the honey plywood chair with its bent arms, the vase on the seat with its speckles and blue-grey strokes, and the dunes and scrub. But each chair foot sits in a cracked, crusty clump of sand that looks like dried plaster, the leg tips are scuffed white, and the chair is sharper than the haze around it. The vase's neck came out shorter than the reference.

• GPT Image 2.5 placed all three with the legs sinking into soft sand, the vase's shape and glaze close to the reference, and the whole frame in the same flat fog light.

Verdict: GPT Image 2.5, narrowly. Both followed every role we gave the three images.

Test 4: change one thing

Input: a photo of three young musicians in cream knit vests on a disused tennis court. Prompt: "Change only one thing: the man standing in the middle now wears a deep tomato-red cable-knit sweater vest instead of his cream one, with the same cable pattern, V-neck, fit and folds. Keep everything else exactly as it is: all three men, their faces, hair, expressions, poses, T-shirts, trousers and shoes, the other two men's cream vests, the keyboard, the snare drum, the open case and cables, the chain-link fence, the trees, the cracked green court, the light and the framing."

• Muse Image changed only the middle vest, to a brick red with the cables kept, and held all three faces, the poses, the kit and the court. It returned its standard 1920 × 1280, a slightly different shape from the 2528 × 1696 source.

• GPT Image 2.5 changed only the middle vest, to a deeper red, held the faces and the scene, and returned the source's own size.

Both redrew the whole photo rather than pasting in a new vest. Outside the vest, 12% of Muse's pixels moved by more than a 10% tolerance and 3.7% by more than 20%; GPT Image's figures were 8% and 1.5%. Neither difference shows at a glance.

Verdict: GPT Image 2.5, narrowly. Both kept to the one change. For text corrections on approved artwork, a precise edit model leaves even more alone; see Ideogram 4.5 vs GPT Image 2.5.

Test 5: small print on packaging

Prompt: "The back of a paper seed packet, flat print design on kraft paper in black and dark green ink, shown flat and square-on, filling the frame. Exact text, each line once, as written: at the top, "SWEET PEA" in bold capitals, and under it "Lathyrus odoratus · Painted Lady" in italic. A heading "SOWING" followed by three small lines: "Sow indoors Feb–Mar, or outdoors Apr–May.", "Soak seeds overnight. Sow 2 cm deep, 8 cm apart.", "Pinch out the tips at 10 cm tall." A heading "GROWING" followed by two small lines: "Give climbing support from day one.", "Pick flowers often to keep them coming." A row of three small facts: "Height 180 cm", "Flowers Jun–Sep", "Fragrant". At the bottom, tiny: "Approx. 30 seeds · Lot 0412 · Packed for 2027". A small line drawing of a sweet pea flower in the top right corner. No other text."

• Muse Image got 282 of 282 characters right, middle dots and en dashes included. It used both inks, black for the body and dark green for the title and facts, set the text left-aligned and split each fact over two lines. The "small" sowing notes came out at a generous reading size.

• GPT Image 2.5 also got 282 of 282, centred everything and added thin rules between the facts. It printed every line in dark green, with no black ink. At Medium quality it again got all 282 right.

Verdict: a tie. Neither made a spelling error; Muse followed the two-ink brief, GPT Image gave the more finished layout.

What each model does

• Muse Image: generates from text or edits and combines up to ten images, with layout planning that Meta says happens before rendering (Meta). In our tests it spelled every requested character and kept punctuation as typed, at about a quarter of the cost of GPT Image 2.5 at High. It renders about 2.5 megapixels at every ratio, with no quality setting and no seed.

• GPT Image 2.5: OpenAI's image model, with Flare (the default) and Sunburst in the same Fuser node, quality settings from low to max and sizes up to 3840 × 2160. Catch: at High it costs about four times as much as Muse at the sizes we used, and its standard sizes are about 1.5 megapixels. At Medium the cost is close to Muse's.

Which to use

• Text-heavy layouts at volume (posters, labels, packaging): Muse Image. It matched GPT Image 2.5 character for character at a fraction of the cost, and followed details such as inks and punctuation literally.

• Charts: let Muse style a chart, not compute it. Draw the data exactly, then connect it as an input; GPT Image 2.5 drew to scale from text in our test.

• Composites and one-change edits: both kept every instruction. GPT Image 2.5's results sat more naturally and moved fewer pixels; check Muse's contact points and edges at full size.

• QR codes: in earlier runs, Muse did not produce a scannable QR code from text, but copied a supplied one exactly. The Muse Image prompt guide shows how.

To run your own brief through both, wire one Text node into a Muse Image node and a GPT Image node and compare the outputs side by side on the canvas. For OpenAI's model on its own, see the GPT Image prompt guide; for more models on lettering, see the best AI models for text in images.