# Prompt for AI Image Generator: Nano Banana Wins vs GPT Image 2 > Source: > Published: 2026-09-28 03:44:51+00:00 The best prompt for an AI image generator in 2026 is a plain-English scene description written for Nano Banana Pro, Google's Gemini 3 Pro Image model. It wins for most people because it takes conversational sentences rather than tag soup, renders legible text inside images, and can pull live facts through Grounding with Google Search. GPT Image 2 wins if your work is surgical conversational editing and transparent-background assets, Midjourney wins if aesthetic control is the actual job, and Stable Diffusion with ComfyUI or AUTOMATIC1111 wins if you need local control and negative prompts. `gemini-3-pro-image`). Prompt formula: [Subject] + [Action] + [Location/context] + [Composition] + [Style], per `--stylize` defaults to 100 on a 0 to 1000 scale ( Because the four leading families parse language differently. Nano Banana and GPT Image 2 are multimodal models that read narrative sentences, so they reward descriptive paragraphs with the intent stated explicitly. Midjourney reads a short creative phrase plus flag parameters. Stable Diffusion reads comma-separated tags with weights and a separate negative field. Paste a Midjourney prompt full of `--ar 16:9 --stylize 750` into Gemini and the flags become literal text the model tries to interpret. Paste a paragraph into a tag-trained SD checkpoint and half the nouns get diluted. That split is why generic "ultimate prompt formula" posts underperform. The formula is real, but the dialect is what determines whether you get the frame you pictured. Write one confident sentence, then add composition and style. Google's guide gives the text-to-image formula as [Subject] + [Action] + [Location/context] + [Composition] + [Style], and for reference-image work, [Reference images] + [Relationship instruction] + [New scenario]. A working example: A ceramicist trimming a tall vase on a kick wheel, in a dusty workshop lit by one high window, shot from low three-quarter angle on a Fujifilm camera with a 35mm lens, chiaroscuro lighting, 4:5. Specifics that matter, all documented in [Google Cloud's guide](https://cloud.google.com/blog/products/ai-machine-learning/ultimate-prompting-guide-for-nano-banana): Nano Banana Pro is Gemini 3 Pro Image, with a 65,536-token input window and a 32,768-token output budget, and Nano Banana 2 is Gemini 3.1 Flash Image with a 131,072-token input window ([Google Cloud](https://cloud.google.com/blog/products/ai-machine-learning/ultimate-prompting-guide-for-nano-banana)). Describe the background first, then the subject, then key details, then constraints. OpenAI's [cookbook guide](https://developers.openai.com/cookbook/examples/multimodal/image-gen-models-prompting-guide) recommends that order and also recommends stating the intended use, for example ad creative, UI mock or infographic, because the stated purpose sets the model's rendering mode. Three practical rules from the same guide: `background="transparent"`, but only with PNG or WebP output. JPEG cannot carry an alpha channel. On sizing, OpenAI documents a maximum edge under 3,840px, both edges as multiples of 16, an aspect ratio no wider than 3:1, and a total pixel ceiling of 8,294,400, with 2560x1440 as the recommended upper boundary for reliable results and anything beyond it marked experimental ([OpenAI](https://developers.openai.com/cookbook/examples/multimodal/image-gen-models-prompting-guide)). At the very end, always. [Midjourney's parameter list](https://docs.midjourney.com/hc/en-us/articles/32859204029709-Parameter-List) specifies that parameters follow the prompt text, need a space before the dashes, and must not contain commas or other punctuation inside them. The parameters worth learning first: `--s`) controls how much of Midjourney's house aesthetic gets applied. Default 100, range 0 to 1000; low values stay literal to your words, high values take creative liberties.`--raw` reduces that default aesthetic, which is what you want for product or documentary looks.`--ar` sets aspect ratio, `--no` excludes elements, `--sref` supplies a style reference image.`--draft` generates at half the GPU cost in V7, which makes exploration cheaper before you commit.`--hd`, and the `--edit` model accepts written instructions plus up to four reference images. Yes, but mostly inside the Stable Diffusion family. The [AUTOMATIC1111 web UI](https://github.com/AUTOMATIC1111/stable-diffusion-webui) describes the negative prompt as an extra text field for listing what you do not want to see, and that field remains the cleanest way to suppress artefacts. Weighting with parentheses and comma-separated tags belongs to the same dialect. [ComfyUI](https://docs.comfy.org/) and the [diffusers library](https://huggingface.co/docs/diffusers/index) are the runtimes most people use for this work now. In Nano Banana and GPT Image 2, the equivalent move is a positive constraint. "Empty pavement, no pedestrians" works better than a separate exclusion list, because these models read your sentence as a description of the finished frame. | Job | Pick | Why | |---|---|---| | General use, free entry point, text in images | Nano Banana Pro | Natural-language prompts, Search grounding, up to 14 reference images | | Conversational edits, transparent PNG assets | GPT Image 2 | Single-change iteration, `background="transparent"` in preview | | Art direction and consistent style | Midjourney | `--stylize` ,`--raw` ,`--sref` | | Local, reproducible, offline pipelines | Stable Diffusion | Negative prompt field, full workflow control | One note on why this topic is worth the effort at all. Our own keyword pricing work, a sample of 656 AI and developer-tooling terms priced with DataForSEO on 2026-09-14 (n=656), found that only about one in nine cleared the winnable bar: at least 150 monthly searches, difficulty 20 or below, and a genuine technical term. Image prompting clears that bar with room to spare, one of the few areas where measured demand and answerable specificity overlap. The honest limitation: all four models change monthly, and prompt behaviour shifts with each release. Treat any formula, including the ones above, as a starting frame rather than a fixed recipe, and keep a small set of test prompts you rerun after every model update. If your work extends into motion, the same dialect logic applies to video tools. We compared throughput and quality in [PixVerse vs Kling 3.0](https://dev.to/articles/pixverse-ai-vs-kling-3-0-speed-beats-4k), walked through Google's video stack in the [Flow and Veo 3.1 guide](https://dev.to/articles/google-flow-50-free-credits-veo-3-1-guide-2026), covered agent-driven editing in our [AI video agent editing guide](https://dev.to/articles/ai-video-agent-editing-guide), and put two summarisers head to head in [Magic Light AI vs Pictory](https://dev.to/articles/magic-light-ai-vs-pictory). **Q: What is the best prompt for an AI image generator?** **A:** A single descriptive sentence built from subject, action, location, composition and style, then adapted to your model's dialect. For Nano Banana Pro, write it as prose. For Midjourney, shorten it and append parameters. For Stable Diffusion, convert it to comma-separated tags plus a negative prompt. **Q: What prompt should I use for Nano Banana or Gemini?** **A:** Use Google's documented formula: [Subject] + [Action] + [Location/context] + [Composition] + [Style]. When working from reference images, lead with the references, state the relationship between them, then describe the new scenario. **Q: How do I write prompts for Midjourney parameters?** **A:** Write the creative phrase first and put every parameter at the end, separated by a space before the dashes, with no commas inside the parameters. Midjourney's documentation sets the `--stylize` default at 100 across a 0 to 1000 range. **Q: Do negative prompts still matter in 2026?** **A:** In Stable Diffusion tools, yes; the negative prompt remains a dedicated input field in AUTOMATIC1111 and equivalent runtimes. In Nano Banana and GPT Image 2, phrase exclusions as positive descriptions of the finished frame instead. **Q: Which AI image generator renders text best?** **A:** Nano Banana Pro is the strongest for legible text in images, and GPT Image 2 is close behind when you put the exact wording in quotes or capitals and specify the typography, as OpenAI's guide recommends.