AI Image Prompts: 12-Step Guide for 5 Models [2026]

September 8, 2026

21 min read

Type “a cat” into an AI image generator and you get a generic, forgettable picture. Type a structured, model-aware prompt and the same tool can produce a magazine-ready shot with legible text, correct hands, and the exact aspect ratio you need. The gap between those two outcomes is not luck. It is a skill, and in 2026 it is also measurable: a study on structured language in text-to-image generation found that prompt structure, not prompt length, is what predicts output quality. This tutorial walks through how to write prompts that work across the five AI image generators people actually use right now: Midjourney V8.1, Nano Banana Pro (Gemini 3 Pro Image), GPT Image 2, Ideogram 4.0, and FLUX.2. You will learn the exact syntax each platform expects, how to fix the failure modes that ruin most AI-generated images, and how to build a reusable prompt template system instead of starting from scratch every time.

None of these platforms speak the same language. Midjourney wants terse, parameter-heavy strings appended with double hyphens. Nano Banana Pro wants full sentences written like a cinematographer’s shot list. Ideogram 4.0 was trained on structured JSON captions and treats plain text as a second-class input. Mixing up these conventions is the single biggest reason people get mediocre results and blame the model instead of the prompt.

This matters more now than it did a year ago, because the gap between a well-prompted and poorly-prompted output has widened rather than narrowed. Newer models like Ideogram 4.0 and GPT Image 2 render dense in-image text and complex layouts well enough for commercial design work, but only when the input prompt is specific about placement, color, and wording. Feed them a vague one-liner and they still guess, they just guess with more confidence, which produces polished-looking mistakes that are easy to miss during a quick review. Learning the syntax each model actually expects turns that guesswork into a repeatable process.

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Prerequisites: accounts, tools, and current versions

Before starting, set up accounts and confirm you’re on current model versions. Prompt syntax changes between major versions, so an outdated guide will actively hurt you.

Midjourney V8.1 access through Discord or the web interface at midjourney.com, on any paid plan starting at $10/month for Basic
Google account with access to Nano Banana Pro (Gemini 3 Pro Image) through the Gemini app, Google AI Studio, or the Gemini API
OpenAI account with API access to GPT Image 2, or ChatGPT Plus/Pro for the built-in image tool
Ideogram account at ideogram.ai, free tier available, plus an API key if you plan to use structured JSON prompting programmatically
Black Forest Labs FLUX.2 access through the official API or an aggregator like fal.ai or Replicate
Python 3.10 or newer if you want to build the prompt template test harness in the final section
A text editor for saving and versioning your prompt templates (VS Code, Notion, or even a plain Markdown file works)

You do not need every one of these accounts to follow along. Pick the two or three models you actually use, then apply the platform-specific sections that match. The underlying prompt-anatomy framework in Step 1 applies everywhere.

Step 1: Learn the anatomy of a strong prompt

Every platform’s documentation converges on a similar skeleton, even when the surface syntax differs. OpenAI’s guidance for GPT Image models recommends ordering a prompt as background and scene, then subject, then key details, then constraints. Google’s official prompting tips for Nano Banana Pro use a near-identical formula: subject, action, location and context, composition, and style. Ideogram’s JSON schema formalizes the same idea into fields for a high-level description, a style block, and a compositional breakdown with individual elements.

Treat every prompt as five layers, written in this order:

Subject — who or what is in the frame, described with specific nouns, not vague categories
Action or pose — what the subject is doing, including body position and expression
Setting and context — where the scene takes place, time of day, environment
Composition and technical spec — camera angle, lens feel, lighting, aspect ratio
Style and constraints — medium (photo, 3D render, watercolor), plus what must not appear

A vague prompt like “a businesswoman in an office” leaves every one of those five layers to chance. A structured version, such as “a woman in her 40s wearing a charcoal blazer, reviewing a printed report at a standing desk, in a sunlit corner office with floor-to-ceiling windows, shot from a low three-quarter angle with soft window light, photorealistic, shallow depth of field, no text or logos visible,” gives the model almost nothing to guess about. That is the difference reflected in the 2026 research on structured prompting: organized, layered detail outperforms both terse prompts and rambling, unstructured ones of the same length.

Step 2: Match your prompt style to the platform, not the other way around

The five-layer skeleton from Step 1 stays constant. How you express it changes completely depending on which model receives it. There are three broad prompting dialects in active use in September 2026:

Model	Prompting style	Syntax convention	Best suited for
Midjourney V8.1	Terse keyword phrase + appended parameters	Double-hyphen flags: –ar, –stylize, –chaos, –seed, –sref, –cref	Stylized, artistic, illustrative work
Nano Banana Pro (Gemini 3 Pro Image)	Conversational, full-sentence, multi-turn	Natural language, no special flags, reference images uploaded directly	Photorealism, iterative editing, multi-turn refinement
GPT Image 2	Structured paragraph with labeled segments	Scene, subject, details, constraints written as plain English blocks	Marketing assets, precise text rendering, multi-image composition
Ideogram 4.0	Structured JSON caption	high_level_description, style_description, compositional_deconstruction with bbox and hex colors	Design layouts, posters, dense in-image text
FLUX.2	Descriptive paragraph, moderate structure	Plain text, supports reference images and light parameter tuning via API	Fast, cost-efficient batch generation

If you paste a Midjourney-style keyword string into Ideogram 4.0, you are handing the model text it was explicitly trained to treat as second-class input, according to Ideogram’s own prompting documentation. If you paste a dense JSON object into Midjourney, it will read the braces and quotation marks as literal words to render. Model-switching without adjusting syntax is the number one reason people conclude a “better” model actually performs worse.

Step 3: Write conversational prompts for Nano Banana Pro

Google’s official prompting tips for Nano Banana Pro are explicit: write full sentences, not comma-separated keyword lists. The company’s own guidance frames the ideal prompt as a hierarchy: subject and adjectives, doing an action, in a location or context, with a stated composition, in a stated style. Camera and lighting language should also be written as natural phrases, like “cinematic 21:9 wide shot” or “low-angle shot with shallow depth of field,” rather than tacked-on tags.

Here is a prompt built from that formula:

A weathered lighthouse keeper in his sixties, standing at the top railing
of a coastal lighthouse, checking a brass telescope during a storm at dusk.
Wide establishing shot, camera positioned below looking up, dramatic
backlighting from the lighthouse beam cutting through rain. Cinematic
21:9 aspect ratio, photorealistic, muted blue and amber color grading,
no text, no watermark, no visible logos.

Nano Banana Pro’s real advantage shows up in multi-turn editing. Instead of rewriting the whole prompt to change one detail, you send a follow-up instruction referencing the previous result. Documentation from Google’s cloud platform and third-party API wrappers both describe the same mechanic: the model returns a conversation-history object alongside the image, you pass that history back with your next request, and you add a short natural-language edit such as “keep the same layout but change the outfit to a yellow raincoat” or “make the lighting warmer.” Keep each turn focused on one or two changes. Stacking five edits into a single follow-up tends to degrade consistency with the original composition.

Step 4: Master Midjourney V8.1 parameters

Midjourney’s V8.1 parameter set controls everything a conversational model would infer from sentence structure. Every parameter is appended to the end of the prompt text with a double hyphen.

a retro-futuristic diner on Mars, neon signage, chrome countertops,
astronauts having coffee, wide shot, cinematic lighting
--ar 16:9 --stylize 250 --chaos 15 --seed 48213

The core flags to know for V8.1:

–ar (or –aspect) sets width-to-height ratio using integers, such as –ar 16:9 or –ar 9:16, with 1:1 as the default
–stylize (or –s) ranges from 0 to 1000 and controls how much of Midjourney’s house art style gets layered over your literal description; V8.1’s default sits at 100, with 0-100 staying close to the prompt and 750-1000 pushing toward bold, artistic interpretation
–chaos (or –c) ranges from 0 to 100 and controls variation between the four images in a generation grid; higher values produce more unpredictable results
–seed locks the starting noise pattern so the same prompt and seed produce near-identical results across runs, described in Midjourney’s own materials as approximately 99% identical rather than pixel-perfect
–sref attaches a style reference image so new generations match its visual look
–cref attaches a character reference image to keep a specific character consistent across multiple generations

If you’re coming from V7, note what changed. V8 dropped Omni Reference and Omni Reference Weight, along with Draft Mode, Turbo Mode, the standalone –quality parameter, and direct –niji version switching, according to comparison guides tracking the V7-to-V8 transition. V8.1 added SD and HD output modes, supporting aspect ratios up to 14:1 in SD and up to 4:1 in HD, so widescreen and ultra-tall compositions both stay within range.

Step 5: Structure GPT Image 2 prompts the way OpenAI recommends

OpenAI’s cookbook guidance for its image models lays out a consistent order: where the image exists, what the image is about, what details must be visible, and what must not drift. For prompts with several requirements, OpenAI recommends breaking them into labeled segments or line breaks rather than one dense paragraph.

Scene: A modern product photography studio with a seamless white
background and soft diffused lighting from above.
Subject: A matte black wireless earbud case, closed, positioned at a
slight three-quarter angle on a small reflective acrylic platform.
Details: Sharp focus on the case's texture and hinge line, subtle
reflection beneath the product, soft shadow falling to the right.
Constraints: No text, no logos, no watermark, no additional props
in frame, square 1:1 crop.

For text that needs to render exactly as written, put the literal string in quotes or ALL CAPS inside the prompt, and specify font style, size, color, and placement as separate constraints. For multi-image inputs, reference each source image by index and role, such as “Image 1: product photo, front-facing; Image 2: style reference, vintage poster. Apply Image 2’s color and texture treatment to Image 1.” For edits, repeat a “change only X, keep everything else the same” instruction on every iteration, since restating the preserve-list each turn prevents gradual drift away from the original image. OpenAI’s documentation also recommends starting with a lower quality setting for latency-sensitive or high-volume batch jobs, then comparing medium and high settings only for final assets that need dense text, close-up portraits, or identity-sensitive edits.

Step 6: Build structured JSON prompts for Ideogram 4.0

Ideogram 4.0, the 9.3-billion-parameter diffusion transformer model Ideogram released as open weights on June 3, 2026, was trained on structured JSON captions rather than plain sentences, according to the model’s own prompting guide. That makes it the most technically demanding of the five platforms to write for, but also the most precise once you learn the schema.

A JSON prompt has three top-level fields, and Ideogram’s documentation specifies they should appear in this order: high_level_description, style_description, and compositional_deconstruction. Bounding boxes use a normalized 0-1000 grid with the origin at the top-left corner, formatted as [y_min, x_min, y_max, x_max]. Color conditioning uses uppercase hex codes only, no shorthand and no lowercase letters.

{
  "high_level_description": "A minimalist concert poster for an
  indie band's summer tour, bold typography, warm sunset palette.",
  "style_description": {
    "medium": "vector illustration, flat design",
    "lighting": "warm gradient background",
    "color_palette": ["#FF6B35", "#F7C59F", "#2E2A2B"]
  },
  "compositional_deconstruction": {
    "background": "warm orange-to-pink gradient sky over a
    silhouette skyline",
    "elements": [
      {
        "type": "text",
        "bbox": [50, 100, 250, 900],
        "desc": "large bold headline reading SUMMER TOUR 2026",
        "color_palette": ["#2E2A2B"]
      },
      {
        "type": "obj",
        "bbox": [300, 200, 900, 800],
        "desc": "silhouette of a guitarist mid-performance"
      }
    ]
  }
}

Bounding boxes are optional. Leaving bbox out of an element lets Ideogram place it automatically, which is fine for casual generations. Add boxes only when a design absolutely requires an element in a specific region, like a headline that must sit in the top third of a poster. Ideogram’s global color_palette field supports up to 16 hex values, while a per-element palette supports up to 5. Describing a color in words, like “deep red,” is documented as measurably less effective than supplying the exact hex code.

When plain text still works on Ideogram

Ideogram’s consumer web app still accepts plain-text prompts, and for quick, low-stakes generations that’s perfectly reasonable. Reach for the JSON schema specifically when you need reliable bounding-box placement, exact color matching, or dense, legible in-image text, since those are the areas where plain text degrades fastest on this model.

Step 7: Write effective negative prompts and constraints

Negative prompting means explicitly stating what should not appear in the output. The mechanism differs by platform, but the underlying discipline is identical everywhere: models drift toward including extra elements (text, watermarks, extra limbs, background clutter) unless you rule them out directly.

Google’s own guidance for Nano Banana Pro favors positive framing (describe what you want) but still uses negative phrases inside constraint clauses when needed, such as “no visible text”
OpenAI’s guidance for GPT Image 2 uses direct constraint language: “no watermark,” “no extra text,” “no logos or trademarks,” and “preserve identity, geometry, layout, and brand elements” on repeated edits
Older Stable Diffusion-style weighted syntax like word::2 still appears in some open-source tooling, but current-generation Midjourney and GPT Image 2 documentation lean on parameter flags and structured language instead of inline numeric weighting

Write constraints as a short, explicit list at the end of every prompt, regardless of platform: no watermark, no extra text unless specified, no additional limbs, no logo, correct hand anatomy. It reads redundant the first few times. It also eliminates a large share of the retakes that waste your generation credits.

Step 8: Control aspect ratio and technical specs consistently

Aspect ratio syntax is one of the few places where every platform genuinely differs, so keep a reference handy rather than guessing.

Model	Aspect ratio method	Example
Midjourney V8.1	–ar flag with integer ratio	–ar 16:9, –ar 9:16, –ar 2:3
Nano Banana Pro	Natural language description	“cinematic 21:9 wide shot” or “vertical 9:16 poster format”
GPT Image 2	Stated in constraints block	“square 1:1 crop” or “widescreen 16:9 output”
Ideogram 4.0	Set at the application or API level, canvas uses normalized coordinates internally	Chosen via client app dropdown, not inside the JSON body
FLUX.2	API parameter, separate from the text prompt	width and height values passed as request fields

A frequent mistake is putting an aspect ratio instruction inside a JSON prompt body for Ideogram, where it has no defined field and gets ignored or misread as a stray description. Set canvas dimensions at the platform or API layer, and keep the JSON body focused purely on content and layout.

Step 9: Fix the most common AI image failure modes

Three failure categories account for most of the retakes people run. Each has a documented, repeatable fix rather than a vague “just try again.”

Failure mode	Why it happens	Fix
Hand and finger errors (extra or missing fingers, melted shapes)	Training-distribution gaps around complex, self-occluding hand poses	Add explicit anatomical constraints (“five fingers, knuckles clearly articulated, thumb visible”) or crop hands out of frame; use inpainting to fix a single region without regenerating the whole image
Text garbling or misspelled words	Vague or absent instructions about exact wording, font, and placement	Put literal text in quotes or ALL CAPS, specify font and placement, or switch to Ideogram 4.0’s structured JSON with dedicated text-type elements and bounding boxes
Stretched necks, oversized shoulders, odd poses	Unusual camera angles or occluded compositions push the model into rare training examples	Reduce stylize and chaos values on Midjourney, use pose-control tools like ControlNet or Firefly’s Structure Reference where available, and keep camera angles closer to conventional framing

Inpainting deserves special mention because it applies across nearly every platform. Instead of regenerating an entire image because one hand looks wrong, select just that region and regenerate only the selection while the rest of the composition stays locked. Every one of the five platforms in this guide supports some form of masked regional editing, whether it is called inpainting, Edit, or Magic Fill depending on the vendor.

Step 10: Use multi-turn editing and prompt chaining

Prompt chaining means starting with a base generation and refining it through a sequence of follow-up instructions rather than trying to perfect everything in one shot. This is where conversational models pull ahead of parameter-based ones. Here’s the general request shape documented for Gemini 3 Pro Image’s multi-turn conversation flow, where the response includes a history object you feed back into the next call:

{
  "contents": [
    { "role": "user", "parts": [{ "text": "A cozy bookstore cafe,
      warm lighting, rain visible through the window" }] },
    { "role": "model", "parts": [{ "inlineData": { "...": "..." } }] },
    { "role": "user", "parts": [{ "text": "Keep the same layout, but
      change it to golden-hour sunlight instead of rain" }] }
  ]
}

The pattern generalizes even to platforms without a formal history API. On GPT Image 2, you re-send the previous image alongside a short instruction like “change only the lighting, keep the layout and composition identical.” On Midjourney, you use the Vary (Region) tool combined with a locked seed to nudge one part of an existing grid result without losing the rest. In every case, the discipline is the same: change one or two things per turn, and always restate what must stay fixed.

Step 11: Use style references and character consistency features

Keeping a consistent character, brand style, or art direction across a batch of images used to require careful prompt repetition and a lot of luck. All five platforms now ship dedicated reference mechanisms instead:

Midjourney’s –sref parameter attaches a style reference image, and –cref attaches a character reference image to anchor a consistent face or figure across separate generations
Nano Banana Pro accepts uploaded reference images directly in the prompt turn, with roles specified in plain language, such as “use this image for pose reference” or “match this image’s art style”
GPT Image 2 references multiple input images by index and role inside the prompt text, for example instructing the model to apply one image’s style to another image’s subject
Ideogram 4.0 includes a Character feature that maintains subject consistency from a single reference photo, alongside a separate Style Reference input that accepts up to three images

If your project needs the same character across a dozen images, generate one strong reference shot first, then feed it into every subsequent prompt through whichever mechanism your chosen platform supports, rather than re-describing the character’s appearance from scratch each time. Text descriptions of faces are notoriously inconsistent across separate generations even with an identical prompt string.

Step 12: Build a reusable prompt template library

Once you have working prompts for a recurring task, like product photography or social media graphics, save them as parameterized templates instead of retyping variations by hand. A simple Python script can store templates per model and swap in variables:

import json

TEMPLATES = {
    "midjourney_product": (
        "{product}, studio product photography, seamless "
        "{bg_color} background, soft diffused lighting, "
        "sharp focus --ar {ar} --stylize 50 --chaos 0"
    ),
    "gpt_image_product": (
        "Scene: A modern product photography studio with a seamless "
        "{bg_color} background.\n"
        "Subject: {product}, positioned at a slight angle.\n"
        "Details: Sharp focus, soft shadow, subtle reflection.\n"
        "Constraints: No text, no logos, no watermark, {ar} crop."
    ),
}

def build_prompt(template_key, **kwargs):
    return TEMPLATES[template_key].format(**kwargs)

prompt = build_prompt(
    "midjourney_product",
    product="a matte black wireless earbud case",
    bg_color="white",
    ar="1:1",
)
print(prompt)

This is deliberately simple. It does not call any API, it just standardizes how you assemble prompts so a five-person marketing team produces consistent output instead of five different prompting styles. Extend it later with a JSON file per model if you want version control on your templates, which matters once a team starts sharing them.

Common pitfalls when writing AI image prompts

Using one prompting style across every platform. A Midjourney-style keyword string underperforms badly on Nano Banana Pro and Ideogram 4.0, which expect sentences or JSON respectively.
Overloading a single prompt with conflicting instructions. Asking for “photorealistic” and “flat vector illustration” in the same prompt forces the model to average two incompatible styles, usually producing a muddy middle ground.
Describing colors in words instead of hex codes on Ideogram. “Deep red” is measurably less precise than “#8B0000” according to Ideogram’s own documentation, and the gap widens on brand-sensitive work.
Skipping constraints and hoping for the best. Omitting “no watermark” or “no extra text” does not mean the model won’t add one, it just means you have no recourse when it does.
Regenerating the whole image to fix one flaw. A single bad hand or a garbled word almost never requires a full regeneration when inpainting or a masked edit tool is available.
Stacking too many edits into one multi-turn instruction. “Change the lighting, the outfit, the background, and the pose” in a single follow-up turn tends to destabilize the whole composition rather than nail four separate changes.
Ignoring version-specific parameter changes. Copying a V7 Midjourney cheat sheet into V8.1 means using –quality or –niji flags that no longer exist in the standard workflow.

Troubleshooting guide

Output ignores half my prompt. The prompt is likely too long and unstructured. Break it into the five-layer skeleton from Step 1 and cut anything not essential to the shot.
Text in the image is misspelled. Put the exact text in quotes, specify the font and placement separately, and on Ideogram 4.0 use a dedicated text-type JSON element with a bounding box instead of describing text in prose.
Hands are distorted in almost every generation. Add explicit finger-count and knuckle-articulation language, reduce Midjourney’s –chaos value, or crop the composition so hands fall outside the frame entirely.
Same seed produces a noticeably different image. Midjourney’s own materials describe seed-locked repeats as approximately 99% identical, not pixel-perfect; minor variation is expected, especially across different aspect ratios.
My Ideogram JSON prompt returns an error. Check key order first (high_level_description, then style_description, then compositional_deconstruction) and confirm every hex code is uppercase with the full six-digit format, since shorthand codes are rejected.
Aspect ratio flag is being ignored. Confirm the flag sits at the very end of the prompt string on Midjourney, after any other parameters, and double-check you’re using an integer ratio like 16:9 rather than a decimal.
Multi-turn edit reverted unrelated parts of the image. You likely didn’t restate a “keep everything else the same” constraint. Add it explicitly to every edit turn, not just the first one.
Reference image is being ignored. Confirm you’re using the correct mechanism for the platform, –sref or –cref on Midjourney, direct image upload with a stated role on Nano Banana Pro and GPT Image 2, or the Style Reference / Character fields on Ideogram, since a plain-text description of a reference image is not the same as attaching one.
Batch generations are inconsistent in style across a set. Lock a seed where supported, or attach the same style reference image to every prompt in the batch instead of relying on repeated text descriptions.
API call for structured JSON prompting keeps failing validation. Confirm the JSON is syntactically valid first with a linter before debugging content, since a trailing comma or unescaped quote inside a description string is a common silent failure.

Advanced tips for consistent, production-grade output

Once the basics are solid, a few habits separate reliable production workflows from one-off experimentation.

Build a small scoring rubric before generating a batch, not after. Decide in advance what “good” means for the task: correct text, correct brand colors, correct composition, no visible artifacts. Score every output against that fixed rubric instead of eyeballing a preference, which keeps a five-person team aligned on quality bar rather than five different opinions.

Start low-cost, scale up only what survives review. On GPT Image 2, generate a first pass at low quality to validate composition and layout, then re-run only the winning candidates at medium or high quality. This mirrors OpenAI’s own guidance and can meaningfully cut spend on high-volume batch jobs, since Ideogram’s Turbo tier at $0.03 per image versus its Quality tier at $0.10 per image is more than a 3x cost difference for the same prompt.

Keep a rejection log alongside your template library. When a prompt fails, note why (hands, text, wrong aspect ratio, style mismatch) rather than just discarding it. Patterns emerge fast, and most teams find two or three failure modes account for the bulk of their wasted generations.

Separate your creative prompt from your technical constraints in your own notes, even on platforms where they get merged into one string before submission. It makes it far easier to swap platforms later without re-deriving the creative brief from scratch.

Finally, budget time for a version check before every large batch job. Model providers ship parameter and schema changes without much fanfare, and a prompt template that worked perfectly last month can silently underperform after an update, such as the V7-to-V8 parameter changes on Midjourney or a schema tweak to Ideogram’s JSON fields. A five-minute test run on two or three prompts before committing to a hundred-image batch catches this early, and it costs far less than discovering the issue after the fact.

Complete working project: a cross-platform prompt kit

Pulling every step together, here is a complete, minimal project structure for a small team that needs consistent AI-generated product images across Midjourney, GPT Image 2, and Ideogram 4.0.

prompt-kit/
├── templates/
│   ├── midjourney.json
│   ├── gpt_image.json
│   └── ideogram.json
├── rejection_log.csv
├── build_prompt.py
└── README.md

templates/midjourney.json holds keyword-and-parameter strings with placeholders. templates/gpt_image.json holds the four-part scene/subject/details/constraints blocks. templates/ideogram.json holds full JSON schema skeletons with bbox and hex-color placeholders already structured in the correct key order. build_prompt.py is the same substitution script from Step 12, extended to load templates from these files instead of a hardcoded dictionary. rejection_log.csv tracks every failed generation with a one-line reason, feeding the advanced-tips habit of pattern-spotting across a project.

This structure scales from a solo creator to a full team because the format-specific knowledge (parameter syntax, JSON schema, constraint phrasing) lives in the templates rather than in any one person’s head. A new team member can generate on-brand output on day one by filling in placeholders, without first reading every platform’s documentation.

Per-image API cost comparison across the major models

Prompt quality and cost per attempt are connected: a well-structured prompt that gets the shot on the first or second try is cheaper than five retakes of a vague one, regardless of the platform’s list price.

Model	API price per image	Notes
Ideogram 4.0 Turbo	$0.03	Fastest, lowest-cost tier; Default runs $0.06 and Quality runs $0.10
FLUX.2	$0.014	Among the cheapest per-image rates of any current-generation model
Seedream 5.0	$0.035	Mid-range pricing for a current-generation model
GPT Image 2	$0.006 to $0.211	Wide range driven by resolution and quality setting; low-quality 1024×1024 sits at the bottom
Nano Banana Pro (Gemini 3 Pro Image)	$0.134 to $0.24	Lower figure for 1K/2K output, higher figure for 4K output
Midjourney	$10/month and up	Subscription model rather than per-image API pricing; Basic starts at $10/month, higher tiers run to $120/month

These figures move often as vendors adjust pricing, so treat the table as a snapshot rather than a permanent reference. Check each provider’s live pricing page before committing to a high-volume workflow, and budget for retakes in your cost model, not just the sticker price per successful image.

Frequently asked questions

Do longer prompts always produce better AI images?

No. A 2026 study on structured language in image generation found that prompt structure, not word count, correlates with output quality. A long, rambling prompt with no clear hierarchy often underperforms a shorter, well-organized one that clearly separates subject, action, setting, and constraints.

Can I use the same prompt across Midjourney, GPT Image 2, and Ideogram 4.0?

Not directly. Each platform expects a different format: Midjourney wants keyword phrases with appended parameters, GPT Image 2 wants labeled plain-English segments, and Ideogram 4.0 wants a structured JSON object. Keep the same underlying creative brief but translate the syntax for each target model.

Why do AI image generators still struggle with hands?

Hands involve complex, self-occluding poses that are underrepresented and highly variable in training data compared to faces or full-body poses. Explicit anatomical constraints in the prompt, pose-control tools, or simply framing shots so hands fall outside the crop all reduce the error rate.

What is the difference between a negative prompt and a constraint?

In practice they describe the same idea: telling the model what should not appear in the output. Some platforms have a dedicated negative-prompt field, while others, including current Nano Banana Pro and GPT Image 2 guidance, fold this into the main prompt as a stated constraint like “no watermark” or “no extra text.”

Is Ideogram 4.0’s JSON prompting worth learning for casual use?

For quick, low-stakes generations, plain text is fine and Ideogram’s app still accepts it. JSON prompting earns its complexity when you need exact bounding-box placement, precise hex-code color matching, or dense, legible in-image text, which is where plain-text prompts on this model degrade fastest.

How do I keep a character consistent across a series of AI-generated images?

Generate one strong reference image first, then use each platform’s dedicated consistency feature, such as Midjourney’s –cref parameter or Ideogram’s Character feature, rather than re-describing the character’s appearance in text for every new prompt. Text-only descriptions of faces are inconsistent across separate generations even when the wording is identical.

What is prompt chaining and when should I use it?

Prompt chaining means refining an image through a sequence of small, targeted follow-up instructions instead of trying to get everything right in one generation. It works best on conversational, multi-turn platforms like Nano Banana Pro, and the key discipline is changing one or two elements per turn while explicitly restating what should stay the same.

Do Midjourney V7 prompts still work on V8.1?

Most base prompt text carries over fine, but several parameters were dropped in the V7-to-V8 transition, including Omni Reference, Draft Mode, Turbo Mode, the standalone –quality flag, and direct –niji version switching. Update any saved parameter strings before relying on them in V8.1.

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Sofia Lindström

Editor-in-Chief

Sofia Lindström is the Editor-in-Chief at Tech Insider, where she leads editorial strategy and oversees coverage across AI, cybersecurity, and enterprise technology. With over a decade in Swedish tech journalism, she previously served as technology editor at Dagens Industri and covered the Nordic startup ecosystem for Breakit. Sofia holds an MSc in Media Technology from KTH Royal Institute of Technology and is a frequent speaker at Web Summit and Slush. She is passionate about making complex technology accessible to business leaders.

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How to Write AI Image Prompts: 12 Steps, 90 Min [2026]