JSON Prompts vs TOON Prompts vs Natural Language Prompts

Why all three matter and when to use each

As AI image and design models mature, the question is no longer “What’s the best way to prompt?” but “What’s the right prompt format for this model and this task?”

JSON prompts, TOON prompts, and natural language prompts are all valid. They just solve different problems. Understanding where each one shines and where it breaks down is how you get better results with less friction, lower cost, and more creative control.

This guide breaks down how each prompt type works, which models they suit best, and how to move between them without losing style consistency.

1. JSON prompts

Maximum control, maximum consistency

JSON prompts are structured, explicit, and deterministic. Instead of describing what you want in flowing language, you define it in clearly separated fields such as style, lighting, materials, camera, mood, and constraints.

Why JSON prompts are powerful

JSON prompts excel when:

• You need repeatable, consistent outputs

• You are building style systems, not one-off images

• You want prompts that behave more like configuration files than creative writing

This makes them especially effective with:

• GPT-based image generation systems

• Gemini-based models such as Nano Banana and Nano Banana Pro

These models are designed to interpret structured data cleanly. JSON gives them exactly what they want: unambiguous instructions with minimal interpretation drift.

Where JSON prompts struggle

JSON prompts are not universal:

• High token usage
  JSON is verbose. Every key, value, and nested object adds tokens, which increases cost.

• Poor compatibility with some diffusion-style models
  Models like Midjourney and Seedream tend to perform better with descriptive natural language.

• Creatively rigid
  JSON locks behaviour tightly. This is great for consistency, but bad for discovery and exploration.

In short, JSON prompts are best when you care about locking a look, not when you want to find one.

2. TOON prompts

Compressed structure with creative side effects

TOON prompts are a compressed evolution of JSON prompts. They aim to preserve structure while aggressively reducing token count by:

• Removing redundant wording

• Flattening hierarchy

• Compressing descriptors into shorthand patterns

Why TOON prompts exist

TOON prompts solve a very real problem:

• JSON prompts are expensive

• Some models penalise verbosity

• Token budgets matter at scale

TOON prompts are excellent when:

• You want structured intent at a lower token cost

• You are running large batch generations

• You want faster iteration without fully abandoning structure

The trade-off: consistency vs discovery

Because TOON prompts compress meaning:

• Some nuance is lost

• Style definitions can blur

• Outputs may drift slightly between generations

This is not always a downside.

TOON prompts are fantastic for exploration:

• They often surface unexpected variations

• They can reveal sub-styles hiding inside your original JSON

• They are useful for stress-testing a style system

However, if your goal is pixel-level consistency, TOON prompts are not a perfect replacement for JSON. Think of them as a creative mutation layer, not a style lock.

3. Natural language prompts

The fastest way to think visually

Natural language prompting is how most people start with AI, and for good reason:

• It is fast

• It is intuitive

• It works with every model

Why natural language is still king

Natural language prompts are ideal for:

• Rapid ideation

• Early-stage concepting

• Mood exploration

• Discovering new directions quickly

They allow you to think like a designer instead of an engineer.

Importantly, all models interpret natural language slightly differently:

• Some prefer short, punchy descriptors

• Others respond better to cinematic, descriptive language

• Some prioritise adjectives, others prioritise nouns

Prompting is not portable by default. A prompt that works in one model often needs small adjustments in another.

Converting JSON to natural language

A powerful hybrid approach is:

• Build your style in JSON

• Convert that JSON into clean natural language

• Use the converted prompt to style-lock models like:

  • Seedream

  • Seedance

  • Flux

  • Other diffusion-style systems

This gives you the consistency logic of JSON with the compatibility of natural language.

4. Midjourney: the outlier model

Natural language with a control layer

Midjourney sits in a category of its own.

At its core:

• Midjourney prefers simple, clear natural language

• Over-structuring usually hurts results

However, Midjourney adds an additional control system:

• Moodboard references

• Style reference codes (SREFs)

• Image weighting and prompt codes

What works best in Midjourney

For Midjourney:

• Keep prompts short and descriptive

• Avoid JSON-style formatting entirely

• Lock consistency using:

  • Reference images

  • SREF codes

  • Carefully curated moodboards

Midjourney rewards visual anchoring more than textual precision. The style lock happens through references, not through verbose instructions.

This is why JSON prompts tend to underperform in Midjourney, while well-crafted natural language plus strong references consistently outperform everything else.

Choosing the right prompt format

There is no universal winner. The best prompt format depends on what you are optimising for.

Final takeaway

Prompting is not about picking one format and committing forever.

• JSON prompts give you control

• TOON prompts give you efficiency and variation

• Natural language gives you speed and creative freedom

The strongest workflows move between these formats, not against them.

If you treat prompts as living systems rather than static text blocks, you unlock far better results across every model you use.