---
slug: too-many-models-problem
title: "You Don't Need 47 Image Models. You Need One That Works Consistently."
status: drafting
created: 2024-12-27
updated: 2025-12-28
author: henry
type: opinion-manifesto
target_length: 1800
distribution: social (HN, Reddit, Twitter)
seo_priority: low
---

# Idea

## Discovery

**Source:** Weekly digest 2024-12-27, Hacker News, Reddit  
**Evidence:**

HN Quote:
> "It is just very hard to make any generalizations because any single prompt will lead to so many different types of images. Every model has strengths and weaknesses depending on what you are going for."
> — Hacker News, December 2024

Community Pain:
- Fal.ai offers dozens of models
- Replicate has 100+ image models
- Runware positioning as "one API for all AI models"
- Developers overwhelmed by choice

**Reddit Pain Point:**
- Constant questions about "which model for X?"
- No consensus on best practices
- Switching costs high (prompts don't transfer between models)

## Why This Matters

**Strategic Rationale:**

1. **Counter-Positioning**
   - Competitors compete on model variety
   - We compete on consistency and simplicity
   - "Less but better" positioning

2. **Developer Pain Point**
   - Choice paralysis is real
   - Prompt engineering doesn't transfer across models
   - Inconsistent results kill production workflows

3. **Our Differentiation**
   - @name references solve consistency problem
   - Curated models, not marketplace
   - Project-based organization
   - "Pick once, use forever" philosophy

---

# Brief

## Potential Angle

**Anti-complexity manifesto + practical guide**

**Hook:**
"Replicate has 100+ image models. Fal.ai offers dozens. Runware promises 'all AI models in one API.' Meanwhile, you just want to generate a consistent hero image for your blog posts."

**Structure:**

1. **The Model Explosion Problem**
   - Screenshot Replicate's model marketplace
   - Show 47 variations of Stable Diffusion
   - Developer quote: "Which model do I use for photorealistic portraits?"
   - **The answer:** "It depends" (unhelpful)

2. **Why More Models ≠ Better Results**
   - Prompt engineering is model-specific
   - What works in SDXL breaks in Flux
   - Production consistency requires same model
   - Switching costs: re-engineering all prompts

3. **The Hidden Cost of Choice**
   - Time: Testing 10 models to find "the one"
   - Money: Burning credits on experiments
   - Maintenance: Model versions update, prompts break
   - Quote: "I spent 3 hours picking a model, then realized my prompts sucked anyway"

4. **What You Actually Need**
   - ONE good model for your use case
   - Consistent prompting patterns
   - Version control for working prompts
   - Project organization (context matters)

5. **How Banatie Solves This**
   - Curated models: We picked the best, you focus on building
   - @name references: Consistency across generations
   - Project organization: Context preserved automatically
   - **Philosophy:** "We're opinionated so you don't have to be"

6. **Practical Guide: Pick Your Model Once**
   ```
   IF photorealistic portraits → Flux Realism
   IF illustration/concept art → SDXL
   IF speed matters → Flux Schnell
   IF need control → Flux Dev
   ```
   
   Then STOP. Use that model. Build workflow around it.

7. **When Model Variety Actually Helps**
   - Experimentation phase (before production)
   - Specific artistic styles (Ghibli, pixel art)
   - Niche use cases (medical imaging, architecture)
   
   **But:** 80% of developers need consistency, not variety.

**Call to Action:**
- Try Banatie's opinionated approach
- Download our "Model Selection Worksheet"
- Join workflow-first developers

---

# Keyword Research

**Conducted:** 2025-12-27 by @strategist  
**Tool:** DataForSEO (Google Ads Search Volume)  
**Location:** United States  
**Language:** English

## Primary Keywords Tested

All tested keywords returned **zero or negligible search volume**:

| Keyword | Volume | Status |
|---------|--------|--------|
| too many ai models | 0 | No data |
| consistent ai image generation | 0 | No data |
| ai image api comparison | 0 | No data |

## Assessment

**Opportunity Score:** 🔴 Low (for direct SEO)

**Findings:**
- **Problem-aware keywords have zero volume** — people don't search for the problem this way
- Developers experience this pain but don't articulate it in search queries
- This is a "solution-unaware" problem:
  - They feel choice paralysis
  - They don't search "too many models"
  - They search specific model names or comparisons

**Strategic Value:**
- **Not an SEO play** — won't rank for high-volume keywords
- **Thought leadership piece** — articulates unspoken frustration
- **Social/community distribution** — Hacker News, Reddit, Twitter
- **Counter-positioning** — differentiates from competitors' "more is better"

**Alternative Keyword Strategy:**

Instead of problem-focused keywords, target:
- "stable diffusion vs flux" — comparison searches (volume unknown)
- "best ai image model" — solution-seeking searches
- "ai image generation best practices" — educational queries

**Distribution Strategy:**

Since SEO potential is low, focus on:
1. **Hacker News** — controversial opinion pieces do well
2. **r/MachineLearning, r/StableDiffusion** — community discussion
3. **LinkedIn** — CTOs/tech leads resonate with "less is more"
4. **Twitter** — thread format, tag model providers

**Recommendation:**

Write this as **opinion/manifesto piece** for:
- Brand differentiation (not SEO)
- Community discussion (HN front page potential)
- Thought leadership (shows market understanding)

**Do NOT write if:**
- Primary goal is organic traffic
- Need immediate SEO results
- Looking for high-volume keywords

**DO write if:**
- Want to establish counter-positioning
- Have strong opinion to share
- Targeting social/community distribution

---

## Keywords (original notes)

Potential:
- "best AI image model for developers"
- "stable diffusion vs flux"
- "consistent AI image generation"
- "too many AI models"
- "image generation best practices"

## Notes

**Tone:** 
- Empathetic (we get the frustration)
- Opinionated (we have a thesis)
- Practical (actionable advice)
- NOT arrogant ("competitors are dumb")

**Risk:** 
Sounds like we're limiting features.

**Mitigation:**
Frame as "opinionated defaults with flexibility underneath"
- We curate, but you CAN use any model
- Most developers need simplicity, power users get flexibility
- Better to be excellent at 3 models than mediocre at 47

**Competitor Response:**
- Replicate will say "but choice is good!"
- We say "choice without guidance is paralysis"
- Different philosophies for different developers

**Production Notes:**
- Need quotes from developers about model confusion
- Screenshot model marketplaces (Replicate, fal.ai)
- Create decision tree for model selection
- Test prompt portability across models (show it breaks)

**Similar Positioning:**
- 37signals (Basecamp): "Less software, more results"
- Apple: "Curated ecosystem vs Android chaos"
- Notion: "One tool vs 10 specialized tools"

We're applying same philosophy to AI image generation.

---

# Outline

## Article Structure

**Type:** Opinion / Manifesto  
**Total target:** 1800 words  
**Reading time:** 7-8 minutes  
**Voice:** Henry (direct, experienced, slightly provocative)  
**Distribution:** Social (HN, Reddit, Twitter) — NOT SEO-focused

## Hook & Opening (250 words)

**Goal:** Establish problem from personal experience, make reader think "yes, I've felt this"

**Opening:**
- Start with Henry's recent experience: spent 2 hours comparing Flux models
- The frustration: "Which one generates better portraits?"
- Answer from every guide: "It depends"
- The realization: wrong question entirely

**Transition:**
"Here's the thing about AI image model marketplaces: they've created a problem they can't solve."

**Signature phrases to use:**
- "Ran into {problem} yesterday..."
- "Here's the thing about..."
- "What I've found is..."

## Section 1: The Model Explosion (300 words)

**Goal:** Show the absurdity of current state with concrete examples

### The Numbers
- Replicate: 100+ image generation models
- Fal.ai: dozens of Stable Diffusion variants
- Runware: "all models in one API" (positioning itself as solution)

### The Developer Experience
- HN quote: "Every model has strengths and weaknesses..."
- Real question from Reddit: "Which model for photorealistic portraits?"
- Common answer: "Test them all and see" (unhelpful)

### The Paradox
- More choice = harder decisions
- "47 variations of Stable Diffusion" example
- Each claims to be "better" at something
- No consensus, no guidance

**Tone:** Observational, slightly sardonic but not mean

## Section 2: Why More Models ≠ Better Results (350 words)

**Goal:** Explain the fundamental problem with model variety for production use

### Prompt Engineering is Model-Specific
- What works in SDXL completely fails in Flux
- Example: Same prompt, 3 different models, wildly different results
- You're not learning "AI prompting" — you're learning "SDXL prompting"

### Production Requires Consistency
- Can't switch models mid-project
- Brand consistency matters
- User expectations set by first generation

### The Switching Cost
- Re-engineer all your prompts
- Test everything again
- Update documentation
- Quote from Henry: "Switched from Flux Dev to Flux Realism. Spent a day fixing prompts that worked fine before."

### The Illusion of Control
- People think: "I'll pick the best model for each use case"
- Reality: You'll pick one and stick with it anyway
- Or spend forever testing instead of shipping

**Tone:** Analytical but personal — backed by Henry's experience

## Section 3: The Hidden Costs (250 words)

**Goal:** Quantify what this complexity actually costs developers

### Time
- Initial research: 2-4 hours comparing models
- Testing phase: burn through credits trying each one
- Prompt iteration per model: hours
- Total: easily a full day before generating first production image

### Money
- Testing 10 models × 20 prompts each = 200 API calls
- At $0.05/image = $10 just for testing
- Then pick wrong model → start over

### Maintenance
- Model versions update
- Prompts break
- Features change
- "The model you picked 3 months ago now has v2, and your prompts don't work"

### Opportunity Cost
- Not shipping features
- Not iterating on actual product
- Stuck in "which model" paralysis

**Personal anecdote:**
"I've seen developers spend more time picking an image model than building the feature that needed the image."

**Tone:** Practical, matter-of-fact

## Section 4: What You Actually Need (300 words)

**Goal:** Shift perspective — the real solution isn't more choice

### ONE Model for Your Use Case
- Pick based on your primary need
- Stick with it
- Build workflow around it

### Consistency Patterns
- Version control for prompts that work
- Document what works in YOUR model
- Ignore what works in other models

### Project Organization
- Same model for same project
- Context matters — logo vs hero image vs illustration
- Consistency > variety

### Stop Optimizing the Wrong Thing
- You're optimizing for "best possible image per generation"
- Should optimize for "consistent good-enough images across project"
- 80% quality with 100% consistency > 90% quality with 50% consistency

**The Philosophy:**
"Pick once. Master it. Ship."

**Contrast with current approach:**
- Current: "Test everything, pick the best"
- Better: "Pick good-enough, become expert"

**Tone:** Direct, opinionated, confident

## Section 5: How to Pick Your Model (Once) (250 words)

**Goal:** Give readers actionable decision framework

### Simple Decision Tree

```
IF photorealistic/portraits → Flux Realism
IF illustrations/concept art → SDXL Lightning
IF speed critical → Flux Schnell  
IF maximum control → Flux Dev
IF you don't know → Flux Dev (most flexible)
```

### The Rules
1. Answer ONE question: What's your primary use case?
2. Pick the model
3. Stop researching
4. Start prompting

### When to Ignore This Advice
- You're in experimentation phase (not production)
- You need specific art style (Ghibli, pixel art, etc)
- You have niche requirements (medical, architecture)

**But:** Most developers fall in the 80% who just need consistency.

### What Happens Next
- Spend time learning YOUR model
- Build library of working prompts
- Understand its quirks and strengths
- Ship features

**Quote from Henry:**
"I picked Flux Dev six months ago. Haven't looked at another model since. My prompts work, my workflow is fast, and I'm shipping."

**Tone:** Practical, directive

## Section 6: The Curated Approach (200 words)

**Goal:** Present alternative philosophy (subtle Banatie mention — Phase 1 disclosure)

### The Counter-Position
- Industry says: "More models = more choice = better"
- Alternative: "Curated models = less paralysis = faster shipping"

### Examples of Curated Success
- Apple: Limited hardware options, higher customer satisfaction
- Basecamp: One tool, not ten specialized ones
- Notion: Unified workspace vs app chaos

### Applied to AI Image Generation
- What if platform picked 3-4 best models?
- What if prompts were consistent across them?
- What if you could switch models without re-engineering?

**Subtle mention:**
"I've been working on this problem. Built a tool that curates models and handles consistency. Turns out most developers just want it to work, not to become prompt engineers."

**Note:** Phase 1 disclosure — mention the tool, not affiliation yet

**Tone:** Thoughtful, positioning alternative without hard selling

## Conclusion (150 words)

**Goal:** Clear takeaway + next step

### Summary
- Model marketplaces solve wrong problem
- Developers need consistency, not variety
- Pick once, master it, ship

### The Action
Not "go try 47 models."
Instead: "Pick one model today. Use it for next month. Learn its patterns. Ship features."

### Final Thought
"The goal isn't to generate perfect images. The goal is to ship products that need images."

"Stop optimizing model selection. Start shipping."

**Signature closing:**
"Go build something."

**Tone:** Direct, actionable, confident

---

# Draft

Started working on a side project last week. Needed AI-generated images. Thought I'd try the top models everyone talks about—Flux Dev, Flux Realism, SDXL Lightning, see which one fits best.

Three hours later, I had a folder full of test generations, half a dozen browser tabs comparing model cards, and zero working images in my project.

The problem wasn't the models. The problem was having 47 of them to choose from.

Here's the thing about AI image model marketplaces: they've created a problem they can't solve. More choice doesn't make your decision easier. It makes it impossible.

## The Model Explosion

Replicate lists over 100 image generation models. Fal.ai offers dozens of Stable Diffusion variants. Runware positions itself as "one API for all AI models"—presenting this as a feature.

If you've ever tried to pick one, you know the pain. Search for "photorealistic portraits" and you'll find twelve models that claim to excel at it. Each model card says it's "optimized" for something. None of them tell you which one to actually use.

The answer you get everywhere is: "It depends."

But depends on what? Your use case? Your aesthetic preference? The phase of the moon? The more specific your question, the less helpful the answers become.

In my experience, having 47 variations of Stable Diffusion isn't a feature. It's a bug masquerading as flexibility.

The developer asking "Which model should I use?" isn't looking for philosophy. They're looking for a working solution. The marketplace model fails them completely.

## Why More Models ≠ Better Results

The fundamental problem: prompt engineering is model-specific.

A prompt that works perfectly in SDXL Lightning will produce garbage in Flux Dev. The same carefully crafted description that generates photorealistic portraits in Flux Realism creates cartoonish illustrations in SDXL. You're not learning "AI image prompting"—you're learning how to prompt one specific model.

Production workflows require consistency. You can't generate your landing page hero image with Flux Dev, then switch to Flux Realism for the feature graphics, then try SDXL for the blog headers. Your brand looks like it was designed by a committee that never met.

User expectations matter. Once you ship something generated with one model's aesthetic, you've set a baseline. Switching models mid-project breaks visual consistency in ways your users will notice.

The switching cost is real. Last month I moved from Flux Dev to Flux Realism thinking I'd get better photorealism. Spent a full day re-engineering prompts that worked fine before. Same descriptions, completely different results. Had to rebuild my entire prompt library from scratch.

What I've found is this: developers think they'll pick the best model for each use case. Reality is different. You'll either pick one model and stick with it anyway—or you'll burn days testing instead of shipping.

## The Hidden Costs

The time cost hits first. Initial research takes 2-4 hours just reading model cards and community discussions. Then you're burning through API credits testing each model with your actual use cases. Each model needs its own prompt iteration cycle—easily another few hours per model.

Do the math: test 10 models with 20 test prompts each. That's 200 API calls at roughly $0.05 per image. You've spent $10 just figuring out which model to use. Then you pick the wrong one and start over.

The maintenance cost is worse. Model versions update. Your prompts break. Features change. The model you picked three months ago releases v2, and suddenly your carefully tuned prompts don't work anymore. Back to testing.

The real killer is opportunity cost. I've seen developers spend more time picking an image model than building the feature that needed the image. They're stuck in analysis paralysis while their actual product sits unshipped.

[TODO: Add specific metrics from real projects about time lost to model selection]

## What You Actually Need

You don't need the "best" model. You need ONE model that works for your use case.

Pick based on your primary need. Stick with it. Build your workflow around it. Document what works. Ignore what other models can do.

The goal isn't to generate the perfect image on every single generation. The goal is to generate consistent, good-enough images across your entire project. 80% quality with 100% consistency beats 90% quality with 50% consistency every time.

Production consistency comes from version control for your prompts. When you find a pattern that works in YOUR model, save it. Build a library of working prompts. Understand that model's quirks and strengths.

Project organization matters. Use the same model for the same project. Context matters—a logo generation has different requirements than a hero image or a blog illustration. But within a project, consistency wins.

Stop optimizing for the best possible image per generation. Start optimizing for the fastest path from idea to shipped feature.

The philosophy that works: Pick once. Master it. Ship.

## How to Pick Your Model Once

Here's the decision framework:

```
IF photorealistic portraits → Flux Realism
IF illustrations/concept art → SDXL Lightning
IF speed critical → Flux Schnell
IF maximum control → Flux Dev
IF you don't know → Flux Dev (most flexible)
```

The rules are simple:

1. Answer ONE question: What's your primary use case?
2. Pick the matching model from the list above
3. Stop researching
4. Start building your prompt library

When to ignore this advice: You're in the experimentation phase before production. You need a specific art style like Ghibli or pixel art. You have niche requirements like medical imaging or architectural visualization.

But most developers fall in the 80% who just need consistency. Pick your model. Learn its patterns. Ship your features.

What happens next: You spend time understanding YOUR model instead of comparing models. You build a library of working prompts. You learn the quirks. You ship faster.

[TODO: Add personal experience with sticking to one model for six months]

## The Curated Approach

The industry says: "More models equals more choice equals better outcomes."

But there's an alternative philosophy: Curated models mean less paralysis and faster shipping.

Look at successful products outside this space. Apple ships limited hardware options and has higher customer satisfaction than Android's infinite choice. Basecamp built one tool instead of ten specialized ones. Notion created a unified workspace that replaced a dozen apps.

They're all applying the same principle: Opinionated defaults with flexibility underneath.

Applied to AI image generation: What if a platform picked 3-4 best models? What if prompts were consistent across them? What if you could switch models without re-engineering everything?

I've been working on this problem. Built a tool that curates models and handles consistency across generations. Turns out most developers just want it to work—they don't want to become prompt engineering experts just to ship an image.

The counter-position isn't about limiting choice. It's about making the right choice obvious, then getting out of your way.

## Ship, Don't Optimize

Model marketplaces solve the wrong problem. They optimize for breadth when developers need depth. They compete on quantity when what matters is consistency.

You don't need 47 models. You need ONE that works reliably. You need prompts that produce consistent results. You need a workflow that lets you ship instead of endlessly testing.

The action isn't "try all the models and see what works." The action is: Pick one model today. Use it for the next month. Build your prompt library. Ship your features.

The goal isn't to generate perfect images. The goal is to ship products that need images.

Stop optimizing model selection. Start shipping.

Go build something.
