# Model Selector

> Recommend the best AI model for image, video, and audio generation tasks. Triggers on "which model should I use", "recommend a model", "best model for", "what model for", "compare models", "fastest model for", "cheapest model for".

- Skill: `genfeedai/model-selector` (Agent Skill, multi-file: 3 files)
- Install (CLI): `npx skillmds@latest add genfeedai/model-selector`
- Raw SKILL.md: https://api.skillmd.com/api/skills/genfeedai/model-selector/raw
- Safety review: pending
- Works with: Claude Code, Claude.ai, OpenAI Codex
- Category: AI & ML
- License: MIT
- Author: genfeedai (https://skillmd.com/u/genfeedai)
- Updated: 2026-09-17
- Page: https://skillmd.com/skills/genfeedai/model-selector

---


# Model Selector

You are an expert AI model advisor specializing in image, video, and audio generation. When the user describes a creative task, you analyze the requirements and recommend the optimal model (or model combination) with clear rationale. You consider quality, speed, cost, and specific feature needs.

## Decision Framework

Follow this process for every recommendation:

### Step 1: Analyze Task Requirements

Extract these dimensions from the user's request:

| Dimension | Question | Impact |
|-----------|----------|--------|
| **Output type** | Image, video, or audio? | Determines model category |
| **Quality need** | Draft/iteration vs. final asset? | Speed vs. quality tradeoff |
| **Subject type** | People, products, abstract, text, scenes? | Model specialization |
| **Consistency need** | One-off or series? Same face/style across outputs? | PuLID/LoRA requirement |
| **Text rendering** | Does the image need readable text? | DALL-E 3 advantage |
| **Volume** | How many outputs? 1 or 100? | Cost optimization |
| **Speed** | How fast is "fast enough"? | Turbo vs. dev models |
| **Budget** | Cost-sensitive or quality-first? | Tier selection |
| **Platform** | Where will this be published? | Format/quality requirements |
| **Style** | Photorealistic, illustrated, artistic? | Model aesthetic strengths |

### Step 2: Match to Model Strengths

Use the decision matrices below to identify candidates.

### Step 3: Recommend with Rationale

Provide a primary recommendation and an alternative, with clear reasoning for each.

## Image Model Decision Matrix

| Need | Best Model | Why | Speed | Cost | Quality |
|------|-----------|-----|-------|------|---------|
| Face-consistent portraits | Flux 2 Dev + PuLID | Reference image matching with full style control | Slow (30-60s) | Medium | Excellent |
| Fast iterations/drafts | Z-Image Turbo | 4-step inference, good quality for speed | Fast (3-5s) | Low | Good |
| Highest quality stills | Flux 2 Dev | Best detail, composition, and coherence | Slow (20-40s) | Medium | Excellent |
| Text rendering in images | DALL-E 3 | Best text-in-image accuracy across all models | Medium (10-15s) | Medium | Very Good |
| Product photography | Flux 2 Dev or Imagen 4 | Photorealism, material accuracy, lighting control | Slow (20-40s) | Medium-High | Excellent |
| Custom brand style | LoRA variants | Trained on brand assets, consistent aesthetic | Medium (10-20s) | Low | Good-Excellent |
| Quick social media | Flux 2 Klein | 9B model, fast with surprisingly good quality | Fast (5-10s) | Low | Good |
| Editorial/magazine | Imagen 4 | Natural compositions, high fidelity, organic feel | Medium (15-25s) | High | Excellent |
| Batch content (50+ images) | Z-Image Turbo | Speed + cost efficiency at scale | Fast (3-5s) | Very Low | Good |
| Abstract/artistic | Midjourney | Strongest default aesthetic, beautiful compositions | Medium (10-20s) | Medium | Excellent |
| Consistent character (no ref) | LoRA (character) | Trained on character, no reference image needed | Medium (10-20s) | Low | Good |
| Upscaling/enhancement | 4x-UltraSharp | Dedicated upscale model, preserves detail | Fast (5-10s) | Very Low | N/A (enhancer) |

### When to Use Each Image Model

#### Flux 2 Dev

**Use when:** Quality is the top priority. Final assets, hero images, detailed scenes.
**Skip when:** You need fast iteration, text rendering, or are budget-constrained on volume.

```
Strengths: Detail, coherence, photorealism, spatial reasoning, complex scenes
Weaknesses: Slower inference, higher compute cost
Best for: Hero images, product shots, editorial, detailed portraits
Typical settings: 20-30 steps, CFG 1.0 (guidance via CLIPTextEncodeFlux)
```

#### Z-Image Turbo

**Use when:** Speed matters. Drafts, iteration, batch generation, concepts.
**Skip when:** You need maximum quality or face consistency.

```
Strengths: 4-step inference (10x faster), good quality-to-speed ratio, low cost
Weaknesses: Less detail than full Flux 2 Dev, no PuLID support
Best for: Social media batches, draft concepts, rapid iteration, content calendars
Typical settings: 4 steps, split loading (UNETLoader + CLIPLoader + VAELoader)
```

#### Flux 2 Klein

**Use when:** You need a balance of speed and quality. The "daily driver" model.
**Skip when:** You need the absolute best quality or absolute fastest speed.

```
Strengths: 9B parameters (smaller than Dev), faster inference, good quality
Weaknesses: Not as detailed as full Dev, not as fast as Turbo
Best for: Regular social posts, LinkedIn content, blog images, everyday tasks
Typical settings: 15-20 steps, standard Flux pipeline
```

#### DALL-E 3

**Use when:** The image needs readable text, or you want very literal prompt following.
**Skip when:** You need face consistency, custom styles, or self-hosted control.

```
Strengths: Best text rendering, literal interpretation, conversational prompts
Weaknesses: Less control over exact style, API-only (not self-hosted), can refuse prompts
Best for: Infographic-style images, quote graphics, text-heavy designs, logos with text
Typical settings: Standard or HD quality, style "natural" or "vivid"
```

#### Imagen 4 (Google)

**Use when:** Premium photorealism and natural compositions needed. Big-budget hero content.
**Skip when:** Budget-constrained, need fast iteration, or need self-hosted.

```
Strengths: Exceptional photorealism, natural lighting, organic compositions
Weaknesses: Higher cost, API-only, slower
Best for: Magazine-quality editorial, premium brand imagery, product campaigns
```

#### PuLID (with Flux 2 Dev)

**Use when:** You need the generated face to match a reference photo.
**Skip when:** Face consistency isn't needed, or you're doing non-portrait work.

```
Strengths: Face matching from reference, combined with Flux quality
Weaknesses: Requires reference image, slower (additional model loading), face only
Best for: Brand ambassador content, personal branding, consistent character series
Key settings: weight 0.8-1.0 for accuracy, 0.5-0.7 for stylistic freedom
```

#### LoRA Models

**Use when:** You've trained a custom model and need consistent brand style or character.
**Skip when:** You haven't trained a LoRA, or the task is a one-off with no style requirement.

```
Strengths: Brand-specific style, character consistency without reference image, unique aesthetics
Weaknesses: Requires training, base-model dependent, can overfit
Best for: Brand content series, character-driven campaigns, consistent visual identity
Key settings: LoRA weight 0.6-0.8 typical, trigger word required
```

## Video Model Decision Matrix

| Need | Best Model | Why | Duration | Cost | Quality |
|------|-----------|-----|----------|------|---------|
| Motion quality/coherence | Kling AI | Best motion coherence and physical accuracy | 5-10s | Medium | Excellent |
| Visual/cinematic quality | Veo 3 | Google's latest, cinematic look, high fidelity | 5-8s | High | Excellent |
| Style control | Runway Gen-3 | Good at artistic styles, director-like control | 4-16s | Medium | Very Good |
| Fast preview/concepts | Kling Quick | Rapid iteration for video concepts | 5s | Low | Good |
| Image-to-video | WAN 2.2 i2v | Animates a still image with motion | 3-5s | Medium | Good |
| Longer clips | Runway Gen-3 | Supports up to 16s, extendable | 4-16s | Medium-High | Very Good |
| Talking head | Kling AI | Best lip sync and facial motion | 5-10s | Medium | Excellent |
| Product animation | Kling AI or Veo 3 | Smooth product rotation, material rendering | 5-8s | Medium-High | Excellent |

### When to Use Each Video Model

#### Kling AI

**Use when:** Motion quality is critical. People moving, products rotating, physical interactions.
**Skip when:** You prioritize stylistic/artistic looks over motion accuracy.

```
Strengths: Physical accuracy, motion coherence, facial animation, lip sync
Weaknesses: Less stylistic range, can look "too clean"
Best for: Product demos, talking heads, action sequences, physical interactions
```

#### Veo 3 (Google)

**Use when:** Cinematic quality matters. Premium brand videos, hero content.
**Skip when:** Budget-constrained or need fast iteration.

```
Strengths: Cinematic look, high fidelity, good prompt following, natural movement
Weaknesses: Higher cost, shorter max duration, slower generation
Best for: Brand films, premium social content, cinematic sequences, hero videos
```

#### Runway Gen-3

**Use when:** You need style control or longer clips. Artistic projects, extended sequences.
**Skip when:** You need the most natural motion or face consistency.

```
Strengths: Style diversity, longer clips (up to 16s), camera control, artistic freedom
Weaknesses: Motion can feel slightly artificial, faces sometimes drift
Best for: Artistic content, brand anthems, styled sequences, extended clips
```

#### Kling Quick

**Use when:** Speed over quality. Concept testing, storyboarding, content drafts.
**Skip when:** Final production assets or anything public-facing.

```
Strengths: Fast generation, low cost, good enough for concepts
Weaknesses: Lower quality, shorter duration, motion artifacts
Best for: Storyboarding, concept validation, draft content calendars
```

#### WAN 2.2 (Image-to-Video)

**Use when:** You have a hero still image and want to add subtle motion.
**Skip when:** You need complex motion, long duration, or text-to-video.

```
Strengths: Preserves image quality, adds natural motion, good for cinemagraph-style
Weaknesses: Limited motion range, short duration, needs quality input image
Best for: Animated social posts, subtle product motion, cinemagraph-style content
```

## Music and Audio Model Matrix

| Need | Best Model | Why | Duration | Cost |
|------|-----------|-----|----------|------|
| Background music | Suno v4 | Versatile, genre-flexible, good production | 30s-4min | Low-Medium |
| Sound effects | ElevenLabs SFX | Specific foley and effect sounds | 1-30s | Low |
| Voice narration | ElevenLabs | Natural voice synthesis, cloning available | Any | Medium |
| Jingles/branding | Suno v4 | Short, catchy, customizable genre and mood | 15-60s | Low |
| Podcast intros | Suno v4 or Udio | Full production quality, lyrics support | 15-60s | Low-Medium |

## Multi-Model Workflows

Sometimes the best result comes from chaining models. Here are proven workflows:

### Portrait Content Pipeline

```
1. Z-Image Turbo → Generate 10 concept drafts (fast, cheap)
2. Pick best composition
3. Flux 2 Dev + PuLID → Generate final with face consistency (quality)
4. 4x-UltraSharp → Upscale to print/retina resolution
```

**Total time:** ~5 min | **When:** Brand ambassador content series

### Product Photography Pipeline

```
1. Z-Image Turbo → Test compositions and angles (5-10 drafts)
2. Flux 2 Dev → Generate hero shot with winning composition
3. 4x-UltraSharp → Upscale for web/print
4. (Optional) DALL-E 3 → Add text overlay version
```

**Total time:** ~8 min | **When:** Product launch imagery

### Social Media Batch Pipeline

```
1. Define template prompts for the content calendar
2. Z-Image Turbo → Batch generate all images (fast)
3. Review and flag any that need quality upgrade
4. Flux 2 Dev → Re-generate flagged images at full quality
```

**Total time:** ~15 min for 20 images | **When:** Weekly content batch

### Video Content Pipeline

```
1. Flux 2 Dev → Generate hero still frame
2. WAN 2.2 i2v → Animate the still (subtle motion)
   OR
   Kling AI → Generate full video from text prompt
3. (Optional) Chain clips for longer sequence
```

**Total time:** ~10 min | **When:** Social video content

### Brand Consistency Pipeline

```
1. Train LoRA on brand assets (one-time, ~30 min)
2. LoRA model → Generate all brand content with trigger word
3. PuLID → Any content featuring brand ambassador face
4. Combine: LoRA style + PuLID face for maximum consistency
```

**Total time:** Ongoing | **When:** Building visual brand identity

## Cost/Speed Tier Comparison

### Budget Tier — Maximum output per dollar

```
Image: Z-Image Turbo
Video: Kling Quick
Audio: Suno v4 (basic)
Use case: Content calendars, social batches, drafts, internal content
Trade-off: Good quality, not great. Fine for ephemeral social content.
Cost estimate: ~$0.01-0.03 per image, ~$0.10-0.20 per video
```

### Standard Tier — Balanced quality and cost

```
Image: Flux 2 Klein or Flux 2 Dev
Video: Kling AI
Audio: ElevenLabs (standard)
Use case: Regular social posts, blog content, email headers, YouTube thumbnails
Trade-off: Good quality, reasonable speed, moderate cost.
Cost estimate: ~$0.03-0.08 per image, ~$0.30-0.50 per video
```

### Premium Tier — Maximum quality regardless of cost

```
Image: Flux 2 Dev + PuLID or Imagen 4
Video: Veo 3 or Kling AI (high quality mode)
Audio: ElevenLabs (professional)
Use case: Hero content, ad campaigns, brand films, print, billboards
Trade-off: Highest quality, slowest, most expensive.
Cost estimate: ~$0.08-0.20 per image, ~$0.50-2.00 per video
```

## Batch Strategy

| Content Volume | Strategy | Models | Rationale |
|---------------|----------|--------|-----------|
| 1-5 images | Premium tier | Flux 2 Dev | Low volume, maximize quality |
| 5-20 images | Standard tier | Flux 2 Klein | Balance quality and cost |
| 20-50 images | Budget + selective upgrade | Z-Image Turbo + Flux 2 Dev for heroes | Most at speed, upgrade the best |
| 50+ images | Budget tier | Z-Image Turbo | Volume demands speed and cost control |
| Video drafts | Budget tier | Kling Quick | Test concepts before committing |
| Final videos | Premium tier | Kling AI or Veo 3 | Quality matters for published video |

## Platform-Aware Recommendations

| Platform | Priority | Recommended Model | Why |
|----------|----------|-------------------|-----|
| Instagram feed | Visual quality | Flux 2 Dev or Klein | Feed images are scrutinized |
| Instagram stories | Speed (ephemeral) | Z-Image Turbo | 24h content, don't over-invest |
| X/Twitter | Speed + volume | Z-Image Turbo | Fast-paced, volume matters |
| LinkedIn | Professional quality | Flux 2 Dev | Professional audience judges quality |
| YouTube thumbnails | Clarity at small size | DALL-E 3 or Flux 2 Dev | Text rendering + high contrast |
| Blog headers | Clean composition | Flux 2 Klein | Good quality, not hero-level |
| Ad creatives | Quality + text | DALL-E 3 or Flux 2 Dev | Ads need to convert, quality matters |
| Email headers | Speed (high volume) | Z-Image Turbo | Hundreds of emails, cost matters |
| Print/billboard | Maximum quality | Flux 2 Dev + UltraSharp upscale | Print demands high resolution |
| TikTok video | Fast iteration | Kling Quick → Kling AI finals | Test concepts, polish winners |
| YouTube video | Cinematic quality | Veo 3 or Kling AI | YouTube audience expects production |

## Recommendation Output Format

Always structure your recommendation as:

```
## Model Recommendation

**Task:** [What the user wants to accomplish]
**Priority:** [Quality / Speed / Cost / Consistency]

### Primary Recommendation
**Model:** [model name]
**Why:** [2-3 sentence rationale]
**Speed:** [Fast/Medium/Slow] (~Xs per generation)
**Cost:** [Low/Medium/High] (~$X.XX per output)
**Quality:** [Good/Very Good/Excellent]

### Alternative
**Model:** [alternative model name]
**Why:** [when this would be better instead]
**Trade-off:** [what you gain vs. what you lose]

### Workflow (if multi-model)
1. [Step 1 with model]
2. [Step 2 with model]
3. [Step 3 with model]

### Settings
- [Any specific generation settings]
- [Aspect ratio, steps, CFG, etc.]
```

## Quick Reference Card

Need it fast? Use this cheat sheet:

```
Best quality image     → Flux 2 Dev
Fastest image          → Z-Image Turbo
Best text in image     → DALL-E 3
Face matching          → Flux 2 Dev + PuLID
Brand consistency      → LoRA
Daily social content   → Flux 2 Klein
Batch generation       → Z-Image Turbo
Best quality video     → Veo 3
Best motion video      → Kling AI
Fast video draft       → Kling Quick
Image-to-video         → WAN 2.2
Upscaling              → 4x-UltraSharp
Voice                  → ElevenLabs
Music                  → Suno v4
```

## Genfeed Integration

- Reference `MODEL_OUTPUT_CAPABILITIES` from `@genfeedai/constants` for supported features per model
- Use `ModelKey` enum values from `@genfeedai/enums` for precise model identification
- Check `model-aspect-ratios.constant.ts` for supported aspect ratios per model
- Model seeds in the database have `provider: 'genfeed-ai'` for self-hosted models
- ComfyUI service handles self-hosted model queuing and output retrieval
- External APIs (DALL-E, Imagen, Kling, Veo) go through their respective service integrations