Model Selection Guide - Noxus Documentation

Selecting the appropriate AI model is crucial for optimizing workflow performance and cost. This guide helps you choose the right model based on task complexity, latency requirements, and budget constraints.

Model Comparison Overview

Model Tier	Example Models	Speed	Quality	Cost	Best For
Fast	GPT-3.5 Turbo, Claude Haiku	⚡️⚡️⚡️	⭐️⭐️	💰	Simple tasks, high volume, quick responses
Balanced	GPT-4o-mini, Claude Sonnet	⚡️⚡️	⭐️⭐️⭐️⭐️	💰💰	General purpose, most workflows
Quality	GPT-4, Claude Opus	⚡️	⭐️⭐️⭐️⭐️⭐️	💰💰💰	Complex reasoning, critical accuracy
Specialist	GPT-4 Vision, Whisper	⚡️⚡️	⭐️⭐️⭐️⭐️	💰💰	Multimodal tasks (images, audio)

Available Models

OpenAI Models

GPT-4 Turbo (gpt-4-turbo)

Strengths: Latest GPT-4, large context window (128K tokens), strong reasoning
Use Cases: Complex analysis, long documents, advanced reasoning
Speed: Moderate
Cost: High

GPT-4 (gpt-4)

Strengths: Most capable OpenAI model, excellent reasoning, reliable
Use Cases: Critical decisions, complex problems, high-stakes outputs
Speed: Slower
Cost: Highest

GPT-4o (gpt-4o)

Strengths: Optimized for speed, multimodal (text + images), balanced performance
Use Cases: General purpose, image analysis, real-time applications
Speed: Fast
Cost: Moderate

GPT-4o-mini (gpt-4o-mini)

Strengths: Cost-effective, fast, good for most tasks
Use Cases: General purpose, high-volume workflows, cost-conscious applications
Speed: Very Fast
Cost: Low

GPT-3.5 Turbo (gpt-3.5-turbo)

Strengths: Very fast, lowest cost, good for simple tasks
Use Cases: Simple generation, classification, high-volume processing
Speed: Fastest
Cost: Lowest

Anthropic Models

Claude Opus 4.8 (claude-4.8-opus)

Strengths: Highest capability, excellent reasoning, nuanced understanding
Use Cases: Complex analysis, creative writing, detailed explanations
Speed: Slower
Cost: Highest

Claude Sonnet 4.6 (claude-4.6-sonnet)

Strengths: Best balance of speed and intelligence, excellent for most tasks
Use Cases: General purpose, workflows requiring both speed and quality
Speed: Fast
Cost: Moderate

Claude Haiku 4.5 (claude-4.5-haiku)

Strengths: Fastest Claude model, cost-effective, reliable
Use Cases: Simple tasks, high-volume processing, quick responses
Speed: Fastest
Cost: Lowest

Specialized Models

GPT-4 Vision (gpt-4-vision-preview)

Strengths: Understands images, extract text from screenshots, analyze diagrams
Use Cases: Image analysis, OCR, visual content understanding
Speed: Moderate
Cost: Moderate-High

Whisper (whisper-1)

Strengths: Speech-to-text, multilingual, accurate transcription
Use Cases: Transcribe audio, voice notes, meeting recordings
Speed: Fast
Cost: Low (per minute of audio)

Selection Decision Tree

Question 1: Does your task involve images or audio?

Yes → Use Specialist Models

Images: GPT-4 Vision, GPT-4o (has vision), Claude Sonnet 3.5 (has vision)
Audio: Whisper
Both: GPT-4o (multimodal)

No → Continue to Question 2

Question 2: How complex is the reasoning required?

Simple (Classification, extraction, simple generation):

Use Fast Models: GPT-3.5 Turbo, Claude Haiku, GPT-4o-mini

Moderate (Analysis, summarization, most workflows):

Use Balanced Models: GPT-4o-mini, Claude Sonnet 3.5, GPT-4o

Complex (Multi-step reasoning, critical decisions, creative tasks):

Use Quality Models: GPT-4, Claude Opus, GPT-4 Turbo

Question 3: What are your latency requirements?

Real-time (<1 second response needed):

GPT-3.5 Turbo, Claude Haiku

Interactive (1-3 seconds acceptable):

GPT-4o-mini, GPT-4o, Claude Sonnet 3.5

Batch (no urgency, can wait):

Any model (optimize for quality or cost)

Question 4: What’s your volume and budget?

High Volume, Cost-Sensitive:

GPT-3.5 Turbo, Claude Haiku, GPT-4o-mini

Moderate Volume, Balanced:

GPT-4o-mini, Claude Sonnet 3.5, GPT-4o

Low Volume, Quality-Critical:

GPT-4, Claude Opus, GPT-4 Turbo

Task-Specific Recommendations

Text Classification

Task: Categorize emails, support tickets, documents Recommended Models:

GPT-4o-mini (Best choice)
GPT-3.5 Turbo (Cheaper)
Claude Haiku (Alternative)

Why: Simple task, fast inference, high volume typical Example:

Classify Email:
  Model: GPT-4o-mini
  Prompt: "Classify this email as: Urgent, Normal, or Low Priority"
  Temperature: 0.3 (deterministic)

Data Extraction

Task: Extract invoice details, resume parsing, form processing Recommended Models:

GPT-4o-mini (Best choice)
Claude Sonnet 3.5 (More accurate)
GPT-4o (With images)

Why: Structured output benefits from reliable models, moderate complexity Example:

Extract Invoice Data:
  Model: GPT-4o-mini
  Use: Structured Output node
  Schema: {invoice_number, date, vendor, amount, items}
  Temperature: 0.0 (deterministic)

Content Generation

Task: Write emails, social media posts, product descriptions Simple Content:

GPT-3.5 Turbo, GPT-4o-mini

Quality Content:

Claude Sonnet 3.5, GPT-4o

Creative Content:

Claude Opus, GPT-4

Example:

Generate Product Description:
  Model: Claude Sonnet 3.5
  Temperature: 0.7 (creative)
  Style: "Professional, engaging, highlights benefits"

Summarization

Short Summaries (tweets, headlines):

GPT-3.5 Turbo, GPT-4o-mini

Standard Summaries (paragraphs):

GPT-4o-mini, Claude Sonnet 3.5

Long Document Summaries (reports, articles):

GPT-4 Turbo (128K context), Claude Opus

Example:

Summarize Article:
  Model: GPT-4o-mini (articles &lt;20K tokens)
  Model: GPT-4 Turbo (long articles >20K tokens)
  Temperature: 0.3

Question Answering

Simple FAQ:

GPT-3.5 Turbo, GPT-4o-mini

Knowledge Base Search:

Claude Sonnet 3.5, GPT-4o

Complex Analysis:

GPT-4, Claude Opus

Example:

Answer Customer Question:
  Knowledge Base Search (retrieves context)
    ↓
  AI Answer:
    Model: Claude Sonnet 3.5
    Context: ((kb_results))
    Temperature: 0.2

Code Generation

Simple Scripts:

GPT-4o-mini, Claude Sonnet 3.5

Complex Programs:

GPT-4, Claude Opus

Code Review:

GPT-4, Claude Sonnet 3.5

Example:

Generate Python Script:
  Model: GPT-4o-mini (simple scripts)
  Model: Claude Opus (complex logic)
  Temperature: 0.5

Translation

Simple Translation:

GPT-3.5 Turbo, GPT-4o-mini

Nuanced Translation:

GPT-4, Claude Opus

Example:

Translate Text:
  Model: GPT-4o-mini
  Prompt: "Translate to French, maintain tone and formality"
  Temperature: 0.3

Sentiment Analysis

Recommended Models:

GPT-4o-mini (Best choice)
GPT-3.5 Turbo (Cheaper)
Claude Haiku (Alternative)

Example:

Analyze Sentiment:
  Model: GPT-4o-mini
  Output: [Positive, Neutral, Negative]
  Temperature: 0.2

Image Analysis

Recommended Models:

GPT-4o (Best balance)
GPT-4 Vision (Higher quality)
Claude Sonnet 3.5 (With vision)

Example:

Analyze Product Image:
  Model: GPT-4o
  Prompt: "Describe this product. Identify brand, condition, notable features."

Audio Transcription

Recommended Model: Whisper (only option) Example:

Transcribe Meeting:
  Model: Whisper
  Language: English (auto-detect available)
  Output: transcript text

Model Parameters

Temperature

Controls randomness/creativity: Temperature: 0.0-0.3 (Deterministic)

Use for: Classification, extraction, factual Q&A
Result: Consistent, predictable outputs

Temperature: 0.5-0.7 (Balanced)

Use for: General content generation, summaries
Result: Natural, varied, but reliable

Temperature: 0.8-1.0 (Creative)

Use for: Creative writing, brainstorming, unique content
Result: Diverse, creative, less predictable

Examples:

Data Extraction: temperature = 0.0
Email Response: temperature = 0.6
Creative Writing: temperature = 0.9

Max Tokens

Limits output length: Short Outputs (50-200 tokens):

Classifications, short summaries, single-sentence responses

Medium Outputs (200-1000 tokens):

Paragraphs, emails, standard summaries

Long Outputs (1000-4000 tokens):

Articles, detailed reports, comprehensive analyses

Recommendation: Set max_tokens slightly higher than expected to avoid truncation.

Top P (Nucleus Sampling)

Alternative to temperature (rarely adjusted): Top P: 1.0 (Default)

Use temperature instead

Top P: 0.9

Slightly more focused than temperature
Use for balanced creativity

Recommendation: Stick with temperature for most cases.

Cost Optimization Strategies

Strategy 1: Model Cascading

Use cheap models first, upgrade if needed:

Try GPT-3.5 Turbo (fast, cheap)
    ↓
Evaluate confidence or quality
    ↓
Conditional: confidence < 0.7
    └─ True → Retry with GPT-4 (expensive, accurate)

Example:

Classify Document:
  Model: GPT-4o-mini
  Output: category, confidence
    ↓
Conditional: confidence < 0.8
    └─ True → Reclassify with GPT-4
        ↓
        Model: GPT-4
        Output: final_category (high confidence)

Strategy 2: Batch Processing

Process multiple items in single request: Instead of:

For each email (1000 emails):
    Classify email → 1000 API calls

Use:

Batch 10 emails per request:
    Classify batch → 100 API calls (10x cheaper)

Strategy 3: Caching

Cache results for repeated inputs:

Check Cache: question hash
    ↓
Conditional: cache hit
    ├─ True → Return cached answer (free)
    └─ False → Call LLM, cache result

Strategy 4: Prompt Optimization

Reduce input tokens: Bad (wasteful):

Prompt: "I want you to please take a look at the following email and tell me if you think it's spam or not spam. Here is the email: ..."
Input Tokens: 30+

Good (efficient):

Prompt: "Classify as spam or not spam:\n\n((email))"
Input Tokens: 8

Strategy 5: Right-Size Model

Don’t use GPT-4 for simple tasks: Overpowered:

GPT-4 for "Extract email from text"
Cost: $0.30 per 1000 inputs

Right-Sized:

GPT-4o-mini for "Extract email from text"
Cost: $0.015 per 1000 inputs (20x cheaper)

Performance Optimization

Latency Optimization

Use Faster Models:

GPT-3.5 Turbo: ~500ms response
GPT-4o-mini: ~800ms response
GPT-4: ~2-4s response

Parallel Processing:

List of 100 items
    ↓
Automatic Iteration (parallel):
    Process 10 items concurrently
    Result: 10x faster than sequential

Reduce Input Size:

Long Document (50K tokens)
    ↓
Summarize First (1K tokens)
    ↓
Analyze Summary (faster)

Quality Optimization

Use Higher-Tier Models:

Upgrade GPT-3.5 → GPT-4o-mini
Upgrade GPT-4o-mini → GPT-4

Increase Context:

Provide More Context:
  - Relevant examples
  - Clear instructions
  - Domain knowledge

Structured Output:

Use Structured Output Node:
  - Forces valid JSON
  - Reduces errors
  - More reliable extraction

Testing Different Models

A/B Testing Pattern

Compare models on same task:

Split Traffic: 50/50
    ↓
Branch A: GPT-4o-mini
    Process with Model A
    Log: model="gpt-4o-mini", quality, latency, cost
    ↓
Branch B: Claude Sonnet 3.5
    Process with Model B
    Log: model="claude-sonnet", quality, latency, cost
    ↓
Merge Results
    ↓
Analyze:
    - Quality comparison
    - Speed comparison
    - Cost comparison

Evaluation Workflow

Test models on sample data:

Load Test Dataset (100 examples)
    ↓
For Each Model:
    Process all examples
    ↓
    Evaluate Results:
        - Accuracy
        - Average latency
        - Total cost
        - Error rate
    ↓
Compare Models:
    Model A: 94% accurate, 800ms avg, $0.50 total
    Model B: 96% accurate, 1.2s avg, $2.00 total
    ↓
Decision: Choose Model A (good enough, 4x cheaper)

Model-Specific Tips

OpenAI Models

GPT-4 Turbo:

Use for long context (>8K tokens input)
Best for complex multi-step reasoning
Enable JSON mode for structured output

GPT-4o:

Use for multimodal (text + images)
Faster than GPT-4, almost same quality
Good for real-time applications

GPT-3.5 Turbo:

Use for simple, high-volume tasks
Set temperature=0 for consistency
Great for classification, simple extraction

Anthropic Models

Claude Opus:

Best for nuanced understanding
Excellent for creative tasks
Strong reasoning capabilities

Claude Sonnet 3.5:

Best all-around model
Fast enough for interactive use
High quality at reasonable cost

Claude Haiku:

Fastest Anthropic model
Good for simple tasks
Cost-effective for high volume

Common Pitfalls

Pitfall 1: Using GPT-4 for Everything

Problem: Overspending on simple tasks Solution: Start with GPT-4o-mini, upgrade only if needed

Pitfall 2: Temperature Too High for Extraction

Problem: Inconsistent structured outputs Solution: Use temperature=0 for deterministic tasks

Pitfall 3: Wrong Model for Images

Problem: Using text-only model for image analysis Solution: Use GPT-4o, GPT-4 Vision, or Claude Sonnet 3.5 (with vision)

Pitfall 4: Not Testing Alternatives

Problem: Missing better/cheaper options Solution: Test multiple models on your specific use case

Best Practices

Start Cheap: Begin with GPT-4o-mini or Claude Haiku, upgrade if needed Measure Performance: Track quality, latency, and cost metrics Right Model for Task: Match model capability to task complexity Use Structured Output: For extraction tasks, use Structured Output nodes Set Temperature Appropriately: 0 for deterministic, 0.7 for creative Test on Real Data: Evaluate models on your actual use cases Monitor Costs: Check model cost per workflow in the flow’s Analytics tab. Optimize Prompts: Reduce unnecessary tokens in prompts Cache When Possible: Cache repeated queries Update Regularly: New models released frequently, reevaluate choices

Choosing the right model balances quality, speed, and cost to optimize your workflows. Start with balanced models and adjust based on results.

Text Generation

Generate text with AI models

Structured Outputs

Extract structured data reliably

AI Operations

Understand AI Operations billing

Vision & Multimodal

Work with images and multimodal AI

​Model Comparison Overview

​Available Models

​OpenAI Models

​Anthropic Models

​Specialized Models

​Selection Decision Tree

​Question 1: Does your task involve images or audio?

​Question 2: How complex is the reasoning required?

​Question 3: What are your latency requirements?

​Question 4: What’s your volume and budget?

​Task-Specific Recommendations

​Text Classification

​Data Extraction

​Content Generation

​Summarization

​Question Answering

​Code Generation

​Translation

​Sentiment Analysis

​Image Analysis

​Audio Transcription

​Model Parameters

​Temperature

​Max Tokens

​Top P (Nucleus Sampling)

​Cost Optimization Strategies

​Strategy 1: Model Cascading

​Strategy 2: Batch Processing

​Strategy 3: Caching

​Strategy 4: Prompt Optimization

​Strategy 5: Right-Size Model

​Performance Optimization

​Latency Optimization

​Quality Optimization

​Testing Different Models

​A/B Testing Pattern

​Evaluation Workflow

​Model-Specific Tips

​OpenAI Models

​Anthropic Models

​Common Pitfalls

​Pitfall 1: Using GPT-4 for Everything

​Pitfall 2: Temperature Too High for Extraction

​Pitfall 3: Wrong Model for Images

​Pitfall 4: Not Testing Alternatives

​Best Practices

Text Generation

Structured Outputs

AI Operations

Vision & Multimodal

Model Comparison Overview

Available Models

OpenAI Models

Anthropic Models

Specialized Models

Selection Decision Tree

Question 1: Does your task involve images or audio?

Question 2: How complex is the reasoning required?

Question 3: What are your latency requirements?

Question 4: What’s your volume and budget?

Task-Specific Recommendations

Text Classification

Data Extraction

Content Generation

Summarization

Question Answering

Code Generation

Translation

Sentiment Analysis

Image Analysis

Audio Transcription

Model Parameters

Temperature

Max Tokens

Top P (Nucleus Sampling)

Cost Optimization Strategies

Strategy 1: Model Cascading

Strategy 2: Batch Processing

Strategy 3: Caching

Strategy 4: Prompt Optimization

Strategy 5: Right-Size Model

Performance Optimization

Latency Optimization

Quality Optimization

Testing Different Models

A/B Testing Pattern

Evaluation Workflow

Model-Specific Tips

OpenAI Models

Anthropic Models

Common Pitfalls

Pitfall 1: Using GPT-4 for Everything

Pitfall 2: Temperature Too High for Extraction

Pitfall 3: Wrong Model for Images

Pitfall 4: Not Testing Alternatives

Best Practices