Choose the right AI model for every ActionFlows node. Compare providers, models, capabilities, and cost trade-offs to balance quality, speed, and budget.

Model Selection Guide

Choosing the right AI model directly impacts your workflow's cost, speed, and quality. This guide helps you select the best model for your needs.

Selection Framework

Model selection depends on four factors: cost, speed, quality, and capabilities.

Cost ─────────────────────────────── Quality
↑                                      ↑
Cheap & Fast                    Expensive & Slow
(Mistral, GPT-4o mini)          (Claude, GPT-4o)

Four Selection Criteria

1. Quality Requirements

Simple tasks (classification, routing) → Cheaper models
Complex reasoning (analysis, decisions) → Expensive models
Creative work (writing, content) → Mid-range models

2. Speed Requirements

Real-time user responses → Fast models (<2s)
Batch processing → Speed doesn't matter
Background jobs → Optimize for cost, not speed

3. Cost Constraints

1,000 runs/month → Budget $10-20
100,000 runs/month → Budget $500-2000
Millions of runs → Enterprise model needed

4. Capabilities Needed

Text only → Any model
Vision/images → Claude, GPT-4o
Long context → Claude 3.5 Sonnet (200k tokens)

Available Models

Anthropic Models

Claude Sonnet 4.5: Recommended

Latest, most intelligent frontier model. Best for: Complex agents, production workflows, coding.

  Speed: Moderate (1-3s)
  Cost: $3 / 1M input tokens
         $15 / 1M output tokens
  Quality: Frontier (best reasoning)
  Context: 200,000 tokens (1M beta available)
  Max Output: 64,000 tokens
  Capabilities: Text, Vision, Coding, Agent orchestration
  Knowledge Cutoff: January 2025

When to use:

Production AI workflows (highest accuracy)
Autonomous agent orchestration
Complex multi-step reasoning
State-of-the-art coding generation
Long-horizon agent tasks (30+ hour autonomy verified)
Computer-use automation

ActionFlows Pattern:

  Sonnet 4.5 (planner) → N × Haiku 4.5 (executors) → Sonnet 4.5 (synthesizer)
  Result: 60-70% cost reduction, same quality

Example cost:

  1,000 complex workflows/month = ~$45
  (vs $150 with basic approaches)

OpenAI Models

GPT-5 (Recommended for Most Tasks)

Flagship model released August 2025. Best for: Complex reasoning, multimodal, production workloads.

  Speed: Moderate (1-3s)
  Cost: $1.25 per 1M input tokens
         $10 per 1M output tokens
  Quality: Highest available
  Context: 272,000 tokens
  Capabilities: Text, Vision, Reasoning (with adjustable effort levels)
  Caching: 90% discount on cached input tokens ($0.125/1M)

When to use:

Production AI workflows requiring best accuracy
Complex multi-step reasoning tasks
Vision-heavy workloads
When cost-per-quality is optimized with caching

Pro Tip: Use semantic caching to reduce input token costs by ~90% on repeated prompts. For ActionFlows workflows that replay context (like chat histories), this dramatically reduces cost.

Mistral Models

Mistral Large

Best for: Cost-effective reasoning with good quality

Speed: Moderate (1-2s)
Cost: $2 per 1M input tokens
      $6 per 1M output tokens
Quality: High
Context: 32,000 tokens
Capabilities: Text, Reasoning

When to use:

Budget-conscious workflows
European data residency required
Cost-sensitive at scale
Standard reasoning tasks

Other Models

Model	Best For	Speed	Cost	Quality
DeepSeek-V3	Reasoning, code	Moderate	Low	High
Gemini 2.0 Pro	Multimodal, vision	Moderate	Medium	High
Llama 3.1	Open-source alternative	Fast	Low	Good

Quick Comparison

Rank by Cost (cheapest → most expensive):
1. Mistral 8x7B ($0.01 per 1k tokens)
2. GPT-4o mini ($0.015 per 1k tokens)
3. Claude 3.5 Haiku ($0.008 per 1k input + $0.04 output)
4. Mistral Large ($0.002 per 1k input + $0.006 output)
5. Claude 3.5 Sonnet ($0.003 per 1k input + $0.015 output)
6. GPT-4o ($0.005 per 1k input + $0.015 output)

Rank by Quality (worst → best):
1. Mistral 8x7B (good for simple tasks)
2. GPT-4o mini (solid all-arounder)
3. Claude 3.5 Haiku (fast + smart)
4. DeepSeek-V3 (strong reasoning)
5. GPT-4o (multimodal excellence)
6. Claude 3.5 Sonnet (best reasoning)

Selection by Use Case

Email Classification

Task: Read email, determine category (bug, feature, billing, general)

Best choice: GPT-4o mini or Claude 3.5 Haiku

Why:

Simple text classification (no complexity)
Speed important (users wait)
Cost matters (high volume possible)

Model: GPT-4o mini
Speed: `<500ms` per email
Cost: $0.0015 per run
Monthly (10,000 emails): $15

Content Generation

Task: Write blog posts, social captions, emails

Best choice: Claude 3.5 Sonnet

Why:

Quality paramount (content represents brand)
Speed less critical (batch process at night)
Reasoning needed (SEO, tone, structure)

Model: Claude 3.5 Sonnet
Speed: 2-3s per piece
Cost: $0.01 per piece (average)
Monthly (500 pieces): $5

Image Analysis

Task: Analyze uploaded images, extract data, describe content

Best choice: GPT-4o or Claude 3.5 Sonnet

Why:

Vision capability required
Quality important (accurate extraction)
Speed moderate

Model: GPT-4o
Speed: 1-2s per image
Cost: $0.008 per image (average)
Monthly (5,000 images): $40

High-Volume Routing

Task: Route 100,000 support tickets to right team daily

Best choice: Mistral 8x7B or GPT-4o mini

Why:

Volume critical (must be cheap)
Speed critical (real-time routing)
Quality: 95% accuracy acceptable

Model: Mistral 8x7B
Speed: `<300ms` per ticket
Cost: $0.0008 per ticket
Daily (100,000): $80
Monthly: $2,400

Complex Analysis

Task: Analyze customer sentiment, predict churn, recommend actions

Best choice: Claude 3.5 Sonnet

Why:

Complex reasoning required
Quality paramount (business decisions)
Speed not critical

Model: Claude 3.5 Sonnet
Speed: 3-5s per analysis
Cost: $0.015 per analysis
Monthly (1,000): $15

Cost Estimation

Calculate cost before deploying workflows.

Formula

Monthly Cost = 
  (Runs per month) × 
  (Average input tokens) × 
  (Input cost per token) +
  (Average output tokens) × 
  (Output cost per token)

Example Calculations

Scenario 1: Support ticket classifier

10,000 tickets/month
Input: 200 tokens average (email text)
Output: 50 tokens average (classification)

Using GPT-4o mini:
Input: 10,000 × 200 × $0.00000015 = $0.30
Output: 10,000 × 50 × $0.0000006 = $0.30
Total: $0.60/month

Scenario 2: Blog post generator

500 posts/month
Input: 300 tokens average (topic, instructions)
Output: 2,000 tokens average (blog post)

Using Claude 3.5 Sonnet:
Input: 500 × 300 × $0.000003 = $0.45
Output: 500 × 2,000 × $0.000015 = $15
Total: $15.45/month

Scenario 3: High-volume email classifier

100,000 emails/month
Input: 150 tokens average
Output: 20 tokens average

Using Mistral 8x7B:
Input: 100,000 × 150 × $0.00000025 = $3.75
Output: 100,000 × 20 × $0.00000075 = $1.50
Total: $5.25/month

Selection Decision Tree

Start
  ↓
Do you need Vision/Images?
├─ Yes → Use GPT-4o or Claude 3.5 Sonnet
├─ No → Continue
  ↓
What's your priority?
├─ Quality (accuracy matters most)
│   └─ Use Claude 3.5 Sonnet
├─ Speed (sub-second response)
│   └─ Use GPT-4o mini or Mistral 8x7B
├─ Cost (budget tight)
│   └─ Use Mistral 8x7B or GPT-4o mini
└─ Balanced → Use Claude 3.5 Haiku or GPT-4o mini

Best Practices

Model selection isn't permanent. Monitor and optimize.

1. Start Conservative

✓ Start with mid-range model (Claude 3.5 Haiku)
✓ Test quality on real data
✓ If quality good → Downgrade for cost savings
✓ If quality bad → Upgrade for better results

2. Test in Actionflow Studio

Before you scale:
1. Put the candidate models on the AI node (one at a time)
2. Run the whole flow from Actionflow Studio
3. Compare outputs, tokens, and cost on those runs
4. Keep the winner on the node

3. Monitor Quality

Track in production:
- Output accuracy
- User satisfaction
- Cost per execution
- Response time

4. Optimize Over Time

If quality drops → Upgrade model
If costs too high → Downgrade model
If speed insufficient → Use faster model

5. Use Model-Specific Features

Claude strength: Long context, reasoning → Use for analysis
GPT-4o strength: Vision, multimodal → Use for images
Mistral strength: Speed, cost → Use for routing

Context Window Considerations

Context window determines how much text a model can process at once.

Model	Context	Use
Claude 3.5 Sonnet	200,000	Process entire documents
Claude 3.5 Haiku	200,000	Process entire documents
GPT-4o	128,000	Process long documents
GPT-4o mini	128,000	Process long documents
Mistral Large	32,000	Process medium documents

✓ Analyzing 50-page document?
  Use Claude 3.5 Sonnet (200k context)

✓ Classifying 5 emails?
  Use any model (5k context enough)

✓ Summarizing book?
  Use Claude (can fit entire book)

Migration Strategy

If switching models mid-production:

Test New Model

Run both models in Actionflow Studio with sample data.

Compare Results

Model A output: "..."
Model B output: "..."

Quality acceptable? Cost lower?

Gradual Migration

Week 1: 10% traffic on new model
Week 2: 25% traffic on new model
Week 3: 50% traffic on new model
Week 4: 100% traffic on new model

Monitor Closely

Track:
- Quality metrics
- Cost difference
- Performance impact
- User feedback

Rollback Plan

If quality drops:
├─ Revert to previous model
└─ Investigate issue

Common Mistakes

Avoid these model selection errors.

Mistake	Impact	Solution
Using Claude 3.5 Sonnet for simple routing	Costs 50x more than needed	Use GPT-4o mini or Haiku
Using Mistral 8x7B for complex analysis	Poor results, wasted time	Use Claude 3.5 Sonnet
Not testing in Actionflow Studio first	Surprises in production	Test before deploying
Ignoring context window limits	Failures on long inputs	Check context window for task
Setting it and forgetting	Costs drift higher	Monitor monthly

Next Steps

Actionflow Studio: Run the graph and compare models on real outputs

Usage & Cost: Detailed pricing and estimation

AI Vendors: Connect the providers behind each model

Node Catalog: See where models are used across nodes

Choose wisely. Monitor continuously. Optimize relentlessly.

AI Model Selection Guide

Model Selection Guide

Selection Framework

Four Selection Criteria

Available Models

Anthropic Models

OpenAI Models

Mistral Models

Other Models

Quick Comparison

Selection by Use Case

Email Classification

Content Generation

Image Analysis

High-Volume Routing

Complex Analysis

Cost Estimation

Formula

Example Calculations

Selection Decision Tree

Best Practices

1. Start Conservative

2. Test in Actionflow Studio

3. Monitor Quality

4. Optimize Over Time

5. Use Model-Specific Features

Context Window Considerations

Migration Strategy

Test New Model

Compare Results

Gradual Migration

Monitor Closely

Rollback Plan

Common Mistakes

Next Steps

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