AI Model Selection Guide: Choosing the Right LLM

With multiple capable LLMs available, choosing the right one for your use case is crucial. Here’s a practical framework.

The Major Players (2026)

Claude (Anthropic)

Model	Best For
Opus 4.5	Complex reasoning, coding, agents
Sonnet 4	Balanced performance and cost
Haiku 4	Fast, simple tasks

GPT (OpenAI)

Model	Best For
GPT-5.2 Pro	Highest quality, critical tasks
GPT-5.2 Thinking	Complex analysis
GPT-5.2 Instant	Fast, everyday tasks
GPT-5.2 Codex	Software development

Gemini (Google)

Model	Best For
Gemini Ultra	Complex, multi-modal
Gemini Pro	General purpose
Gemini Flash	Speed-critical

Decision Framework

Step 1: Define Requirements

Task Complexity

• Simple (classification, extraction) → Smaller models
• Complex (reasoning, creativity) → Larger models

Speed Requirements

• Real-time → Fast models (Haiku, Instant, Flash)
• Batch → Accuracy over speed

Cost Sensitivity

• High volume, low margin → Smaller models
• Low volume, high value → Best available

Special Needs

• Coding → Codex, Opus 4.5
• Multi-modal → Gemini, GPT-5.2
• Long context → Check context windows

Step 2: Match to Use Case

Use Case	Recommended
Code generation	Claude Opus 4.5, GPT-5.2 Codex
Complex analysis	Claude Opus 4.5, GPT-5.2 Pro
Customer support	Claude Sonnet 4, GPT-5.2 Instant
Content creation	Claude Sonnet 4, GPT-5.2 Thinking
Data extraction	Claude Haiku 4, GPT-5.2 Instant
Multi-modal	Gemini Ultra, GPT-5.2
Real-time chat	Claude Haiku 4, Gemini Flash

Step 3: Evaluate Trade-offs

Performance
    ↑
    │ Opus 4.5 ★ GPT-5.2 Pro
    │     ★ Sonnet 4 ★ GPT-5.2 Thinking
    │         ★ Haiku 4 ★ GPT-5.2 Instant
    │
    └──────────────────────────→ Cost

Practical Comparison

Coding Tasks

Aspect	Claude Opus 4.5	GPT-5.2 Codex
Accuracy	Excellent	Excellent
Context	Very long	Long
Agents	Best-in-class	Very good
Cost	Higher	Higher

Recommendation: Claude for agents and complex projects, Codex for large-scale refactoring.

Document Analysis

Aspect	Claude	GPT-5.2
Long docs	Excellent	Very good
Accuracy	High	High
Citations	Good	Good

Recommendation: Either works well; test with your documents.

Customer Interactions

Aspect	Claude Sonnet	GPT-5.2 Instant
Speed	Fast	Very fast
Natural	Excellent	Very good
Cost	Moderate	Moderate

Recommendation: Test both; preference often subjective.

Multi-Model Strategy

Why Use Multiple Models?

1. Cost optimization: Use cheaper models for simple tasks
2. Best-of-breed: Match model strengths to tasks
3. Redundancy: Fallback if one fails
4. Comparison: A/B test for quality

Implementation Pattern

Request → Router → [ Model Selection Logic ] → Best Model
                           ↓
                   [ Classification ]
                           ↓
         Simple → Fast model (Haiku, Instant)
         Complex → Capable model (Opus, Pro)
         Coding → Specialized (Codex, Opus)

Routing Criteria

• Task type
• Content length
• Required speed
• Quality threshold
• Cost budget

Enterprise Considerations

Data Privacy

Provider	Data Training	Enterprise Options
Anthropic	Opt-out available	Enterprise tier
OpenAI	Opt-out available	Enterprise tier
Google	Configurable	Vertex AI

Compliance

• SOC 2 certification
• GDPR compliance
• HIPAA options
• Data residency

Support

• SLA guarantees
• Technical support
• Account management
• Custom solutions

Cost Optimization

Strategies

1. Right-size models: Don’t use Opus for simple tasks
2. Caching: Store common responses
3. Prompt optimization: Fewer tokens = lower cost
4. Batch processing: Volume discounts

Cost Comparison (Approximate)

Model	Input (per 1M tokens)	Output (per 1M tokens)
Haiku 4	$0.25	$1.25
Sonnet 4	$3	$15
Opus 4.5	$15	$75
GPT-5.2 Instant	~$0.30	~$1.20
GPT-5.2 Pro	~$20	~$80

Prices are approximate and may vary

Testing Framework

Before Committing

1. Benchmark: Test with representative tasks
2. Quality: Evaluate output accuracy
3. Speed: Measure latency
4. Cost: Calculate total cost of ownership
5. Integration: Test API reliability

Ongoing Evaluation

• Track performance metrics
• Monitor costs
• Re-evaluate as models update
• Test new options periodically

Future-Proofing

Abstraction Layers

Build applications that can switch models:

• Standard interface across providers
• Configuration-driven model selection
• Easy A/B testing capability

Stay Informed

• Model release announcements
• Capability improvements
• Pricing changes
• New providers

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