Choosing the Right LLM

Selecting the right Large Language Model (LLM) is critical for your AI project’s success. This guide compares the leading models across cost, performance, and use cases to help you make an informed decision.

Model Overview

Model	Context Window	Input Cost	Output Cost	Best For
GPT-5 Turbo	128K tokens	$0.01/1K tokens	$0.03/1K tokens	Complex reasoning, code generation
Claude 3 Opus	200K tokens	$0.015/1K tokens	$0.075/1K tokens	Nuanced analysis, long documents
Claude 3 Sonnet	200K tokens	$0.003/1K tokens	$0.015/1K tokens	Balanced cost/performance
Claude 3 Haiku	200K tokens	$0.00025/1K tokens	$0.00125/1K tokens	Fast, simple queries
Llama 2 70B	4K tokens	$0 (self-hosted)	$0 (self-hosted)	Cost-sensitive, data privacy

Use Case Recommendations

Customer Support Chatbots

Recommended: Claude 3 Sonnet or GPT-5 Turbo

Why:

• Claude 3 Sonnet excels at natural conversation and maintains context well
• GPT-5 Turbo is better for complex troubleshooting and technical queries
• Both handle follow-up questions gracefully

Cost Estimate:

• 500 support queries/day × 2K avg tokens = $5-15/day ($150-450/month)

Document Analysis (Long Documents)

Recommended: Claude 3 Opus (200K context)

Why:

• Process entire documents (up to 150 pages) without chunking
• Superior at understanding nuanced language and legal/medical text
• Excellent at citation and maintaining factual accuracy

Use Cases:

• Contract review and analysis
• Medical literature review
• Legal document summarization
• Financial report analysis

Code Generation & Technical Tasks

Recommended: GPT-5 Turbo

Why:

• Stronger coding abilities than Claude (based on benchmarks)
• Better at understanding complex codebases
• Produces more syntactically correct code
• Superior debugging capabilities

Benchmark Performance (HumanEval):

• GPT-5 Turbo: 85-90% pass rate
• Claude 3 Opus: 80-85% pass rate
• Llama 2 70B: 60-65% pass rate

High-Volume, Simple Queries

Recommended: Claude 3 Haiku

Why:

• 100x cheaper than GPT-5 Turbo
• Fastest response time (<1 second)
• Sufficient for classification, extraction, simple Q&A

Use Cases:

• Sentiment analysis
• Named entity recognition
• Simple categorization
• Content moderation

Data Privacy & On-Premise Requirements

Recommended: Llama 2 70B (self-hosted)

Why:

• Complete data control—no API calls to external providers
• No per-token costs after initial infrastructure setup
• Compliance with HIPAA, GDPR, SOC2 easier to demonstrate
• Can be fine-tuned on proprietary data

Infrastructure Requirements:

• 4x A100 GPUs (80GB each) for inference
• ~400GB RAM for model loading
• Kubernetes cluster for scaling

Cost Comparison by Use Case

Example 1: E-commerce Product Description Generator

Scenario: Generate 1,000 product descriptions daily

Model	Tokens/Request	Daily Cost	Monthly Cost
GPT-5 Turbo	500 tokens	$5.00	$150
Claude 3 Sonnet	500 tokens	$1.50	$45
Claude 3 Haiku	500 tokens	$0.13	$3.75
Llama 2 70B	500 tokens	$0 (infrastructure only)	$500-1K (servers)

Recommendation: Start with Claude 3 Haiku, upgrade to Sonnet if quality insufficient

Example 2: RAG Customer Support System

Scenario: 10K support queries/month, 2K avg tokens per query

Model	Monthly Cost	Annual Cost
GPT-5 Turbo	$600	$7,200
Claude 3 Sonnet	$300	$3,600
Fine-tuned Llama 2	$200 (servers)	$2,400

Recommendation: Claude 3 Sonnet for best balance. Consider fine-tuned Llama 2 at scale.

Performance Benchmarks

Reasoning & Logic (MMLU Benchmark)

1. GPT-5 Turbo: 86.4%
2. Claude 3 Opus: 86.0%
3. Claude 3 Sonnet: 79.0%
4. Llama 2 70B: 68.9%

Context Window Utilization

1. Claude 3 (all): 200K tokens (~150 pages)
2. GPT-5 Turbo: 128K tokens (~100 pages)
3. Llama 2: 4K tokens (~3 pages)

Response Speed (p50 latency)

1. Claude 3 Haiku: <1 second
2. Claude 3 Sonnet: 2-3 seconds
3. GPT-5 Turbo: 3-5 seconds
4. Claude 3 Opus: 4-6 seconds

Hybrid Strategies

Router Pattern

Use multiple models based on query complexity:

def route_query(query: str):
    if is_simple(query):
        return claude_haiku(query)  # $0.00125/1K output
    elif requires_complex_reasoning(query):
        return gpt4_turbo(query)  # $0.03/1K output
    else:
        return claude_sonnet(query)  # $0.015/1K output

Cost Savings: 40-60% compared to always using the most expensive model

Cascade Strategy

Progress through models from cheapest to most expensive:

1. Try Haiku first (fastest, cheapest)
2. If confidence < 80%, try Sonnet
3. If still unsure, use Opus or GPT-5 Turbo

Benefits:

• Reduce costs by 50% on average
• Maintain quality where it matters
• Faster responses for simple queries

Fine-Tuning Considerations

For domain-specific applications, consider fine-tuning:

When to Fine-Tune:

• You have 10K+ high-quality examples
• Domain-specific jargon or knowledge
• Consistent output format required
• Base model performs poorly on your data

Fine-Tuning Costs:

• OpenAI fine-tuning: $0.008/1K tokens training + $0.012/1K tokens usage
• Llama 2 fine-tuning: Infrastructure costs only (requires 4-8x A100 GPUs)
• ROI typically positive at 100K+ inferences

Decision Framework

Use this flowchart to decide:

1. Data privacy required?

   • Yes → Llama 2 self-hosted
   • No → Continue to 2

2. Volume > 100K requests/month?

   • Yes → Consider fine-tuned Llama 2 for cost savings
   • No → Continue to 3

3. Query complexity:

   • Simple (classification, extraction) → Claude 3 Haiku
   • Medium (general Q&A, writing) → Claude 3 Sonnet
   • Complex (reasoning, code, analysis) → GPT-5 Turbo or Claude 3 Opus

4. Long documents (>50 pages)?

   • Yes → Claude 3 (200K context)
   • No → Any model works

Testing Checklist

Before committing to a model:

• [ ] Run 100 test queries with your actual data
• [ ] Compare quality using human evaluators (1-5 scale)
• [ ] Measure latency (p50, p95, p99)
• [ ] Calculate costs at expected volume
• [ ] Test edge cases (adversarial inputs, malformed queries)
• [ ] Evaluate guardrails (safety, content filtering)

Need help choosing? Contact our team for a free consultation and proof-of-concept testing.

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• Cost Optimization Strategies for AI Projects
• Backend Architecture Checklist for AI Applications