How to Choose the Right LLM for Your Use Case

Choosing the wrong LLM for your application can mean the difference between a delightful user experience and a product that's too slow, too expensive, or simply inadequate. With dozens of models available from multiple providers, how do you make the right choice?

This guide provides a practical decision framework for selecting LLMs based on real-world constraints: cost, latency, capabilities, and context requirements.

The Four Key Decision Factors

When evaluating LLMs for your use case, prioritize these four dimensions:

1. Task Complexity

What it measures: The reasoning depth and output quality required

Questions to ask:

• Does the task require multi-step reasoning?
• Is creative or nuanced output critical?
• Can mistakes be tolerated, or is accuracy paramount?
• Does the task involve domain-specific knowledge?

Model selection:

• Simple tasks (classification, extraction, formatting): Use smaller models like Claude 3 Haiku, GPT-3.5 Turbo, or Gemini Flash
• Moderate tasks (summarization, code generation, Q&A): Use mid-tier models like Claude 3.5 Sonnet or GPT-4o
• Complex tasks (research, analysis, creative writing): Use frontier models like Claude 3 Opus or GPT-4

2. Cost Constraints

What it measures: Budget per API call and total volume

Questions to ask:

• What's your per-request budget?
• How many requests per day/month?
• Is this user-facing or internal tooling?
• Can you cache system prompts or responses?

Pricing comparison (approximate, as of 2025):

Cost optimization strategies:

• Cache system prompts (Anthropic supports prompt caching)
• Use smaller models for routing/classification, larger models for core tasks
• Implement request deduplication
• Stream responses to reduce perceived latency

3. Latency Requirements

What it measures: Time from request to first token (TTFT) and completion

Questions to ask:

• Is this user-facing or background processing?
• Can you stream responses, or need complete response?
• What's acceptable wait time for your users?
• Can you parallelize requests?

Latency characteristics:

// Example: Measuring LLM response times
interface LLMMetrics {
  model: string;
  timeToFirstToken: number; // ms
  tokensPerSecond: number;
  totalDuration: number; // ms
}

async function benchmarkLLM(
  apiCall: () => Promise<Response>
): Promise<LLMMetrics> {
  const start = performance.now();
  let firstTokenTime: number | null = null;
  let tokenCount = 0;

  const stream = await apiCall();
  const reader = stream.body.getReader();

  while (true) {
    const { done, value } = await reader.read();
    if (done) break;

    if (firstTokenTime === null) {
      firstTokenTime = performance.now() - start;
    }
    tokenCount += countTokens(value);
  }

  const totalDuration = performance.now() - start;

  return {
    model: 'claude-3-haiku',
    timeToFirstToken: firstTokenTime,
    tokensPerSecond: (tokenCount / totalDuration) * 1000,
    totalDuration,
  };
}

Latency optimization:

• Use streaming for user-facing applications
• Choose smaller models when latency is critical
• Consider edge deployment for lower network latency
• Implement speculative execution for predictable queries

4. Context Window Size

What it measures: Maximum input tokens the model can process

Questions to ask:

• How much context do you need to provide?
• Are you processing long documents or code files?
• Do you need conversation history?
• Can you chunk and summarize, or need full context?

Context window comparison:

Decision Framework: A Practical Example

Let's apply this framework to a real scenario:

Use case: AI-powered comment moderation system

Requirements:

• Classify comments as safe/spam/toxic
• Must respond in <500ms
• Processing 10,000 comments/day
• Need high accuracy to avoid false positives

Decision process:

1. Task complexity: Moderate (classification with nuance)
2. Cost constraint: $50/month budget = $0.005/request
3. Latency requirement: Critical (<500ms)
4. Context size: Small (single comment, <500 tokens)

Recommended model: Claude 3 Haiku

Reasoning:

• Fast enough for <500ms requirement
• Cost: ~$0.0003/request (well under budget)
• Sufficient accuracy for classification
• Small context window is adequate

Implementation:

import Anthropic from '@anthropic-ai/sdk';

const anthropic = new Anthropic({
  apiKey: process.env.ANTHROPIC_API_KEY,
});

interface ModerationResult {
  category: 'safe' | 'spam' | 'toxic';
  confidence: number;
  reasoning: string;
}

async function moderateComment(
  comment: string
): Promise<ModerationResult> {
  const startTime = performance.now();

  const message = await anthropic.messages.create({
    model: 'claude-3-haiku-20240307',
    max_tokens: 200,
    temperature: 0, // Deterministic for classification
    messages: [
      {
        role: 'user',
        content: `Classify this comment as safe, spam, or toxic. Respond in JSON format with category, confidence (0-1), and brief reasoning.

Comment: "${comment}"`,
      },
    ],
  });

  const duration = performance.now() - startTime;
  console.log(`Moderation completed in ${duration}ms`);

  const response = JSON.parse(message.content[0].text);
  return response;
}

// Example usage
const result = await moderateComment(
  'Great article! Very insightful.'
);
// => { category: 'safe', confidence: 0.95, reasoning: 'Positive feedback' }

Advanced Considerations

Model Routing

For complex applications, route requests to different models based on characteristics:

interface ModelRouter {
  route(task: Task): LLMConfig;
}

class SmartModelRouter implements ModelRouter {
  route(task: Task): LLMConfig {
    // Simple classification → fast, cheap model
    if (task.type === 'classification' && task.urgency === 'high') {
      return { model: 'claude-3-haiku', maxTokens: 100 };
    }

    // Code generation → capable model with streaming
    if (task.type === 'code-generation') {
      return { model: 'claude-3-5-sonnet', maxTokens: 4096, stream: true };
    }

    // Research/analysis → frontier model
    if (task.complexity === 'high') {
      return { model: 'claude-3-opus', maxTokens: 8192 };
    }

    // Default to balanced option
    return { model: 'claude-3-5-sonnet', maxTokens: 1024 };
  }
}

Prompt Caching

Many providers now support prompt caching, dramatically reducing costs for repeated system prompts:

// Anthropic prompt caching example
const response = await anthropic.messages.create({
  model: 'claude-3-5-sonnet-20241022',
  max_tokens: 1024,
  system: [
    {
      type: 'text',
      text: 'You are an expert code reviewer...', // Cached
      cache_control: { type: 'ephemeral' },
    },
  ],
  messages: [{ role: 'user', content: 'Review this PR...' }],
});
// Subsequent requests with same system prompt: 90% cost reduction on cached portion

Fallback Strategies

Always implement fallback logic for production systems:

async function generateWithFallback(prompt: string): Promise<string> {
  try {
    // Try primary model
    return await generateWithClaude(prompt);
  } catch (error) {
    console.error('Primary model failed:', error);

    try {
      // Fallback to alternative provider
      return await generateWithOpenAI(prompt);
    } catch (fallbackError) {
      console.error('Fallback failed:', fallbackError);

      // Final fallback: return error message
      return 'Unable to generate response. Please try again.';
    }
  }
}

Key Takeaways

1. Match model to task complexity: Don't overpay for capability you don't need
2. Consider total cost, not just per-token pricing: Volume matters
3. Latency compounds: User-facing features need fast models
4. Context window rarely matters for most applications (unless you're processing books)
5. Test before committing: Run benchmarks with real data
6. Build flexibility: Model landscape changes rapidly

Explore Our Benchmark Comparisons

Want to dive deeper into LLM performance metrics? Check out our comprehensive benchmark database for detailed comparisons across models, tasks, and providers.

Have questions about LLM selection for your specific use case? Drop a comment below.

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