LLM Latency vs Cost: The Tradeoffs Nobody Talks About

There's a direct relationship between LLM speed and cost. Faster models are more expensive. But most applications don't need the fastest model—they need the right balance.

Let me show you how to think about this tradeoff with real data.

The Speed Hierarchy

Based on production measurements across major providers:

Ultra-Fast (< 1 second):

• Claude 3 Haiku: ~400ms average
• Gemini 1.5 Flash: ~500ms average
• GPT-3.5 Turbo: ~600ms average

Fast (1-2 seconds):

• Claude 3.5 Sonnet: ~1.1s average
• GPT-4o: ~1.4s average
• Gemini 1.5 Pro: ~1.6s average

Slow (2-4 seconds):

• Claude 3 Opus: ~2.8s average
• GPT-4 Turbo: ~3.2s average

Very Slow (5-30 seconds):

• GPT-4o with reasoning: ~8-15s average
• Claude 3 Opus (complex): ~10-20s average

Note: Times vary based on prompt length, output length, and API load. These are typical production averages.

The Cost-Speed Matrix

Let's compare cost vs speed for a standard query (1K input, 2K output):

Claude 3 Haiku:

• Speed: 400ms
• Cost: $0.0028
• Cost per second: $0.007

Claude 3.5 Sonnet:

• Speed: 1,100ms
• Cost: $0.033
• Cost per second: $0.030

GPT-4o:

• Speed: 1,400ms
• Cost: $0.035
• Cost per second: $0.025

Claude 3 Opus:

• Speed: 2,800ms
• Cost: $0.165
• Cost per second: $0.059

Key insight: Haiku delivers the best cost-per-second ratio. Opus is 8x more expensive per second of latency.

When Speed Actually Matters

Not all applications need sub-second responses. Here's how to think about it:

Real-time chat (< 1s required):

• User expectation: Instant responses
• Acceptable latency: < 1 second
• Best models: Haiku, Gemini Flash, GPT-3.5
• Cost impact: Low

Interactive applications (1-2s acceptable):

• User expectation: Quick responses
• Acceptable latency: 1-2 seconds
• Best models: Claude 3.5 Sonnet, GPT-4o
• Cost impact: Medium

Background processing (5-30s acceptable):

• User expectation: "Processing..."
• Acceptable latency: 5-30 seconds
• Best models: Any model, optimize for quality/cost
• Cost impact: Flexible

Batch processing (minutes-hours acceptable):

• User expectation: Async results
• Acceptable latency: Doesn't matter
• Best models: Cheapest that meets quality bar
• Cost impact: Optimize for cost only

The User Experience Threshold

Research shows:

• < 100ms: Feels instant
• 100-300ms: Slight delay, still feels responsive
• 300-1000ms: Noticeable delay, but acceptable
• 1-3 seconds: Feels slow, needs loading indicator
• > 3 seconds: Feels broken, users abandon

For AI applications:

• < 1s: Excellent UX, users happy
• 1-2s: Good UX, acceptable for most use cases
• 2-4s: Mediocre UX, needs progress indication
• > 4s: Poor UX, users will complain

Real-World Latency Analysis

Customer support chatbot (10K queries/day):

Option 1: Claude 3 Haiku

• Average latency: 400ms
• User satisfaction: 94%
• Monthly cost: $280

Option 2: Claude 3.5 Sonnet

• Average latency: 1,100ms
• User satisfaction: 96%
• Monthly cost: $3,300

Analysis:

• Sonnet is 2.75x slower
• Sonnet is 11.8x more expensive
• User satisfaction only 2% higher

Decision: Use Haiku. The 700ms speed advantage of Haiku outweighs Sonnet's marginal quality improvement.

Streaming: The UX Hack

Streaming responses can make slower models feel faster.

Without streaming:

• User waits 2.5 seconds
• Sees complete response at once
• Feels slow

With streaming:

• First token arrives in 300ms
• Response streams over 2.5 seconds
• Feels responsive

Implementation:

import { CostLens } from 'costlens';

const client = new CostLens({
  apiKey: process.env.COSTLENS_API_KEY,
});

const stream = await client.chat({
  messages: [...],
  stream: true,
});

for await (const chunk of stream) {
  process.stdout.write(chunk.content);
}

Result: Even slower models feel fast because users see progress immediately.

The Parallel Processing Strategy

For non-sequential tasks, run multiple cheap models in parallel instead of one expensive model.

Sequential (expensive model):

• 1 query to GPT-4o
• Latency: 1,400ms
• Cost: $0.035

Parallel (cheap models):

• 3 queries to Haiku simultaneously
• Latency: 400ms (same as single Haiku call)
• Cost: $0.0084 (3 × $0.0028)
• Benefit: Multiple perspectives, faster, cheaper

Use case: Content generation, brainstorming, A/B testing responses.

Caching: Speed AND Cost Savings

Cached responses are both instant and free (or nearly free).

First request:

• Latency: 1,100ms
• Cost: $0.033

Cached request:

• Latency: 50ms (database lookup)
• Cost: $0.0003 (Anthropic cache read) or $0 (your cache)

With 40% cache hit rate:

• Average latency: 700ms (36% faster)
• Average cost: $0.020 (39% cheaper)

Read our guide on Anthropic's prompt caching to learn how to implement this.

Implementation:

import { CostLens } from 'costlens';

const client = new CostLens({
  apiKey: process.env.COSTLENS_API_KEY,
  enableCache: true,
  cacheTTL: 3600,
});

// Automatic caching
const resp client.chat({
  messages: [...],
});

Smart Routing Based on Latency Requirements

Different queries have different latency requirements.

import { CostLens } from 'costlens';

const client = new CostLens({
  apiKey: process.env.COSTLENS_API_KEY,
  smartRouting: true,
});

// Real-time chat - prioritize speed
const chatResp client.chat({
  messages: [...],
  maxLatency: 1000, // milliseconds
});
// Routes to: Haiku or Gemini Flash

// Background analysis - prioritize quality
const analysisResp client.chat({
  messages: [...],
  maxLatency: 5000,
});
// Routes to: Sonnet or GPT-4o based on task

Monitoring Latency in Production

Track these metrics:

P50 latency: Median response time
P95 latency: 95th percentile (catches outliers)
P99 latency: 99th percentile (worst case)

Example distribution:

• P50: 800ms (most users)
• P95: 2,100ms (slow queries)
• P99: 4,500ms (edge cases)

If P95 > 3 seconds: Consider faster models or optimization.

The ROI Calculation

Question: Is it worth paying 10x more for 2x faster responses?

Framework:

1. What's your current latency?
2. What's the user impact of faster responses?
3. What's the cost difference?

Example:

• Current: 2s latency, $1,000/month
• Faster option: 1s latency, $3,000/month
• User impact: 5% higher conversion rate
• Revenue per user: $50

Calculation:

• 10,000 users/month
• 5% improvement = 500 more conversions
• Revenue gain: $25,000/month
• Cost increase: $2,000/month
• Net benefit: $23,000/month

Decision: Absolutely worth it.

The Bottom Line

For most applications:

• Use fast, cheap models (Haiku, Gemini Flash)
• Implement streaming for perceived speed
• Cache aggressively
• Reserve expensive models for complex tasks

Only use slow, expensive models when:

• Quality requirements justify the cost
• Latency isn't user-facing
• Revenue impact justifies premium pricing

The sweet spot:

• Claude 3.5 Sonnet for balanced performance
• Haiku for speed-critical applications
• Smart routing to use both strategically

Track your latency and costs with CostLens. Most teams discover they're overpaying for speed they don't need—or under-investing in speed that would drive revenue.