Claude 4.5 Haiku vs. Sonnet: The Ultimate Tool-Use Comparison
Claude 4.5 Haiku or Sonnet for AI agents? Compare latency, accuracy, and cost. Learn which model is optimal for which use case—with real-world examples and performance benchmarks.
Anewera
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Executive Summary: Choosing between Claude 4.5 Haiku and Sonnet affects cost, speed, and accuracy. This comprehensive comparison analyzes both models across tool-use performance, latency, pricing, and real-world use cases. Haiku excels at simple, high-frequency tasks with 3x speed and 15x cost advantage. Sonnet dominates complex multi-tool workflows with 40% higher accuracy. Based on 50,000+ production runs at Anewera, we recommend: Haiku for routine automation (70% of tasks), Sonnet for complex reasoning (30% of tasks). Strategic model routing delivers best results at optimal cost.
The Claude 4.5 Family: Speed vs. Power
Anthropic offers two production models in the Claude 4.5 family:
Haiku: The speed demon
Sonnet: The powerhouse
Both support tool use. Both are excellent. But for different reasons.
What Is Tool Use?
Tool use (also called "function calling") enables LLMs to interact with external systems.
Example:
User: "What's the weather in London?"
Without tool use:
LLM: "I don't have access to real-time weather data."
With tool use:
LLM → Calls get_weather(city="London") → Returns "15°C, cloudy"
For AI agents, tool use is essential — it's how they act on the world.
Haiku: The Speed Demon
Performance Metrics
Speed:
- Latency: 0.8-1.2s (average)
- Tokens/second: 120-150
- Tool-call overhead: +200ms
Tool-Use Accuracy:
- Simple calls: 94%
- Nested calls: 81%
- Multi-tool orchestration: 68%
Cost:
- Input: $0.0008/1K tokens
- Output: $0.004/1K tokens
- 15x cheaper than Sonnet
Best Use Cases for Haiku
✅ High-frequency, simple tasks:
- Email classification ("Spam or not?")
- Data extraction ("Pull name and email from text")
- Quick calculations ("Convert 100 USD to EUR")
- Sentiment analysis ("Is this review positive?")
✅ Budget-sensitive workloads:
- 10,000+ API calls/day
- Cost matters more than perfection
- Acceptable accuracy: 90-95%
✅ Speed-critical applications:
- Real-time chat responses
- Live customer support
- Interactive demos
Anewera uses Haiku for:
- Classifying user intents (80K calls/day)
- Extracting structured data from emails
- Quick lead scoring
Sonnet: The Powerhouse
Performance Metrics
Speed:
- Latency: 2.5-4.0s (average)
- Tokens/second: 80-100
- Tool-call overhead: +300ms
Tool-Use Accuracy:
- Simple calls: 96%
- Nested calls: 97%
- Multi-tool orchestration: 95%
Cost:
- Input: $0.003/1K tokens
- Output: $0.015/1K tokens
- Premium pricing for premium quality
Best Use Cases for Sonnet
✅ Complex, multi-tool workflows:
- "Research company, draft email, create landing page, deploy it"
- 5-10 tools orchestrated in sequence
- Each step informs the next
✅ High-stakes decisions:
- Financial analysis
- Legal document review
- Medical triage
- Errors = expensive
✅ Creative tasks:
- Marketing copy (nuanced tone)
- Code generation (complex algorithms)
- Strategic planning
Anewera uses Sonnet for:
- Building landing pages (15-20 tool calls)
- Complex research reports (multi-source synthesis)
- Agent-to-agent orchestration
Head-to-Head Comparison
Benchmark: 10,000 Tool-Use Tasks
| Metric | Haiku | Sonnet | Winner |
|---|---|---|---|
| Simple Tool Call Accuracy | 94% | 96% | Sonnet +2% |
| Nested Tool Call Accuracy | 81% | 97% | Sonnet +16% |
| Multi-Tool Accuracy | 68% | 95% | Sonnet +27% |
| Average Latency | 1.0s | 3.2s | Haiku 3.2x faster |
| Cost per 1K tokens | $0.004 | $0.015 | Haiku 3.75x cheaper |
| Error Recovery | 72% self-fix | 89% self-fix | Sonnet +17% |
Real-World Anewera Examples
Use Case 1: Email Classification (Haiku Wins)
Task: "Is this email spam, support, or sales inquiry?"
- Haiku: 0.9s, 95% accuracy, $0.0008
- Sonnet: 2.8s, 96% accuracy, $0.003
Verdict: Haiku. 1% accuracy gain not worth 3x cost + 3x latency.
Use Case 2: Build Landing Page (Sonnet Wins)
Task: "Research target audience, write copy, design layout, generate code, deploy"
- Haiku: 8 min, 68% fully working, $0.40
- Sonnet: 12 min, 92% fully working, $1.20
Verdict: Sonnet. 24% higher success rate worth 3x cost (failed pages cost more to fix).
Total Cost of Ownership (TCO)
Scenario: 30K Agent Runs/Month
Agent Type A: Simple Lead Qualification
- 3 tool calls avg
- 2K tokens avg
Haiku:
- Cost: 30K × $0.012 = $360/month
- Accuracy: 94%
- Failed runs: 1,800 (need manual review)
Sonnet:
- Cost: 30K × $0.045 = $1,350/month
- Accuracy: 96%
- Failed runs: 1,200
Verdict: Haiku. Savings: $990/month.
Agent Type B: Complex Multi-Tool Workflow
- 15 tool calls avg
- 8K tokens avg
Haiku:
- Cost: 30K × $0.048 = $1,440/month
- Accuracy: 68%
- Failed runs: 9,600 (costly to fix)
Sonnet:
- Cost: 30K × $0.180 = $5,400/month
- Accuracy: 95%
- Failed runs: 1,500
Verdict: Sonnet. Failed runs cost more than model premium.
Decision Framework
Use Haiku when:
- ✅ Task is simple (1-3 tool calls)
- ✅ High volume (10K+ per day)
- ✅ Speed matters
- ✅ 90-95% accuracy acceptable
- ✅ Errors are cheap to fix
Use Sonnet when:
- ✅ Task is complex (5+ tool calls)
- ✅ Accuracy critical (> 95%)
- ✅ Multi-step reasoning required
- ✅ Errors are expensive
- ✅ Creative/strategic output needed
Use Both (Routing):
- Classify task complexity
- Simple → Haiku
- Complex → Sonnet
- 70% cost savings vs. all-Sonnet
Frequently Asked Questions (FAQ)
Can I use both models in the same agent workflow?
Yes! Smart routing: Use Haiku for initial classification/filtering, then Sonnet for complex follow-up. Example: Haiku scores 100 leads (fast, cheap) → Sonnet drafts personalized emails for top 10 (quality matters).
How do I know which model my task needs?
Test both. Run 100 sample tasks through each model. Measure: (1) Accuracy, (2) Cost, (3) Latency. If Haiku achieves 90%+ accuracy, use it. If not, Sonnet is worth the premium.
Does Haiku support the same tools as Sonnet?
Yes, both support identical tool-use capabilities. The difference is reasoning quality, not tool compatibility.
What about Claude Opus?
Opus (the largest Claude model) offers even higher quality but at 5x Sonnet's cost. Use for extremely complex, high-stakes tasks only. Most businesses find Sonnet sufficient.
Can I switch models mid-workflow?
Technically yes, but tricky (context transfer overhead). Better: Use one model per agent run, route at the start based on task classification.
How often does Anthropic update these models?
Major releases: 6-12 months. Minor updates: Continuous (without version changes). Performance improves over time without price increases.
Is there a Haiku vs. Sonnet latency difference in tool use specifically?
Yes. Haiku's tool-call overhead is ~200ms, Sonnet's ~300ms. For workflows with 20 tool calls, this adds up: Haiku saves 2 seconds total.
Conclusion: Choose Based on Your Needs, Not Hype
Summary:
✅ Haiku: 3x faster, 15x cheaper, 90-95% accuracy—ideal for high-volume, simple tasks
✅ Sonnet: 40% more accurate on complex tasks, better reasoning, worth the premium for critical workflows
✅ Strategic routing: 70% Haiku + 30% Sonnet = optimal cost/quality balance
At Anewera, we use both—intelligently routed based on task complexity. This delivers production-grade quality at sustainable costs.
The best model is the one that fits your use case.
Ready to optimize your AI agent costs? Contact Anewera