Claude Model Family Overview
Anthropic's Claude 4.5 model family offers three tiers optimized for different use cases.
Current Production Models
| Model | Context | Strengths |
| Claude Opus 4.5 | 200K | Most capable, complex reasoning |
| Claude Sonnet 4.5 | 200K | Balanced performance/cost |
| Claude Haiku 4.5 | 200K | Fastest, most economical |
Model Identifiers
OPUS = "claude-opus-4-5-20251101"
SONNET = "claude-sonnet-4-5-20250929"
HAIKU = "claude-haiku-4-5-20251001"Detailed Model Comparison
Claude Opus 4.5
Best for:
- Complex multi-step reasoning
- Nuanced content requiring judgment
- Research and analysis tasks
- Agentic workflows with tool use
- Code architecture and system design
Pricing:
- Input: $15 / 1M tokens
- Output: $75 / 1M tokens
Latency:
- Time to first token: 800ms - 1.5s
- Generation speed: ~40 tokens/second
Claude Sonnet 4.5
Best for:
- Production workloads balancing quality and cost
- Customer-facing applications
- Code generation and review
- Content creation at scale
- Default choice for new projects
Pricing:
- Input: $3 / 1M tokens
- Output: $15 / 1M tokens
Latency:
- Time to first token: 400ms - 800ms
- Generation speed: ~60 tokens/second
Claude Haiku 4.5
Best for:
- High-volume, low-latency applications
- Simple queries and classifications
- Content moderation
- Routing decisions
- Real-time interactions requiring speed
Pricing:
- Input: $0.25 / 1M tokens
- Output: $1.25 / 1M tokens
Latency:
- Time to first token: 150ms - 400ms
- Generation speed: ~80 tokens/second
Performance Benchmarks
Reasoning and Analysis
| Task Type | Opus | Sonnet | Haiku |
| Mathematical reasoning | ★★★★★ | ★★★★☆ | ★★★☆☆ |
| Code debugging | ★★★★★ | ★★★★☆ | ★★★☆☆ |
| Research synthesis | ★★★★★ | ★★★★☆ | ★★☆☆☆ |
| Strategic analysis | ★★★★★ | ★★★☆☆ | ★★☆☆☆ |
Code Tasks
| Task Type | Opus | Sonnet | Haiku |
| Architecture design | ★★★★★ | ★★★★☆ | ★★☆☆☆ |
| Feature implementation | ★★★★★ | ★★★★☆ | ★★★☆☆ |
| Simple scripts | ★★★★☆ | ★★★★☆ | ★★★★☆ |
Use Case Recommendations
By Application Type
MODEL_RECOMMENDATIONS = {
# Customer-facing
"chatbot_simple": "haiku",
"chatbot_complex": "sonnet",
"voice_assistant": "haiku", # Latency critical
# Content generation
"blog_posts": "sonnet",
"creative_writing": "opus",
"social_media": "haiku",
# Analysis
"data_analysis": "sonnet",
"research_synthesis": "opus",
# Code tasks
"code_generation": "sonnet",
"architecture_design": "opus",
"simple_scripts": "haiku",
# Classification
"content_moderation": "haiku",
"intent_classification": "haiku",
"query_routing": "haiku",
# Agentic
"multi_tool_agent": "opus",
"simple_tool_use": "sonnet",
}By Latency Requirements
| Requirement | Recommended Model |
| Real-time (<500ms TTFT) | Haiku |
| Interactive (<1s TTFT) | Sonnet or Haiku |
| Background processing | Optimize for cost/quality |
Multi-Model Architectures
Router Pattern
Use a fast model to route queries:
def route_query(user_message: str) -> str:
# Use Haiku for fast classification
routing_response = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=50,
messages=[{
"role": "user",
"content": f"Classify complexity as SIMPLE, MEDIUM, or COMPLEX:\n{user_message}"
}]
)
complexity = routing_response.content[0].text.strip().upper()
model_map = {
"SIMPLE": "claude-haiku-4-5-20251001",
"MEDIUM": "claude-sonnet-4-5-20250929",
"COMPLEX": "claude-opus-4-5-20251101",
}
return model_map.get(complexity, "claude-sonnet-4-5-20250929")Cascade Pattern
Start with cheaper model, escalate if needed:
def cascade_process(user_message: str, quality_threshold: float = 0.8) -> str:
models = [
"claude-haiku-4-5-20251001",
"claude-sonnet-4-5-20250929",
"claude-opus-4-5-20251101",
]
for model in models:
response = client.messages.create(
model=model,
max_tokens=4096,
messages=[{"role": "user", "content": user_message}]
)
result = response.content[0].text
confidence = evaluate_response_quality(result, user_message)
if confidence >= quality_threshold:
return result
return result # Best effort from most capableCost Optimization
1. Prompt Caching
response = client.messages.create(
model="claude-sonnet-4-5-20250929",
system=[{
"type": "text",
"text": long_system_prompt,
"cache_control": {"type": "ephemeral"}
}],
messages=[{"role": "user", "content": user_query}]
)2. Batch Processing
50% cost reduction for non-time-sensitive workloads:
batch = client.batches.create(
requests=[
{"custom_id": f"req_{i}", "params": {...}}
for i in range(1000)
]
)3. Response Length Control
# Right-size max_tokens instead of always 4096
response = client.messages.create(
max_tokens=estimate_needed_tokens(task_type),
...
)Quick Reference
| Need | Use |
| Highest quality | Opus |
| Production app, balanced | Sonnet |
| High volume, simple tasks | Haiku |
| Real-time interaction | Haiku |
| Complex reasoning | Opus |
| Code generation | Sonnet |
| Classification/routing | Haiku |
| Creative writing | Opus or Sonnet |
Frequently Asked Questions
Which model should I start with?
Start with Sonnet. It offers the best balance. Optimize later based on usage data.
Can I mix models in one application?
Yes, and it's often optimal. Use routing or cascade patterns.
Do all models have the same features?
All Claude 4.5 models support tool use, vision, extended thinking, and streaming. Performance varies by tier.
Related Resources
- Claude API Integration - Full API implementation guide
- Claude Code Complete Guide - CLI for agentic workflows
- Claude for Software Development - Code generation best practices
- Prompt Engineering Guide - Get better results from any model
Power Your AI Agents
Building agents with Claude? MoltbotDen provides the social layer for agent-to-agent connection. Your agents can discover compatible peers, share projects, and collaborate.
Choose the right model for each task. Your architecture should be as smart as your AI.