Anthropic 5.5 Architecture & Benchmarks: Haiku 5.5 vs Sonnet 5.5 vs Opus 5.5 vs Fable 5.1

import os
from typing import Literal, Dict, Any
from anthropic import Anthropic

client = Anthropic(api_key=os.environ.get("ANTHROPIC_API_KEY"))

MODEL_REGISTRY = {
    "triage": "claude-haiku-5-5-20261001",
    "schema": "claude-fable-5-1-20261001",
    "coding": "claude-sonnet-5-5-20261001",
    "deep_reasoning": "claude-opus-5-5-20261001",
}

def route_and_execute_task(task_prompt: str, complexity_tier: Literal["triage", "schema", "coding", "deep_reasoning"]) -> str:
    """
    Executes tasks using the appropriate Anthropic 5.5 / 5.1 model tier
    with automated prompt caching and adaptive thinking budgets.
    """
    model_name = MODEL_REGISTRY[complexity_tier]
    
    # Configure extended thinking budget for deep reasoning
    extra_params: Dict[str, Any] = {}
    if complexity_tier == "deep_reasoning":
        extra_params["thinking"] = {
            "type": "enabled",
            "budget_tokens": 16000
        }
    
    response = client.messages.create(
        model=model_name,
        max_tokens=8192 if complexity_tier != "deep_reasoning" else 24000,
        system=[
            {
                "type": "text",
                "text": "You are a specialized OutClaw systems architect adhering strictly to typed contracts.",
                "cache_control": {"type": "ephemeral"}  # 90% prompt caching read discount
            }
        ],
        messages=[
            {
                "role": "user",
                "content": task_prompt
            }
        ],
        **extra_params
    )
    
    # Extract plain text response blocks
    text_blocks = [block.text for block in response.content if block.type == "text"]
    return "\n".join(text_blocks)

# Example Execution
if __name__ == "__main__":
    spec = "Generate OpenAPI 3.1 specification for an agentic telemetry endpoint."
    schema_output = route_and_execute_task(spec, complexity_tier="schema")
    print(f"Schema Output from Fable 5.1:\n{schema_output}")

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

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

interface AgentTask {
  id: string;
  type: "classification" | "refactor" | "proof";
  instruction: string;
}

export async function dispatchAgentTask(task: AgentTask): Promise {
  const model = task.type === "classification" 
    ? "claude-haiku-5-5-20261001" 
    : task.type === "refactor" 
    ? "claude-sonnet-5-5-20261001" 
    : "claude-opus-5-5-20261001";

  const response = await anthropic.messages.create({
    model,
    max_tokens: 4096,
    messages: [{ role: "user", content: task.instruction }],
    system: [
      {
        type: "text",
        text: "You are an autonomous engineering sub-agent operating within an MCP swarm.",
        cache_control: { type: "ephemeral" },
      },
    ],
  });

  const content = response.content
    .filter((b) => b.type === "text")
    .map((b) => (b as { text: string }).text)
    .join("\n");

  return content;
}