Claude Haiku 3 Deprecation (April 19, 2026): Complete Migration Guide to Claude Haiku 4.5
Anthropic has announced that Claude Haiku 3 (model ID: claude-3-haiku-20240307) will be retired on April 19, 2026. If you're using this model in production, you need to migrate to Claude Haiku 4.5 (claude-haiku-4-5-20251001) before that date.
Why Is Claude Haiku 3 Being Deprecated?
Anthropic follows a structured model lifecycle policy: as newer, more capable models become available, older versions are retired to simplify the model portfolio and encourage adoption of improved technology. In line with this policy, Anthropic provides at least 60 days' notice before retiring any publicly released model.
Claude Haiku 3 has served as a popular fast, cost-effective model since its March 2024 release. Its successor, Claude Haiku 4.5, offers significantly better performance across all benchmarks while maintaining the same speed and cost profile that made Haiku 3 popular.
Deprecation Timeline
| Milestone | Date |
|---|---|
| Deprecation announcement | February–March 2026 |
| Retirement date | April 19, 2026 |
| Post-retirement behavior | API requests will return an error |
After April 19, 2026, any API call specifying claude-3-haiku-20240307 will fail. Complete your migration well before this date to avoid service disruptions.
Claude Haiku 3 vs Claude Haiku 4.5: What's Different?
Claude Haiku 4.5 is a substantial upgrade over Haiku 3 in nearly every dimension:
| Feature | Claude Haiku 3 | Claude Haiku 4.5 |
|---|---|---|
| Context window | 200K tokens | 200K tokens |
| Response speed | Fast | Equal or faster |
| Coding ability | Standard | Significantly improved |
| Reasoning & analysis | Standard | Significantly improved |
| Tool use accuracy | Good | More reliable |
| Vision (image input) | Supported | Supported |
| Near-frontier performance | No | Yes |
Haiku 4.5 is described as "near-frontier" — meaning it punches well above its weight class and is especially well-suited for real-time applications, high-volume processing, agentic tasks, and cost-sensitive deployments requiring strong reasoning.
Step-by-Step Migration Guide
Step 1: Find All References to the Old Model ID
Search your codebase for every occurrence of claude-3-haiku-20240307:
# Search across Python, TypeScript, and JavaScript files
grep -r "claude-3-haiku-20240307" ./src --include="*.py" --include="*.ts" --include="*.js"
# Example output:
# ./src/api/chat.py:12: model="claude-3-haiku-20240307",
# ./src/workers/summarizer.ts:8: model: 'claude-3-haiku-20240307',
# ./config/defaults.json:5: "model": "claude-3-haiku-20240307"Step 2: Update the Model ID
Replace every instance of claude-3-haiku-20240307 with claude-haiku-4-5-20251001.
Python (Anthropic SDK):
import anthropic
client = anthropic.Anthropic()
# ❌ Old: deprecated model
# model = "claude-3-haiku-20240307"
# ✅ New: Claude Haiku 4.5
message = client.messages.create(
model="claude-haiku-4-5-20251001", # ← updated model ID
max_tokens=1024,
messages=[
{"role": "user", "content": "Say hello in Japanese."}
]
)
print(message.content[0].text)
# Expected output: こんにちは！ (Konnichiwa!)TypeScript / Node.js (Anthropic SDK):
import Anthropic from "@anthropic-ai/sdk";
const client = new Anthropic();
async function main() {
// ❌ Old: deprecated model
// const model = "claude-3-haiku-20240307";
// ✅ New: Claude Haiku 4.5
const message = await client.messages.create({
model: "claude-haiku-4-5-20251001", // ← updated model ID
max_tokens: 1024,
messages: [{ role: "user", content: "Explain prompt engineering briefly." }],
});
console.log(message.content[0].text);
// Expected output: Prompt engineering is the practice of crafting precise
// instructions for AI models to guide their responses toward a desired output...
}
main();If you manage the model ID via environment variables (recommended):
# .env
# ❌ Old
# MODEL_ID=claude-3-haiku-20240307
# ✅ New
MODEL_ID=claude-haiku-4-5-20251001import os
import anthropic
client = anthropic.Anthropic()
# Load model ID from environment variable
model_id = os.environ.get("MODEL_ID", "claude-haiku-4-5-20251001")
message = client.messages.create(
model=model_id,
max_tokens=512,
messages=[{"role": "user", "content": "Summarize: AI is changing software development."}]
)
print(message.content[0].text)
# Expected output: AI is transforming software development by automating repetitive
# tasks, enhancing code review, and enabling developers to build faster.Step 3: Run Tests Before Going to Production
Always validate the migration in a staging environment before deploying to production.
import anthropic
import pytest
client = anthropic.Anthropic()
def test_haiku_45_basic_response():
"""Verify Claude Haiku 4.5 returns a valid response."""
message = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=100,
messages=[{"role": "user", "content": "What is 2 + 2?"}]
)
assert message.stop_reason == "end_turn"
assert len(message.content) > 0
assert "4" in message.content[0].text
print("✅ Basic response test passed")
def test_haiku_45_tool_use():
"""Verify tool use (function calling) works correctly."""
tools = [
{
"name": "get_weather",
"description": "Retrieve current weather for a city",
"input_schema": {
"type": "object",
"properties": {
"location": {"type": "string", "description": "City name"}
},
"required": ["location"]
}
}
]
message = client.messages.create(
model="claude-haiku-4-5-20251001",
max_tokens=200,
tools=tools,
messages=[{"role": "user", "content": "What's the weather in Tokyo?"}]
)
tool_uses = [block for block in message.content if block.type == "tool_use"]
assert len(tool_uses) > 0
assert tool_uses[0].name == "get_weather"
print(f"✅ Tool use test passed — input: {tool_uses[0].input}")
# Expected: ✅ Tool use test passed — input: {'location': 'Tokyo'}
if __name__ == "__main__":
test_haiku_45_basic_response()
test_haiku_45_tool_use()Step 4: Benchmark Latency and Token Usage
While Haiku 4.5 is designed to match Haiku 3's speed and cost profile, it's worth benchmarking your specific use case.
import anthropic
import time
client = anthropic.Anthropic()
def benchmark_model(model_id: str, prompt: str, runs: int = 5) -> dict:
"""Measure average latency and token usage for a given model."""
latencies = []
input_tokens_list = []
output_tokens_list = []
for _ in range(runs):
start = time.time()
message = client.messages.create(
model=model_id,
max_tokens=256,
messages=[{"role": "user", "content": prompt}]
)
latencies.append(time.time() - start)
input_tokens_list.append(message.usage.input_tokens)
output_tokens_list.append(message.usage.output_tokens)
return {
"model": model_id,
"avg_latency_sec": round(sum(latencies) / runs, 2),
"avg_input_tokens": round(sum(input_tokens_list) / runs),
"avg_output_tokens": round(sum(output_tokens_list) / runs),
}
prompt = "Explain the concept of machine learning in 3 sentences."
result = benchmark_model("claude-haiku-4-5-20251001", prompt)
print(f"Model: {result['model']}")
print(f"Avg latency: {result['avg_latency_sec']}s")
print(f"Avg input tokens: {result['avg_input_tokens']}")
print(f"Avg output tokens: {result['avg_output_tokens']}")
# Expected output:
# Model: claude-haiku-4-5-20251001
# Avg latency: 0.87s
# Avg input tokens: 18
# Avg output tokens: 71Common Migration Patterns
Pattern A: Centralize Model IDs as Constants (Recommended)
Define model IDs as named constants to make future updates a one-line change:
# constants/models.py
class ClaudeModels:
"""Centralized Claude model ID constants."""
# Haiku — fast, cost-efficient
HAIKU = "claude-haiku-4-5-20251001"
# Sonnet — balanced speed and intelligence
SONNET = "claude-sonnet-4-6"
# Opus — maximum capability
OPUS = "claude-opus-4-6"
# Usage
from constants.models import ClaudeModels
import anthropic
client = anthropic.Anthropic()
message = client.messages.create(
model=ClaudeModels.HAIKU,
max_tokens=512,
messages=[{"role": "user", "content": "Hello!"}]
)
print(message.content[0].text)
# Expected output: Hello! How can I help you today?Pattern B: Batch Processing Migration
If you use the Batch API, only the model ID needs to change:
import anthropic
client = anthropic.Anthropic()
requests = [
{"id": "req-001", "text": "Summarize AI trends in 2026."},
{"id": "req-002", "text": "What is the difference between Claude Haiku and Sonnet?"},
{"id": "req-003", "text": "Explain context windows in LLMs."},
]
batch = client.messages.batches.create(
requests=[
{
"custom_id": req["id"],
"params": {
"model": "claude-haiku-4-5-20251001", # Updated model ID
"max_tokens": 256,
"messages": [{"role": "user", "content": req["text"]}]
}
}
for req in requests
]
)
print(f"Batch ID: {batch.id}")
print(f"Status: {batch.processing_status}")
# Expected output:
# Batch ID: msgbatch_01XFDUDYJgAACzvnptvVoYEL
# Status: in_progressLooking back
The Claude Haiku 3 retirement on April 19, 2026 is approaching fast. The good news: migration is simple — just update a model ID string. Here's a quick recap:
- Retire date: April 19, 2026
- Replacement:
claude-haiku-4-5-20251001 - What to do: Search your codebase, replace the model ID, test in staging, deploy
- Benefit: You get noticeably better performance at the same price point
Don't wait until the last minute — update your applications now and take advantage of Haiku 4.5's improvements today.
For a broader look at how to get started with the Claude API, see Claude API Quickstart. For details on tool use and function calling, check out the Tool Use Guide.