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Peyton Green
Peyton Green

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Claude 3 Haiku Is Being Deprecated April 19. Here's How to Find and Fix Every Reference in Your Python Code.

#python #anthropic #claude #migration

If you've been building with the Anthropic API, you have 26 days to audit your Python code for one specific string: "claude-3-haiku".

On April 19, 2026, Anthropic is deprecating the Claude 3 Haiku model family. Any API call that hardcodes claude-3-haiku-20240307 (the full model ID) or uses claude-3-haiku as a prefix will break. This isn't a soft sunset where the old model keeps running in the background — it's a hard deprecation.

Here's how to find every reference and migrate before the deadline.

Step 1: Find every hardcoded model reference in your codebase

The problem with model strings is they end up everywhere: environment variable defaults, test fixtures, config files, utility functions, half-finished scripts. Start with a ripgrep scan:

# Find every claude-3-haiku reference across your entire project
rg "claude-3-haiku" --type py --type json --type yaml --type env -n
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If you don't have ripgrep, standard grep works too:

grep -r "claude-3-haiku" . --include="*.py" --include="*.json" --include="*.yaml" --include="*.env" -n
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Common places this shows up that get missed:

# Hardcoded in function defaults
def get_client(model="claude-3-haiku-20240307"):  # ← will break
    return anthropic.Anthropic()

# Buried in config dicts
MODELS = {
    "fast": "claude-3-haiku-20240307",    # ← will break
    "smart": "claude-3-opus-20240229",
}

# In test fixtures
@pytest.fixture
def haiku_client():
    return anthropic.Anthropic(), "claude-3-haiku-20240307"  # ← will break

# In .env files (easy to forget)
DEFAULT_MODEL=claude-3-haiku-20240307  # ← will break
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Step 2: Choose your migration target

Claude 3 Haiku was the go-to model for high-volume, latency-sensitive tasks — fast responses, low cost, good for classification, routing, and short generations. Your migration options:

Option A: Claude Haiku 4.5 (like-for-like replacement)

# Before
model = "claude-3-haiku-20240307"

# After
model = "claude-haiku-4-5-20251001"
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Claude Haiku 4.5 is the direct successor — same speed profile, same cost bracket, better capability. This is the right call if you're optimizing for throughput or cost.

Option B: Claude Sonnet 4.6 (if you've been underserved by Haiku)

# Before
model = "claude-3-haiku-20240307"

# After
model = "claude-sonnet-4-6"
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With 1M context and significantly stronger reasoning, Sonnet 4.6 is worth considering if your Haiku usage was doing light reasoning tasks that occasionally felt underpowered. The cost difference may be acceptable if you're running lower volumes.

Rule of thumb: If you chose Haiku for speed/cost, go to Haiku 4.5. If you chose Haiku because Sonnet felt like overkill, re-evaluate — Sonnet 4.6 at 1M context changes what "overkill" means.

Step 3: Centralize the model string (so this never happens again)

The real problem isn't the migration — it's having model strings scattered across 12 files. Fix the pattern while you're in there:

# models.py — single source of truth
class Models:
    # Current — update here and nowhere else
    FAST = "claude-haiku-4-5-20251001"
    STANDARD = "claude-sonnet-4-6"
    POWERFUL = "claude-opus-4-6"

    # Deprecated — kept for reference, remove after migration
    # HAIKU_3 = "claude-3-haiku-20240307"  # deprecated April 19, 2026
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Then in your code:

from models import Models

client = anthropic.Anthropic()
response = client.messages.create(
    model=Models.FAST,  # ← change this one constant, affects all callers
    max_tokens=1024,
    messages=[{"role": "user", "content": prompt}]
)
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One file, one change, zero scrambling at the next deprecation.

Step 4: Test before April 19

Don't assume a string swap is safe. Run your test suite with the new model string before the deadline — especially if you have:

  • Token count assumptions: Response lengths may differ between models
  • Latency-sensitive code: Haiku 4.5 is fast, but measure your actual p95 if you have SLA requirements
  • Structured output parsing: Newer models are more consistent, but validate your JSON/Pydantic schemas still parse correctly

A quick smoke test for each Haiku usage in your codebase:

import anthropic

def test_migration_smoke():
    """Verify new model ID works before deadline."""
    client = anthropic.Anthropic()
    response = client.messages.create(
        model="claude-haiku-4-5-20251001",
        max_tokens=50,
        messages=[{"role": "user", "content": "Say 'migration successful' and nothing else."}]
    )
    assert "migration successful" in response.content[0].text.lower()
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Quick checklist

  • [ ] Run rg "claude-3-haiku" across your entire project (Python, JSON, YAML, .env files)
  • [ ] Choose migration target: Haiku 4.5 (speed/cost) or Sonnet 4.6 (capability)
  • [ ] Centralize model strings into a single constants file
  • [ ] Update all references
  • [ ] Run test suite with new model IDs
  • [ ] Deploy before April 19

One more thing: find model strings in AI-generated code

If you use LLMs to generate code — including code review, test generation, or scaffolding — there's a good chance some of that generated code hardcoded claude-3-haiku-20240307 based on training data. Worth a dedicated grep pass on any code that was AI-generated or recently added to your project.

The Python AI Dev Toolkit includes a set of code review prompts specifically for auditing AI model references and configuration strings — useful if you're doing this across a large codebase or multiple projects.

April 19 deadline. Audit takes 10 minutes. Don't get caught with a broken prod deploy.

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