Migration Guide

How to Switch from Claude 4 to Llama 4 Maverick — Save 98%

Claude 4 is dead. Llama 4 Maverick costs 98% less and has a 5x larger context window. Step-by-step migration guide with code examples for Python and Node.js.

Published Jun 13, 2026 · 8 min read

Claude 4 is dead. Your API calls are returning 410 Gone. Instead of just replacing it with the next Claude model, why not save 98% by switching to Llama 4 Maverick — and get a 5x larger context window while you're at it?

98%
average savings when switching from Claude 4 Opus to Llama 4 Maverick
Claude 4 Opus (DEAD)
$15 / $75
Input / Output — 200K context — 410 GONE
Llama 4 Maverick
$0.27 / $0.85
98% cheaper · 1M context · OpenAI-compatible

Why Llama 4 Maverick?

Monthly Cost Comparison

Monthly Usage Claude 4 Opus Llama 4 Maverick You Save
1M input + 500K output $52.50 $0.70 $51.80 (99%)
5M input + 2M output $225 $3.05 $221.95 (99%)
10M input + 5M output $525 $6.95 $518.05 (99%)
50M input + 20M output $2,250 $30.50 $2,219.50 (99%)

Step-by-Step Migration

1

Sign Up for Together.ai

Go to api.together.ai and create an account. You get $5 in free credits to start.

Go to API Keys and create a new key. Save it — you'll need it in the next step.

2

Install the SDK

Llama 4 Maverick uses the OpenAI-compatible API via Together.ai. Install the OpenAI SDK:

# Python
pip install openai

# Node.js
npm install openai
3

Update Your Code

Replace the Anthropic SDK with the OpenAI SDK pointed at Together.ai:

Python

# Before (Claude 4 — dead)
import anthropic
client = anthropic.Anthropic(api_key="your-anthropic-key")
response = client.messages.create(
    model="claude-4-opus",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}]
)

# After (Llama 4 Maverick — 98% cheaper)
from openai import OpenAI
client = OpenAI(
    api_key="your-together-key",
    base_url="https://api.together.xyz/v1"
)
response = client.chat.completions.create(
    model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    max_tokens=1024,
    messages=[{"role": "user", "content": "Hello!"}]
)

Node.js

// Before (Claude 4 — dead)
import Anthropic from '@anthropic-ai/sdk';
const client = new Anthropic({ apiKey: 'your-anthropic-key' });
const response = await client.messages.create({
    model: 'claude-4-opus',
    max_tokens: 1024,
    messages: [{ role: 'user', content: 'Hello!' }]
});

// After (Llama 4 Maverick — 98% cheaper)
import OpenAI from 'openai';
const client = new OpenAI({
    apiKey: 'your-together-key',
    baseURL: 'https://api.together.xyz/v1'
});
const response = await client.chat.completions.create({
    model: 'meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8',
    max_tokens: 1024,
    messages: [{ role: 'user', content: 'Hello!' }]
});
4

Update Response Parsing

The response format is slightly different. Update your parsing code:

# Before (Anthropic format)
text = response.content[0].text

# After (OpenAI format)
text = response.choices[0].message.content
5

Leverage the 1M Context Window

With 5x more context than Claude 4, you can now:

  • Send entire codebases in a single API call
  • Process 500+ page documents without chunking
  • Maintain much longer conversation histories
  • Analyze large datasets in context

Update your max_tokens and input limits to take advantage of the larger window.

6

Test and Deploy

Make a few test calls to verify Llama 4 Maverick works with your prompts. The 1M context window means fewer truncation errors. Then deploy. That's it — you're now paying 98% less.

Calculate Your Exact Savings

Use the APIpulse migration calculator to see exactly how much you'll save by switching to Llama 4 Maverick.

Calculate Savings →

LangChain Migration

If you're using LangChain, the migration is even simpler:

# Before (Claude 4 — dead)
from langchain_anthropic import ChatAnthropic
chat = ChatAnthropic(model="claude-4-opus")

# After (Llama 4 Maverick — 98% cheaper)
from langchain_openai import ChatOpenAI
chat = ChatOpenAI(
    model="meta-llama/Llama-4-Maverick-17B-128E-Instruct-FP8",
    base_url="https://api.together.xyz/v1",
    api_key="your-together-key"
)

What About Quality?

Llama 4 Maverick is excellent at:

Where Claude Opus 4.8 may be better:

For 98% cost savings and 5x more context, Llama 4 Maverick is the clear winner for most use cases. You can always use Claude Opus 4.8 for the 5% of tasks that need its specific strengths.

Llama 4 Maverick vs Other Alternatives

Model Input Price Output Price Context vs Claude 4
Llama 4 Maverick $0.27 $0.85 1M 98% cheaper
DeepSeek V4 Pro $0.44 $0.87 128K 97% cheaper
Gemini 3.1 Pro $2.00 $12.00 1M 87% cheaper
GPT-5 $1.25 $10.00 272K 92% cheaper
Claude Opus 4.8 $5.00 $25.00 1M 67% cheaper
Claude 4 Opus $15.00 $75.00 200K DEAD
Pro Tip

Optimize your migration with APIpulse Pro

Get personalized model recommendations, save migration scenarios, and export cost reports — so you always pick the cheapest model for each task.

Get Pro — $29 lifetime →

Frequently Asked Questions

Is Llama 4 Maverick compatible with my existing prompts?

Mostly yes. Llama 4 Maverick responds well to the same prompts you'd use with Claude. You may need to tweak system prompts slightly, but most code works as-is. The 1M context window means you can send more context without worrying about truncation.

What about rate limits?

Together.ai offers generous rate limits. Free tier: 60 requests/minute. Paid tier: 1,000+ requests/minute. Check their pricing page for current limits.

Can I use both Llama 4 Maverick and Claude?

Yes. Many developers use Llama 4 Maverick for high-volume tasks and Claude Opus 4.8 for complex reasoning. The APIpulse comparison tool helps you decide which model for which task.

Can I self-host Llama 4 Maverick?

Yes. Llama 4 Maverick is open source. You can self-host it on your own infrastructure for even lower costs at scale. Together.ai handles the hosting if you prefer not to manage servers.

What about the 1M context window — is it really usable?

Yes. Llama 4 Maverick handles 1M tokens efficiently. You'll need proportionally more memory, but the cost per token is so low that even massive contexts are affordable. A 500K token call costs about $0.14 on input.

Related Reading