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.

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

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!' }]
});

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

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.

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.

# 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"
)