Llama 3.3 70B Instruct

by Meta

Zero data retention−25%
meta-llama/llama-3.3-70b-instruct
Input
$0.83/1M tokens
Output
$1.11/1M tokens
Context
131Ktokens
Max output
33Ktokens
Knows to
December 2023
Released
6 Dec 2024

About

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out).

Pricing

Input$0.83 from $0.14per 1M prompt tokens
Output$1.11 from $0.45per 1M generated tokens, reasoning included
Cached input$0.41per 1M tokens read back from the prompt cache

25% off right now. The provider is running a promotion, and the developer API bills what the provider actually charged for your call plus our flat markup, so it reaches your bill with nothing to claim. The “from” figure is the cheapest endpoint serving the model; which one answers is the provider’s routing decision, so treat it as a floor. Promotions can end at any time.

What a request costs

Typical requestEachPer 1,000
Short chat reply1,500 in · 400 out$0.0017$1.68
Question over a long document40,000 in · 800 out$0.034$33.92
Agent tool step20,000 in · 1,200 out$0.018$17.85

At the developer-API rate, before any provider promotion or prompt-cache saving. In the app, the model picker shows what a message costs on your plan before you send it.

Benchmarks

Intelligence7.7#241 of 285
Coding11.9#149 of 163

Scores from Artificial Analysis; bars are scaled to the best model on our list. Published by OpenRouter alongside its catalogue.

Call it from code

OpenAI-compatible: point any OpenAI SDK at https://api.privateer.pro/v1 and set model. Keys are created in the app under Settings.

cURL
curl https://api.privateer.pro/v1/chat/completions \
  -H "Authorization: Bearer $PRIVATEER_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "meta-llama/llama-3.3-70b-instruct",
    "messages": [{ "role": "user", "content": "Hello" }]
  }'
Python
import os
from openai import OpenAI

client = OpenAI(
    base_url="https://api.privateer.pro/v1",
    api_key=os.environ["PRIVATEER_API_KEY"],
)
reply = client.chat.completions.create(
    model="meta-llama/llama-3.3-70b-instruct",
    messages=[{"role": "user", "content": "Hello"}],
)
print(reply.choices[0].message.content)
JavaScript
import OpenAI from "openai";

const client = new OpenAI({
  baseURL: "https://api.privateer.pro/v1",
  apiKey: process.env.PRIVATEER_API_KEY,
});
const reply = await client.chat.completions.create({
  model: "meta-llama/llama-3.3-70b-instruct",
  messages: [{ role: "user", content: "Hello" }],
});
console.log(reply.choices[0].message.content);

Parameters this model accepts

frequency_penaltylogit_biaslogprobsmax_tokensmin_ppresence_penaltyrepetition_penaltyresponse_formatseedstopstructured_outputstemperaturetool_choicetoolstop_ktop_logprobstop_p

API reference → · How API billing works →

Prices are USD at the developer-API rate, as of 16 Oct 2026, rebuilt daily from the providers’ live catalogues. ← All models