How much does the Llama 3.3 70B Instruct API cost?

On FastMetal, Llama 3.3 70B Instruct is billed per token: $0.1055 per 1M input tokens and $0.3376 per 1M output tokens on a US-dollar account, before tax. Usage is drawn from a prepaid balance; there is no subscription or monthly fee.

Can I call Llama 3.3 70B Instruct with the OpenAI SDK?

Yes. Point base_url at https://api.fastmetal.ai/v1 and pass "llama-3.3-70b-instruct" as the model; existing OpenAI-style code works unchanged, including streaming, tool calls and structured output.

What is the context window of Llama 3.3 70B Instruct?

131,072 tokens, shared between the prompt and the response.

What do I need to try Llama 3.3 70B Instruct?

Create an account and add credit; the model is then available both in the browser chat and over the API. There is no contract or minimum spend.

Back to Models

Llama 3.3 70B Instruct

The Meta Llama 3.3 multilingual large language model (LLM) is a pretrained and instruction tuned generative model in 70B (text in/text out). The Llama 3.3 instruction tuned text only model is optimized for multilingual dialogue use cases and outperforms many of the available open source and closed chat models on common industry benchmarks. Supported languages: English, German, French, Italian, Portuguese, Hindi, Spanish, and Thai. [Model Card](https://github.com/meta-llama/llama-models/blob/main/models/llama3_3/MODEL_CARD.md)

12/6/2024

131,072 tokens

Input: $0.1055/M

Output: $0.3376/M

Try in Chat Get API Key

Specifications

Modalities

Input

text

Output

text

Supported Parameters

frequency_penalty

logit_bias

logprobs

max_tokens

min_p

presence_penalty

repetition_penalty

response_format

seed

stop

structured_outputs

temperature

tool_choice

tools

top_k

top_logprobs

top_p

Max Output Tokens

16,384

Data policy

Prompt retention: Unknown — we could not confirm
Training: Not used for training

"Unknown" does not mean "safe". It means we could not confirm it.

Whether a provider trains on prompts is a declared value from our terms with them. Retention is determined from the upstream listing for every host this model can reach. Neither is guessed.

Measured performance

Time to first response (p50)3.0s

Throughput (p50)20 tokens/s

Measured through FastMetal's own gateway over the last 30 days (648 first-response samples).

Throughput is sampled once a day: the median of 26 observations over the last 30 days.

Time to first response is measured to the first streamed chunk; throughput counts all output tokens, including reasoning tokens. These are observations, not a performance guarantee.

Code Examples

curl https://api.fastmetal.ai/v1/chat/completions \
  -H "Content-Type: application/json" \
  -H "Authorization: Bearer YOUR_API_KEY" \
  -d '{
    "model": "llama-3.3-70b-instruct",
    "messages": [{"role": "user", "content": "Hello!"}]
  }'

How Llama 3.3 70B Instruct actually answers

Real responses to our standard prompts, recorded on FastMetal.

Count the number of 'r's in 'strawberry'

Count the number of 'r's in 'strawberry'. Explain your reasoning step by step.

To count the number of 'r's in 'strawberry', I'll break down the word into its individual letters and then examine each one to identify the 'r's. Here are the steps: 1. **Write down the word 'strawberry' with its individual letters separated**: s-t-r-a-w-b-e-r-r-y 2. **Examine each letter to find the 'r's**: Going through the word letter by letter, I identify the 'r's. - The first 'r' appears after 't', so that's one 'r'. - Continuing through the word, I find another 'r' after 'e', which gives me a second 'r'. - Then, immediately after this second 'r', I find a third 'r'. 3. **Count the total number of 'r's identified**: From the step above, I've identified a total of 3 'r's in th…

12.6s response217 output tokens

Debug This Error

I'm getting the following error in my Node.js application: TypeError: Cannot read properties of undefined (reading 'map') at UserList (/app/components/UserList.js:12:25) at renderWithHooks (/app/node_modules/rea…

# Step-by-step analysis of the problem: 1. **The error is occurring because the `map()` function is being called on `undefined`**: This means that the `users` prop is not being passed to the `UserList` component, or its value is `undefined`. 2. **The `map()` function is a method of arrays**: It is used to iterate over the elements of an array and apply a transformation to each element. If `users` is not an array (i.e., it's `undefined`), this method will not be available, hence the error. 3. **To fix this error, you need to ensure that `users` is always an array**: You can achieve this by adding a conditional statement to check if `users` is an array before calling `map()` on it. # Fixed so…

28.4s response560 output tokens

Code Review

Please review the following Python function and suggest improvements for readability, performance, and best practices: def get_data(url, retries=3): import requests import time for i in range(retries):…

### Review and Suggestions The provided Python function `get_data` appears to be designed to fetch data from a given URL, filter active items, and return the filtered results. Here's a review of the function focusing on readability, performance, and best practices, along with suggestions for improvement. #### Issues and Improvements: 1. **Import Statements**: Import statements should be placed at the top of the file, not inside the function. This adheres to PEP 8, the official Python style guide. 2. **Exception Handling**: The `except` block is too broad. It's better to catch specific exceptions that you anticipate might occur. For example, `requests.get` might raise `requests.RequestExc…

40.3s response815 output tokens

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Frequently asked questions

How much does the Llama 3.3 70B Instruct API cost?: On FastMetal, Llama 3.3 70B Instruct is billed per token: $0.1055 per 1M input tokens and $0.3376 per 1M output tokens on a US-dollar account, before tax. Usage is drawn from a prepaid balance; there is no subscription or monthly fee.
Can I call Llama 3.3 70B Instruct with the OpenAI SDK?: Yes. Point base_url at https://api.fastmetal.ai/v1 and pass "llama-3.3-70b-instruct" as the model; existing OpenAI-style code works unchanged, including streaming, tool calls and structured output.
What is the context window of Llama 3.3 70B Instruct?: 131,072 tokens, shared between the prompt and the response.
What do I need to try Llama 3.3 70B Instruct?: Create an account and add credit; the model is then available both in the browser chat and over the API. There is no contract or minimum spend.