Meta: Llama 3.3 70B Instruct fp8 Fast
Llama 3.3 70B Instruct fp8 Fast is a language model from Meta released on Dec 1, 2024. It reads text and writes text, with a 24K token context window and up to 24K tokens per reply.
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It costs $0.71 per million input tokens and $0.71 per million output tokens. Cached input bills at $0.71, 0% less than fresh input.
It supports tool calling, structured JSON output and prompt caching, and its weights are open, so you can host it yourself.
34 providers serve it, including Novita AI, Cloudflare Workers AI and Hugging Face. The lowest input price is $0.05 per million tokens at NanoGPT.
- Modalities
- In / out price
- $0.71 / $0.71 per 1M
- Context
- 24K
- Max output
- 24K
- Released
- Dec 1, 2024
- Knowledge cutoff
- Dec 2023
- Tool calling
- Structured output
- Reasoning
- Prompt caching
- Open weights
- Free to use
Llama 3.3 70B Instruct fp8 Fast pricing
USD, as listed on Oct 15, 2026. Most bills are input heavy, so the cached input rate matters as much as the headline price.
| Input | $0.71 / 1M tokens | |
|---|---|---|
| Output | $0.71 / 1M tokens | |
| Cached input (read) | $0.71 / 1M tokens | 0% off input |
Llama 3.3 70B Instruct fp8 Fast providers
34 providers serve it, including Novita AI, Cloudflare Workers AI and Hugging Face. The lowest input price is $0.05 per million tokens at NanoGPT. Each host sets its own price and limits, so the cheapest one is not always the one with the longest context.
| Provider | Input /M | Output /M | Cache read /M | Context | Max output |
|---|---|---|---|---|---|
| Novita AI | $0.135 | $0.40 | – | 12K | 12K |
| Cloudflare Workers AI | $0.293 | $2.25 | – | 24K | 24K |
| Hugging Face | $0.59 | $0.79 | – | 131K | 4K |
| SambaNova | $0.60 | $1.20 | – | 131K | 3K |
| Azure | $0.71 | $0.71 | – | 128K | 33K |
| Amazon Bedrock | $0.72 | $0.72 | – | 128K | 4K |
| OCI Generative AI | $0.72 | $0.72 | – | 128K | 4K |
| watsonx.ai | $0.7526 | $0.7526 | – | 131K | 4K |
| Scaleway | $0.90 | $0.90 | – | 100K | 16K |
| Snowflake Cortex | n/a | n/a | – | 128K | 4K |
| NanoGPT | $0.05 | $0.23 | $0.025 | 131K | 16K |
| Eden AI | $0.10 | $0.32 | – | 131K | 4K |
Showing 12 of 34 providers: the lab first, then the major clouds, then the cheapest resellers.
What Llama 3.3 70B Instruct fp8 Fast costs per month
Pick a workload close to yours or type your own volumes.
Supported request parameters
What you can set when you call Llama 3.3 70B Instruct fp8 Fast through an OpenAI compatible API.
- Tools
tools - Structured outputs
structured_outputs - Temperature
temperature
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Llama 3.3 70B Instruct fp8 Fast questions
How much does the Llama 3.3 70B Instruct fp8 Fast API cost?
$0.71 per million input tokens and $0.71 per million output tokens, with cached input at $0.71. The estimator on this page turns that into a monthly bill for your own workload.
What is the context window of Llama 3.3 70B Instruct fp8 Fast?
24,000 tokens of input, and it can write up to 24,000 tokens in one reply. As a rough guide, a million tokens is around 750,000 English words.
Which providers serve Llama 3.3 70B Instruct fp8 Fast?
34 providers serve it, including Novita AI, Cloudflare Workers AI and Hugging Face. The lowest input price is $0.05 per million tokens at NanoGPT. Prices differ by host, so the Providers table on this page lists each one with its input and output price.
Does Llama 3.3 70B Instruct fp8 Fast support tool calling?
Yes. It accepts tool definitions and can return JSON that matches a schema, which is what an agent needs to call your APIs.
Is Llama 3.3 70B Instruct fp8 Fast open source?
Its weights are published, so you can run it on your own hardware or a cloud of your choice. Read the license before commercial use.
When was Llama 3.3 70B Instruct fp8 Fast released?
Dec 1, 2024, with training data up to Dec 2023.
Catalog data from models.dev (MIT licensed), refreshed daily, last on Oct 15, 2026. The summary is written from these facts.
Building on Llama 3.3 70B Instruct fp8 Fast?
I test your real tasks on it and the two closest alternatives, then ship the agent on whichever wins on cost and accuracy.
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