Last refreshed 2026-09-24. Next refresh: weekly.
Why use Llama 3.1 70B Instruct on Featherless?
Featherless offers Llama 3.1 70B Instruct with pay-as-you-go pricing at $0.72/1M input tokens. Featherless is a serverless inference provider for a large catalog of third-party open text-generation models (40,000+).
Compare Llama 3.1 70B Instruct across 14 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport FEATHERLESS_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.featherless.ai/v1",meta-llama/Meta-Llama-3.1-70B-InstructRequest example
import os
from openai import OpenAI
client = OpenAI(
base_url="https://api.featherless.ai/v1",
api_key=os.environ["FEATHERLESS_API_KEY"],
)
response = client.chat.completions.create(
model="meta-llama/Meta-Llama-3.1-70B-Instruct",
messages=[{"role": "user", "content": "Hello!"}],
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "meta-llama/Meta-Llama-3.1-70B-Instruct", not the LLMReference slug "llama3.1-70b-instruct".
- Use exact Featherless catalog id in modelProvider.providerModelId (HF-style org/name). Not interchangeable with LLM Reference slugs.
- The examples expect FEATHERLESS_API_KEY; rename it only if your application config maps the new variable.
Compare Llama 3.1 70B Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Cloudflare Workers AI | — | — |
| OctoAI API (Deprecated) | — | — |
| Together AI | $0.88 | $0.88 |
| Fireworks AI | $0.90 | $0.90 |
| NVIDIA NIM | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.72 |
| Output tokens | $0.72 |
Capabilities
About Llama 3.1 70B Instruct
The Llama 3.1 70B Instruct model is a cutting-edge large language model with 70 billion parameters, designed for instruction-following tasks. It features multilingual capabilities, supporting languages like English, German, French, and others. Fine-tuned using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF), it excels in understanding and responding to user instructions. The model can handle a context length of up to 128k tokens, making it suitable for complex dialogue systems and applications requiring detailed responses.