Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 3.1 70B Instruct on Together AI?
Together AI offers Llama 3.1 70B Instruct with pay-as-you-go pricing at $0.88/1M input tokens. Together AI is a platform for running open-source and proprietary LLMs with fast serverless and dedicated endpoints at competitive inference pricing.
Compare Llama 3.1 70B Instruct across 14 providers to find the best fit for your use caseSetup recipe
Python + curlpip install togetherexport TOGETHER_API_KEY=...from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="llama3.1-70b-instruct",llama3.1-70b-instructRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="llama3.1-70b-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Together uses "organization/model-name" format, e.g. "meta-llama/Llama-4-Scout-17B-16E-Instruct" or "Qwen/QwQ-32B". See the Together model catalog for the exact ID.
- The examples expect TOGETHER_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 | $0.88 |
| Output tokens | $0.88 |
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.