Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 3.1 8B Instruct on Together AI?
Together AI offers Llama 3.1 8B Instruct with pay-as-you-go pricing at $0.18/1M 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 8B Instruct across 17 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="llama3.1-8b-instruct",llama3.1-8b-instructRequest example
from together import Together
client = Together() # reads TOGETHER_API_KEY from env
response = client.chat.completions.create(
model="llama3.1-8b-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 8B Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Cloudflare Workers AI | — | — |
| OctoAI API (Deprecated) | — | — |
| Together AI | $0.18 | $0.18 |
| Fireworks AI | $0.20 | $0.20 |
| NVIDIA NIM | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.18 |
| Output tokens | $0.18 |
Capabilities
About Llama 3.1 8B Instruct
The Llama 3.1 8B Instruct model, released on July 23, 2024, is a multilingual large language model with 8 billion parameters, optimized for instruction-following tasks. It features an enhanced transformer architecture, supporting languages like English, German, French, and others. The model excels in dialogue applications, having been fine-tuned using supervised fine-tuning and reinforcement learning with human feedback. Trained on approximately 15 trillion tokens with a December 2023 data cutoff, it outperforms many existing open-source and closed chat models in various benchmarks.