Last refreshed 2026-10-12. Next refresh: weekly.
Why use Llama 3.1 8B Instruct on DeepInfra?
DeepInfra offers Llama 3.1 8B Instruct with pay-as-you-go pricing at $0.02/1M input tokens. DeepInfra is a cloud inference platform offering cost-effective access to open-source AI models.
Compare Llama 3.1 8B Instruct across 17 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport DEEPINFRA_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["DEEPINFRA_API_KEY"],meta-llama/Meta-Llama-3.1-8B-InstructRequest example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["DEEPINFRA_API_KEY"],
base_url="https://api.deepinfra.com/v1/openai"
)
response = client.chat.completions.create(
model="meta-llama/Meta-Llama-3.1-8B-Instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Use provider model ID "meta-llama/Meta-Llama-3.1-8B-Instruct", not the LLMReference slug "llama3.1-8b-instruct".
- DeepInfra uses "organization/model-name" format, e.g. "meta-llama/Meta-Llama-3-8B-Instruct" or "mistralai/Mistral-7B-Instruct-v0.3". See the DeepInfra model catalog for exact IDs.
- The examples expect DEEPINFRA_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 | $0.15 | $0.29 |
| OctoAI API (Deprecated) | — | — |
| Together AI | $0.18 | $0.18 |
| Fireworks AI | $0.20 | $0.20 |
| NVIDIA NIM | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.02 |
| Output tokens | $0.05 |
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.