Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 3 70B Instruct on DeepInfra?
DeepInfra offers Llama 3 70B Instruct with pay-as-you-go pricing at $0.45/1M input tokens. DeepInfra is a cloud inference platform offering cost-effective access to open-source AI models.
Compare Llama 3 70B Instruct across 18 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"],llama3-70b-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="llama3-70b-instruct",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- 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 70B Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| GCP Vertex AI | $1.20 | $3.60 |
| AWS Bedrock | $0.99 | $0.99 |
| Microsoft Foundry | $3.78 | $11.34 |
| NVIDIA NIM | — | — |
| DeepInfra | $0.45 | $0.65 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.45 |
| Output tokens | $0.65 |
Capabilities
About Llama 3 70B Instruct
The Llama 3 70B Instruct model is a large language model with 70 billion parameters, released by Meta on April 18, 2024. It's an instruction-tuned variant optimized for conversational applications, utilizing an advanced auto-regressive transformer architecture. The model excels in following instructions and engaging in dialogue, having been trained on over 15 trillion tokens with a December 2023 knowledge cutoff. It demonstrates superior performance on industry benchmarks, scoring 82.0 on the MMLU (5-shot) test. The model incorporates extensive safety measures and optimizations, including RLHF, to enhance helpfulness and reduce harmful content generation.