Last refreshed 2026-09-18. Next refresh: weekly.
Why use Llama 3 70B on Replicate API?
Replicate API offers Llama 3 70B with pay-as-you-go pricing at $0.65/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Setup recipe
Python + curlpip install replicateexport REPLICATE_API_TOKEN=...import replicate
output = replicate.run(
"meta/meta-llama-3-70b",
input={"prompt": "Hello"}meta/meta-llama-3-70bRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# meta/meta-llama-3-70b format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"meta/meta-llama-3-70b",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "meta/meta-llama-3-70b", not the LLMReference slug "llama3-70b".
- Replicate uses "owner/model-name" format (e.g. "meta/meta-llama-3-8b-instruct") for the latest version, or "owner/model-name:version-sha" to pin to a specific version. The REST endpoint splits owner and model-name into the path: /v1/models/{owner}/{model-name}/predictions.
- The examples expect REPLICATE_API_TOKEN; rename it only if your application config maps the new variable.
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.65 |
| Output tokens | $2.75 |
Capabilities
No model capability flags are currently sourced.
About Llama 3 70B
The Llama 3 70B model is a state-of-the-art large language model with 70 billion parameters, released by Meta on April 18, 2024. It's based on an auto-regressive transformer architecture and has been optimized for dialogue applications using supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF). The model supports an 8,000-token context length and has been trained on over 15 trillion tokens from public online sources. It excels in tasks such as conversational AI, text generation, and natural language understanding, outperforming many existing open-source chat models on industry benchmarks.