Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 3 70B Instruct on Replicate API?
Replicate API offers Llama 3 70B Instruct 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.
Compare Llama 3 70B Instruct across 18 providers to find the best fit for your use caseSetup recipe
Python + curlpip install replicateexport REPLICATE_API_TOKEN=...import replicate
output = replicate.run(
"meta/meta-llama-3-70b-instruct",
input={"prompt": "Hello"}meta/meta-llama-3-70b-instructRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# meta/meta-llama-3-70b-instruct format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"meta/meta-llama-3-70b-instruct",
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-instruct", not the LLMReference slug "llama3-70b-instruct".
- 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.
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 | $0.65 |
| Output tokens | $2.75 |
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.