Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 3 8B Instruct on Replicate API?
Replicate API offers Llama 3 8B Instruct with pay-as-you-go pricing at $0.05/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Compare Llama 3 8B Instruct across 17 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-3-8b-instruct",
input={"prompt": "Hello"}meta/meta-llama-3-8b-instructRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# meta/meta-llama-3-8b-instruct format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"meta/meta-llama-3-8b-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-3-8b-instruct", not the LLMReference slug "llama3-8b-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 8B Instruct Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| AWS Bedrock | $0.30 | $0.60 |
| DeepInfra | $0.02 | $0.05 |
| OctoAI API (Deprecated) | — | — |
| Fireworks AI | $0.20 | $0.20 |
| Alibaba Cloud PAI-EAS | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.05 |
| Output tokens | $0.25 |
Capabilities
About Llama 3 8B Instruct
The Llama 3 8B Instruct model, released on April 18, 2024, is Meta's latest instruction-following language model with 8 billion parameters. It utilizes an auto-regressive transformer architecture with Grouped-Query Attention for improved scalability. Trained on over 15 trillion tokens and fine-tuned with 10 million human-annotated examples, it excels in dialogue and conversational tasks. The model outperforms its predecessors on industry benchmarks, scoring 68.4 on MMLU (5-shot). Designed for commercial and research applications, it prioritizes safety and helpfulness, making it suitable for chatbots, virtual assistants, and other interactive AI applications. For more details, visit the Hugging Face page [1].