Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 2 70B Chat on Replicate API?
Replicate API offers Llama 2 70B Chat 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 2 70B Chat across 14 providers to find the best fit for your use caseSetup recipe
Python + curlpip install replicateexport REPLICATE_API_TOKEN=...import replicate
output = replicate.run(
"meta/llama-2-70b-chat",
input={"prompt": "Hello"}meta/llama-2-70b-chatRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# meta/llama-2-70b-chat format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"meta/llama-2-70b-chat",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "meta/llama-2-70b-chat", not the LLMReference slug "llama2-70b-chat".
- 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 2 70B Chat Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Databricks Foundation Model Serving | $0.50 | $1.50 |
| Microsoft Foundry | $1.54 | $1.77 |
| GCP Vertex AI | $0.80 | $2.40 |
| Alibaba Cloud PAI-EAS | — | — |
| AWS Bedrock | $1.95 | $2.56 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.65 |
| Output tokens | $2.75 |
Capabilities
About Llama 2 70B Chat
Llama 2 70B Chat is a large-scale language model with 70 billion parameters, designed for conversational AI applications. Released on July 18, 2023, it's part of Meta's Llama 2 family, featuring advanced transformer architecture optimized through supervised fine-tuning and reinforcement learning with human feedback. The model excels in generating human-like responses, outperforming many open-source alternatives and rivaling closed-source models like ChatGPT. Trained on 2 trillion tokens from diverse public sources, it's suitable for commercial and research applications in English, particularly for assistant-like functionalities. The model is available on Hugging Face for further exploration and implementation .