Last refreshed 2026-07-09. Next refresh: weekly.
Why use Llama 2 13B Chat on Replicate API?
Replicate API offers Llama 2 13B Chat with pay-as-you-go pricing at $0.10/1M input tokens. Replicate is a cloud-based platform that enables users to run machine learning models easily and efficiently.
Compare Llama 2 13B Chat across 11 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-2-13b-chat",
input={"prompt": "Hello"}meta/llama-2-13b-chatRequest example
import replicate
# reads REPLICATE_API_TOKEN from env
# meta/llama-2-13b-chat format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"meta/llama-2-13b-chat",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Gotchas
- Use provider model ID "meta/llama-2-13b-chat", not the LLMReference slug "llama2-13b-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 13B Chat Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Alibaba Cloud PAI-EAS | — | — |
| AWS Bedrock | $0.75 | $1.00 |
| Microsoft Foundry | $0.81 | $0.94 |
| GCP Vertex AI | $0.16 | $0.48 |
| DeepInfra | $0.13 | $0.13 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.10 |
| Output tokens | $0.50 |
Capabilities
About Llama 2 13B Chat
The Llama 2 13B Chat model is a 13 billion parameter generative text model developed by Meta, optimized for conversational applications. Released on July 18, 2023, it's part of the Llama 2 family and excels in dialogue scenarios. The model leverages supervised fine-tuning (SFT) and reinforcement learning with human feedback (RLHF) to generate coherent and contextually relevant responses. Trained on 2 trillion tokens from diverse public sources, it outperforms many open-source chat models and matches popular closed-source models in helpfulness and safety. This model is ideal for AI engineers working on chatbots, virtual assistants, and customer service automation.