Using Phi-3 Mini 4k on Replicate API
Implementation guide · Phi-3 · Microsoft Research
Replicate API exposes Phi-3 Mini 4k through model ID microsoft/phi-3-mini-4k-instruct. Use the setup steps, sourced pricing, capabilities, and official provider links below to validate this route before deployment.
Last refreshed 2026-09-21. Next refresh: weekly.
Quick Start
- 1
- 2Use the Replicate API SDK or REST API to call
microsoft/phi-3-mini-4k-instruct— see the documentation for request format. - 3
Code Examples
pip install replicateREPLICATE_API_TOKENmicrosoft/phi-3-mini-4k-instructReplicate 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.
import replicate
# reads REPLICATE_API_TOKEN from env
# microsoft/phi-3-mini-4k-instruct format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
"microsoft/phi-3-mini-4k-instruct",
input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))Pricing on Replicate API
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.05 |
| Output tokens | $0.25 |
Capabilities
No model capability flags are currently sourced.
About Phi-3 Mini 4k
The Phi-3 Mini-4K-Instruct model by Microsoft is an advanced, lightweight language model boasting 3.8 billion parameters, optimized for environments with limited computational resources. It excels in various natural language processing tasks, especially in reasoning, text generation, and maintaining multi-turn conversations. Trained on a mix of synthetic and high-quality data, the model is tailored for effective instruction-following. Despite its capabilities, it has limitations in factual knowledge and multilingual support, often requiring external resources to enhance accuracy. The model is ideal for commercial and research applications that demand efficient processing, such as mobile apps and real-time systems.