Using Phi-3 Mini 128K on Replicate API

Implementation guide · Phi-3 · Microsoft Research

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Replicate API exposes Phi-3 Mini 128K through model ID microsoft/phi-3-mini-128k-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. 1
    Create an account at Replicate API and generate an API key.
  2. 2
    Use the Replicate API SDK or REST API to call microsoft/phi-3-mini-128k-instruct — see the documentation for request format.
  3. 3
    You'll be billed $0.05/1M input, $0.25/1M output tokens. See full pricing.

Code Examples

Install
pip install replicate
API key
REPLICATE_API_TOKEN
Model ID
microsoft/phi-3-mini-128k-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.

import replicate

# reads REPLICATE_API_TOKEN from env
# microsoft/phi-3-mini-128k-instruct format: "owner/model-name" (latest version) or "owner/model-name:version-hash"
output = replicate.run(
    "microsoft/phi-3-mini-128k-instruct",
    input={"prompt": "Hello"}
)
# Output is a list or generator depending on the model
print("".join(output))

Pricing on Replicate API

TypePrice (per 1M)
Input tokens$0.05
Output tokens$0.25

Capabilities

No model capability flags are currently sourced.

About Phi-3 Mini 128K

Phi-3 Mini-128K-Instruct, developed by Microsoft, is a 3.8 billion-parameter large language model renowned for its lightweight, open-source architecture. Despite its modest size, it excels in reasoning tasks, particularly in math and logic, and showcases strong code generation capabilities. A standout feature is its remarkable ability to handle up to 128,000 tokens, allowing it to process extensive text documents and codebases efficiently. While it has limitations in factual knowledge and focuses primarily on English, it strikes a balance between performance and efficiency, making it ideal for resource-constrained environments.

Model Specs

Released2024-04-23
Parameters3.8B
Context128k
ArchitectureDecoder Only
Knowledge cutoff2023-10

Provider

Replicate API

Replicate

San Francisco, California, United States