Last refreshed 2026-09-21. Next refresh: weekly.
Why use Phi-3 Mini 128K on Fireworks AI?
Fireworks AI offers Phi-3 Mini 128K with pay-as-you-go pricing at $0.10/1M input tokens. Fireworks AI offers a generative AI platform as a service, focusing on rapid product iteration and cost-efficient AI deployment.
Compare Phi-3 Mini 128K across 5 providers to find the best fit for your use caseSetup recipe
Python + curlpip install openaiexport FIREWORKS_API_KEY=...import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],phi-3-mini-128kRequest example
import os
from openai import OpenAI
client = OpenAI(
api_key=os.environ["FIREWORKS_API_KEY"],
base_url="https://api.fireworks.ai/inference/v1"
)
response = client.chat.completions.create(
model="phi-3-mini-128k",
messages=[{"role": "user", "content": "Hello"}]
)
print(response.choices[0].message.content)Gotchas
- Fireworks model IDs use "accounts/fireworks/models/{model-name}" format, e.g. "accounts/fireworks/models/llama4-scout-instruct-basic" or "accounts/fireworks/models/deepseek-r1".
- The examples expect FIREWORKS_API_KEY; rename it only if your application config maps the new variable.
Compare Phi-3 Mini 128K Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| NVIDIA NIM | — | — |
| Baseten API | — | — |
| Microsoft Foundry | $0.30 | $0.90 |
| Fireworks AI | $0.10 | $0.10 |
| Replicate API | $0.05 | $0.25 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.10 |
| Output tokens | $0.10 |
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.