NOUMENOpen app
NOUMEN

microsoft · Open weights

Phi 4 Mini

Added Jul 26, 2025Open weights

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Use in the app

Phi 4 Mini by Microsoft. A small multilingual model.

Input
Text
Output
Text
Discovery tier
Open weights

Pricing

USD per 1M tokens
Input
$0.17
Output
$0.68

Reference prices as published by the model provider. API example

Specifications

Context window
128K
Max output
16.4K
Average output (7 days)
24 tokens
Longer than 0% of 468 models

Side by side

Phi 4 Mini compared

Specifications, prices, speed and benchmark scores next to each other. Benchmarks below show every compared model too.

Compare models

Choose up to 3 models to compare.1 of 3 chosen

Loading models…

Phi 4 MinimicrosoftThis modelMistral SabaMistralRemove
ProvidermicrosoftMistral
Discovery tierOpen weightsOpen weights
AddedJul 26, 2025Jan 1, 2024
Limits
Context window128KBest32.77K
Max output16.38K26.21KBest
Average output24 tokens63 tokens
Pricing, USD per 1M tokens
Input$0.17Best$0.20
Output$0.68$0.60Best
Performance
Request success (24h)100.0%100.0%
Benchmark indexes
Intelligence Index6.36.5Best
Coding Index3.8—
Math Index6.7—
Capabilities
Reasoning effort——

Public evaluations

Benchmarks

Independent evaluation results, with how each score ranks among the models in the Noumen catalog.

  • Phi 4 Mini
  • Mistral Saba

Intelligence Index

  • Phi 4 Mini6.3
  • Mistral Saba6.5

Phi 4 Mini: Better than 4% of 326 scored models

3.8Catalog models by score55.1

Indexes

  • Coding Index

    Composite of the coding evaluations

    • Phi 4 Mini: 3.8
    • Mistral Saba: —

  • Math Index

    Composite of the math evaluations

    • Phi 4 Mini: 6.7
    • Mistral Saba: —

Document reasoning

  • AA-LCRv1.1

    Reasoning across long documents

    • Phi 4 Mini: 15.3%
    • Mistral Saba: —

Reasoning and knowledge

  • Humanity’s Last Exam

    Expert-level questions across academic fields

    • Phi 4 Mini: 4.4%Top
    • Mistral Saba: 4.3%

  • GPQA Diamond

    Graduate-level science questions

    • Phi 4 Mini: 33.1%
    • Mistral Saba: 42.4%Top

  • MMLU-Pro

    Multi-subject knowledge and reasoning

    • Phi 4 Mini: 46.5%
    • Mistral Saba: 61.1%Top

  • IFBench

    Following precise output instructions

    • Phi 4 Mini: 21.1%
    • Mistral Saba: —

Coding

  • LiveCodeBench

    Recent competitive programming problems

    • Phi 4 Mini: 12.6%
    • Mistral Saba: —

Math

  • AIME 2025

    Competition mathematics, 2025 problems

    • Phi 4 Mini: 6.7%
    • Mistral Saba: —

  • AIME

    Competition mathematics

    • Phi 4 Mini: 3.0%
    • Mistral Saba: 13.0%Top

  • MATH-500

    Competition math problems

    • Phi 4 Mini: 69.6%Top
    • Mistral Saba: 67.7%

Legacy evaluations

  • Terminal-Bench Hard

    Earlier terminal task suite

    • Phi 4 Mini: 0.0%
    • Mistral Saba: —

  • τ²-Bench Telecom

    Tool use in a customer-support simulation

    • Phi 4 Mini: 8.2%
    • Mistral Saba: —

  • AA-LCR (unversioned)

    Long-context reasoning, earlier grading

    • Phi 4 Mini: 15.3%
    • Mistral Saba: —

Scores from Artificial Analysis, updated Oct 26, 2026. Artificial Analysis

Live routing data

Performance and reliability

Recent averages across requests routed for this model. Your own speed depends on prompt length, reasoning effort and load.

Prompt cache hits
0.0%
Request success (24h)
100.0%
Reliable

Cache hits are the share of input tokens recently served from a prompt cache across all requests. They are not a forecast of your own hit rate.

Hourly request success · last 72 hoursLowest hour 100.0%
  • Healthy (98% or more)
  • Degraded (90–98%)
  • Down (under 90%)
  • No requests

Developers

Use it through the API

Noumen’s API is OpenAI-compatible: any Chat Completions client works with a base URL and a key.

Read the quickstart
curl https://www.noumen.xyz/api/v1/chat/completions \
  -H "Authorization: Bearer $NOUMEN_API_KEY" \
  -H "Content-Type: application/json" \
  -d '{
    "model": "phi-4-mini-instruct",
    "messages": [{ "role": "user", "content": "Explain this stack trace." }]
  }'
import os
from openai import OpenAI

client = OpenAI(base_url="https://www.noumen.xyz/api/v1", api_key=os.environ["NOUMEN_API_KEY"])
reply = client.chat.completions.create(
    model="phi-4-mini-instruct",
    messages=[{"role": "user", "content": "Explain this stack trace."}],
)
print(reply.choices[0].message.content)

microsoft

Other microsoft models, starting with the same family.