Model profile
Microsoft Azure

Phi-4 Mini Instruct

Phi-4 Mini Instruct is a budget-priced text-first model from Microsoft Azure with balanced runtime profile, partial context coverage, and the clearest fit around agent workflows / reasoning.

Best for: Agent workflows / ReasoningBalanced latencyN/A contextBudget pricing
Intelligence
8.4

Benchmark blend

Coding
3.6

Dev workflow signal

Context
N/A

N/A

Input Price
$0.00

Budget tier

Decision snapshot
35

Phi-4 Mini Instruct currently reads as a budget text-first option with partially published context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Reasoning
Latency tier
Balanced
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
8.4
8

General reasoning and benchmark headroom.

Limited
Speed
45 tok/s
66

TTFT 0.32s

Competitive
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$0.00
86

$0.00 output / 1M

Efficient

Editorial Profile

Phi-4 Mini Instruct in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 4Math score 7

The Phi-4 Mini Instruct AI model by Microsoft Azure.

Identity

Microsoft Azure text-first profile

Positioning

Agent workflows / Reasoning with partially published context and balanced runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • The available source data suggests a balanced profile rather than one dominant edge.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Latency is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

  • Context limits are only partially published, so long-session planning needs extra validation.

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Context
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Context
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Input Price
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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
8.4
MMLU Pro
46.5%
GPQA
33.1%
HLE
4.2%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
3.6
LiveCodeBench
0.126
SciCode
10.8%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
6.7
AIME
3.0%
AIME 2025
6.7%
Math 500
69.6%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
21.1%
TAU2
8.2%
TerminalBench Hard
0.0%
LCR
13.7%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$0.00
Output
per 1M output tokens
$0.00
Blended
AA 3:1 mix
$0.00

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.