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A · open · Phi

Phi-3.5-mini

Microsoft

B · open · Phi

Phi-4

Microsoft

1:1

overall capability wins (1 ties across 3 scored). Phi-4 leads overall.

  • Pricing0:3
  • Speed0:2
  • Specs1:1

Verdict

Phi-3.5-mini offers ~8.0x larger context (131K vs 16K); Phi-4 is ~2.6x cheaper on a blended token basis than Phi-3.5-mini.

  • Phi-3.5-mini offers ~8.0x larger context (131K vs 16K)
  • Phi-4 is ~2.6x cheaper on a blended token basis than Phi-3.5-mini

Cost for 1M in + 250K out

Illustrative chat workload at primary-provider list prices.

Phi-3.5-mini
$0.26
Phi-4
$0.11

Phi-4 is about 2.5x cheaper on this workload.

Price per 1M tokens

Full comparison

Organization

Microsoft

Microsoft

Family

Phi

Phi

License

MIT

MIT

Open weights

Yes

Yes

Release

Aug 21, 2024

Dec 12, 2024

Knowledge cutoff

-

-

API / provider

Azure AI Foundry

Azure / self-host

Modalities

text → text

text → text

Specs

Context window

131K

16K

A +115K

Max output

4K

4K

tie

Parameters

3.8B

14B

B +10

Pricing

Input $/1M

$0.13

$0.07

B +$0.06

Output $/1M

$0.52

$0.14

B +$0.38

Blended $/1M (3∶1)

$0.23

$0.09

B +$0.14

Speed

tok/s

130

150

B +20

TTFT (s)

0.2

0.15

B +0.05

Reasoning

MMLU-Pro

—

70.4

—

GPQA Diamond

—

57.5

—

Humanity's Last Exam

—

3.8

—

AIME 2025

—

18.0

—

MATH-500

—

81.0

—

Humanity's Last Exam (with tools)

—

—

—

AA-Omniscience Accuracy

—

14.1

—

AA-LCR v1.1

—

—

—

CritPt

—

—

—

MMMU-Pro

—

—

—

IFBench

—

23.5

—

Chartography

—

—

—

Chartography (With Tools)

—

—

—

Coding

SWE-bench Verified

—

—

—

SWE-bench Pro

—

—

—

SWE-bench Multilingual

—

—

—

LiveCodeBench

—

23.1

—

Terminal-Bench 2.1

—

—

—

Aider Polyglot

—

—

—

Terminal-Bench 3

—

—

—

BigCodeBench

—

45.5

—

SciCode

—

26.0

—

CursorBench

—

—

—

SWE-Rebench

—

—

—

NL2Repo-Bench

—

—

—

DeepSWE

—

—

—

WebDev Arena

—

—

—

Terminal-Bench 4.0

—

—

—

CursorBench 4.0

—

—

—

FrontierCode v1.1 (Main)

—

—

—

Arena

LMArena Elo

—

1,256

—

Phi-3.5-mini

Context
131K / 4K out
Parameters
3.8B
Price
$0.13 / $0.52
Speed
130 tok/s · 0.2s TTFT
Modalities
text → text
License
MIT

Prior Phi SLM generation — still common on Azure for low-cost classification before the Phi-4 refresh.

Phi-4

Context
16K / 4K out
Parameters
14B
Price
$0.07 / $0.14
Speed
150 tok/s · 0.15s TTFT
Modalities
text → text
License
MIT

Small high-quality dense model from Microsoft - punches above its size on STEM.

Benchmark charts

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These two models have no overlapping published benchmarks in our dataset. Compare specs and pricing instead.

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