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Phi 4 vs Qwen3.5-27B

Qwen3.5-27B leads Phi 4 on the Artificial Analysis Intelligence Index, 22.9 to 5.9, and Phi 4 costs 11.1x less per output token ($0.14 vs $1.56 per million). Updated October 21, 2026; 34 benchmarks have a result for both.

Replace Phi 4 Replace Qwen3.5-27B
Phi 4Microsoft
Coding
No data
Overall
5.9
Output $/M
$0.14
0
Qwen3.5-27BQwen
Coding
No data
Overall
22.9
Output $/M
$1.56
34
vs boards won of 34 shared

Phi 4 or Qwen3.5-27B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick Phi 4 for output tokens 11.1x cheaper ($0.14 vs $1.56 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model APhi 4MicrosoftNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-27BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
34
Phi 4 only
11
Qwen3.5-27B only
19
Like-for-like categories
7 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
Phi 4 not benchmarked, Qwen3.5-27B not benchmarked.
Widest category
Agentic work, 93.9 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Phi 4 0.0% against Qwen3.5-27B 93.9%, 93.9 points apart on a 0 to 100 scale.
Output price
Phi 4 $0.14 against Qwen3.5-27B $1.56 per million output tokens, 11.1x apart.
Context window
Phi 4 takes 16K tokens, Qwen3.5-27B 262K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryPhi 4BarsQwen3.5-27BTies
Coding03
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
General018
All shared boards034
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for Phi 4 and Qwen3.5-27B
BenchmarkPhi 4Qwen3.5-27BLeader
LMArena WebDevNot measured1358Only Qwen3.5-27B measured
LiveCodeBench (Artificial Analysis run)23.1%Not measuredOnly Phi 4 measured
Terminal-Bench Hard (Artificial Analysis run)3.8%32.6%Qwen3.5-27B
WeirdMLNot measured39.5%Only Qwen3.5-27B measured
ALE-BenchNot measured349.4Only Qwen3.5-27B measured
LMArena Text: coding12311427Qwen3.5-27B
LMArena Text: software and IT12281431Qwen3.5-27B
LMArena WebDev: ReactNot measured1346Only Qwen3.5-27B measured
LMArena WebDev: HTMLNot measured1386Only Qwen3.5-27B measured
Neither model has a result on the other 29 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Phi 4 and Qwen3.5-27B
BenchmarkPhi 4Qwen3.5-27BLeader
AA Intelligence Index5.922.9Qwen3.5-27B
Epoch Capabilities Index130.5Not measuredOnly Phi 4 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Phi 4 and Qwen3.5-27B
BenchmarkPhi 4Qwen3.5-27BLeader
τ²-Bench Telecom0.0%93.9%Qwen3.5-27B
BALROG11.6%Not measuredOnly Phi 4 measured
Vending-Bench 2Not measured$202Only Qwen3.5-27B measured
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Phi 4 and Qwen3.5-27B
BenchmarkPhi 4Qwen3.5-27BLeader
GPQA Diamond (Artificial Analysis run)57.5%85.8%Qwen3.5-27B
Humanity's Last Exam (Artificial Analysis run)3.8%23.9%Qwen3.5-27B
GPQA Diamond (Epoch AI run)56.1%Not measuredOnly Phi 4 measured
LMArena Text: hard prompts12201415Qwen3.5-27B
LMArena Text: expert12031427Qwen3.5-27B
LMArena Text: hard prompts in English12301429Qwen3.5-27B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-27B measured
LMArena Vision: homeworkNot measured1256Only Qwen3.5-27B measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Phi 4 and Qwen3.5-27B
BenchmarkPhi 4Qwen3.5-27BLeader
AA Math Index18.0Not measuredOnly Phi 4 measured
AIME 2025 (Artificial Analysis run)18.0%Not measuredOnly Phi 4 measured
OTIS Mock AIME13.8%Not measuredOnly Phi 4 measured
MATH level 564.9%Not measuredOnly Phi 4 measured
LMArena Text: math12461429Qwen3.5-27B
LMArena Text: mathematical work12541429Qwen3.5-27B
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Phi 4 and Qwen3.5-27B
BenchmarkPhi 4Qwen3.5-27BLeader
MMLU Pro (Artificial Analysis run)71.4%Not measuredOnly Phi 4 measured
MMLU84.8%Not measuredOnly Phi 4 measured
LMArena Text: science12061437Qwen3.5-27B
LMArena Text: medicine and healthcare12021425Qwen3.5-27B
LMArena Text: legal and government12411405Qwen3.5-27B
LMArena Text: business and finance11991399Qwen3.5-27B
Neither model has a result on the other 11 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Phi 4 and Qwen3.5-27B
BenchmarkPhi 4Qwen3.5-27BLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
General benchmarks for Phi 4 and Qwen3.5-27B
BenchmarkPhi 4Qwen3.5-27BLeader
IFBench23.5%75.6%Qwen3.5-27B
Long context reasoning (AA-LCR)0.0%77.7%Qwen3.5-27B
DTBenchNot measured82.4%Only Qwen3.5-27B measured
LMCANot measured34.0%Only Qwen3.5-27B measured
Creative writing (Lech Mazur)62.6%Not measuredOnly Phi 4 measured
LMArena Text12171409Qwen3.5-27B
LMArena Text: instruction following12011393Qwen3.5-27B
LMArena Text: creative writing11811362Qwen3.5-27B
LMArena VisionNot measured1241Only Qwen3.5-27B measured
LMArena Text: English12311425Qwen3.5-27B
LMArena Text: non-English11971390Qwen3.5-27B
LMArena Text: Chinese12121478Qwen3.5-27B
LMArena Text: German12221392Qwen3.5-27B
LMArena Text: Japanese11581346Qwen3.5-27B
LMArena Text: Korean11511359Qwen3.5-27B
LMArena Text: French12241410Qwen3.5-27B
LMArena Text: Spanish12341407Qwen3.5-27B
LMArena Text: Russian12091390Qwen3.5-27B
LMArena Text: PolishNot measured1375Only Qwen3.5-27B measured
LMArena Text: multi-turn12061410Qwen3.5-27B
LMArena Text: longer queries12171413Qwen3.5-27B
LMArena Text: writing and language11981380Qwen3.5-27B
LMArena Text: entertainment and media11621363Qwen3.5-27B
LMArena Vision: EnglishNot measured1246Only Qwen3.5-27B measured
LMArena Vision: ChineseNot measured1301Only Qwen3.5-27B measured
LMArena Vision: reading textNot measured1246Only Qwen3.5-27B measured
LMArena Vision: humorNot measured1227Only Qwen3.5-27B measured
LMArena Vision: captioningNot measured1170Only Qwen3.5-27B measured
LMArena Vision: recognizing thingsNot measured1210Only Qwen3.5-27B measured
LMArena Vision: creative writingNot measured1224Only Qwen3.5-27B measured
Neither model has a result on the other 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Phi 4 and Qwen3.5-27B
BenchmarkPhi 4Qwen3.5-27BLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

Phi 4$0.0001Fits in one request
Qwen3.5-27B$0.0010Fits in one request

Phi 4 costs less

Repository review

50K input + 3K output tokens

Phi 4$0.0039Exceeds the context window
Qwen3.5-27B$0.014Fits in one request

Phi 4 costs less

Agent loop

200K input + 10K output tokens

Phi 4$0.015Exceeds the context window
Qwen3.5-27B$0.055Fits in one request

Phi 4 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of Phi 4 and Qwen3.5-27B
SpecificationMicrosoft Phi 4Qwen3.5-27B
MakerMicrosoftQwen
WeightsOpen weightsOpen weights
ReleasedJanuary 10, 2025February 25, 2026
Context window16K262K
Input $/Mtok$0.07$0.195
Output $/Mtok$0.14$1.56
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Phi 4 better than Qwen3.5-27B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Phi 4 for output tokens 11.1x cheaper ($0.14 vs $1.56 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, Phi 4 or Qwen3.5-27B?

Phi 4 costs $0.07 per million input tokens and $0.14 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for neither of them.

Which has the bigger context window, Phi 4 or Qwen3.5-27B?

Qwen3.5-27B, with 262K tokens against 16K.

How many benchmarks is the Phi 4 vs Qwen3.5-27B comparison based on?

34 benchmarks have a result for both models as of October 21, 2026. Phi 4's coding index rests on 0 independent benchmarks and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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