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Phi 4 vs Qwen3.5-122B-A10B

Qwen3.5-122B-A10B leads Phi 4 on the Artificial Analysis Intelligence Index, 17.7 to 5.9, and Phi 4 costs 14.9x less per output token ($0.14 vs $2.08 per million). Updated October 21, 2026; 34 benchmarks have a result for both.

Phi 4Microsoft
Coding
No data
Overall
24.7
Output $/M
$0.14
0
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
34
vs boards won of 34 shared

Phi 4 or Qwen3.5-122B-A10B: which to pick

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

Pick Phi 4 for output tokens 14.9x cheaper ($0.14 vs $2.08 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Shared results
34
Phi 4 only
12
Qwen3.5-122B-A10B only
17
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, Qwen3.5-122B-A10B not benchmarked.
Widest category
Agentic work, 93.6 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: Phi 4 0.0% against Qwen3.5-122B-A10B 93.6%, 93.6 points apart on a 0 to 100 scale.
Output price
Phi 4 $0.14 against Qwen3.5-122B-A10B $2.08 per million output tokens, 14.9x apart.
Context window
Phi 4 takes 16K tokens, Qwen3.5-122B-A10B 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 4BarsQwen3.5-122B-A10BTies
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 Qwen3.5-122B-A10B
BenchmarkPhi 4Qwen3.5-122B-A10BLeader
LMArena WebDevNot measured1361Only Qwen3.5-122B-A10B measured
AA Coding IndexNot measured43.3Only Qwen3.5-122B-A10B measured
LiveCodeBench (Artificial Analysis run)23.1%Not measuredOnly Phi 4 measured
SciCodeNot measured39.7%Only Qwen3.5-122B-A10B measured
Terminal-Bench Hard (Artificial Analysis run)3.8%31.1%Qwen3.5-122B-A10B
LMArena Text: coding12311436Qwen3.5-122B-A10B
LMArena Text: software and IT12281436Qwen3.5-122B-A10B
LMArena WebDev: ReactNot measured1353Only Qwen3.5-122B-A10B measured
LMArena WebDev: HTMLNot measured1351Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Phi 4 and Qwen3.5-122B-A10B
BenchmarkPhi 4Qwen3.5-122B-A10BLeader
AA Intelligence Index5.917.7Qwen3.5-122B-A10B
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 Qwen3.5-122B-A10B
BenchmarkPhi 4Qwen3.5-122B-A10BLeader
τ²-Bench Telecom0.0%93.6%Qwen3.5-122B-A10B
BALROG11.6%Not measuredOnly Phi 4 measured
Berkeley Function Calling Leaderboard (BFCL)28.8%Not measuredOnly Phi 4 measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Phi 4 and Qwen3.5-122B-A10B
BenchmarkPhi 4Qwen3.5-122B-A10BLeader
GPQA Diamond (Artificial Analysis run)57.5%85.7%Qwen3.5-122B-A10B
Humanity's Last Exam (Artificial Analysis run)3.8%25.2%Qwen3.5-122B-A10B
GPQA Diamond (Epoch AI run)56.1%Not measuredOnly Phi 4 measured
Mystery game puzzlesNot measured17.0%Only Qwen3.5-122B-A10B measured
LMArena Text: hard prompts12201421Qwen3.5-122B-A10B
LMArena Text: expert12031431Qwen3.5-122B-A10B
LMArena Text: hard prompts in English12301431Qwen3.5-122B-A10B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-122B-A10B measured
LMArena Vision: homeworkNot measured1261Only Qwen3.5-122B-A10B 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 Qwen3.5-122B-A10B
BenchmarkPhi 4Qwen3.5-122B-A10BLeader
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: math12461431Qwen3.5-122B-A10B
LMArena Text: mathematical work12541431Qwen3.5-122B-A10B
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 Qwen3.5-122B-A10B
BenchmarkPhi 4Qwen3.5-122B-A10BLeader
MMLU Pro (Artificial Analysis run)71.4%Not measuredOnly Phi 4 measured
MMLU84.8%Not measuredOnly Phi 4 measured
LMArena Text: science12061432Qwen3.5-122B-A10B
LMArena Text: medicine and healthcare12021423Qwen3.5-122B-A10B
LMArena Text: legal and government12411422Qwen3.5-122B-A10B
LMArena Text: business and finance11991414Qwen3.5-122B-A10B
Neither model has a result on the other 12 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Phi 4 and Qwen3.5-122B-A10B
BenchmarkPhi 4Qwen3.5-122B-A10BLeader
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 Qwen3.5-122B-A10B
BenchmarkPhi 4Qwen3.5-122B-A10BLeader
IFBench23.5%75.7%Qwen3.5-122B-A10B
Long context reasoning (AA-LCR)0.0%76.3%Qwen3.5-122B-A10B
DTBenchNot measured84.3%Only Qwen3.5-122B-A10B measured
LMCANot measured32.2%Only Qwen3.5-122B-A10B measured
Creative writing (Lech Mazur)62.6%Not measuredOnly Phi 4 measured
LMArena Text12171417Qwen3.5-122B-A10B
LMArena Text: instruction following12011399Qwen3.5-122B-A10B
LMArena Text: creative writing11811368Qwen3.5-122B-A10B
LMArena VisionNot measured1244Only Qwen3.5-122B-A10B measured
LMArena Text: English12311428Qwen3.5-122B-A10B
LMArena Text: non-English11971401Qwen3.5-122B-A10B
LMArena Text: Chinese12121463Qwen3.5-122B-A10B
LMArena Text: German12221425Qwen3.5-122B-A10B
LMArena Text: Japanese11581367Qwen3.5-122B-A10B
LMArena Text: Korean11511353Qwen3.5-122B-A10B
LMArena Text: French12241443Qwen3.5-122B-A10B
LMArena Text: Spanish12341425Qwen3.5-122B-A10B
LMArena Text: Russian12091400Qwen3.5-122B-A10B
LMArena Text: PolishNot measured1398Only Qwen3.5-122B-A10B measured
LMArena Text: multi-turn12061416Qwen3.5-122B-A10B
LMArena Text: longer queries12171410Qwen3.5-122B-A10B
LMArena Text: writing and language11981387Qwen3.5-122B-A10B
LMArena Text: entertainment and media11621366Qwen3.5-122B-A10B
LMArena Vision: EnglishNot measured1249Only Qwen3.5-122B-A10B measured
LMArena Vision: ChineseNot measured1301Only Qwen3.5-122B-A10B measured
LMArena Vision: reading textNot measured1251Only Qwen3.5-122B-A10B measured
LMArena Vision: humorNot measured1243Only Qwen3.5-122B-A10B measured
LMArena Vision: creative writingNot measured1220Only Qwen3.5-122B-A10B measured
Neither model has a result on the other 18 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Phi 4 and Qwen3.5-122B-A10B
BenchmarkPhi 4Qwen3.5-122B-A10BLeader
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
Qwen3.5-122B-A10B$0.0013Fits in one request

Phi 4 costs less

Repository review

50K input + 3K output tokens

Phi 4$0.0039Exceeds the context window
Qwen3.5-122B-A10B$0.019Fits in one request

Phi 4 costs less

Agent loop

200K input + 10K output tokens

Phi 4$0.015Exceeds the context window
Qwen3.5-122B-A10B$0.073Fits 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 Qwen3.5-122B-A10B
SpecificationMicrosoft Phi 4Qwen3.5-122B-A10B
MakerMicrosoftQwen
WeightsOpen weightsOpen weights
ReleasedJanuary 10, 2025February 25, 2026
Context window16K262K
Input $/Mtok$0.07$0.26
Output $/Mtok$0.14$2.08
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Phi 4 better than Qwen3.5-122B-A10B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Phi 4 for output tokens 14.9x cheaper ($0.14 vs $2.08 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 Qwen3.5-122B-A10B?

Phi 4 costs $0.07 per million input tokens and $0.14 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for neither of them.

Which has the bigger context window, Phi 4 or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B, with 262K tokens against 16K.

How many benchmarks is the Phi 4 vs Qwen3.5-122B-A10B 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 Qwen3.5-122B-A10B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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