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o4 Mini vs Qwen3.5 397B A17B

Qwen3.5 397B A17B leads o4 Mini on the Artificial Analysis Intelligence Index, 21.4 to 16.7, and Qwen3.5 397B A17B costs 1.9x less per output token ($2.34 vs $4.40 per million). Updated October 22, 2026; 58 benchmarks have a result for both.

Replace o4 Mini Replace Qwen3.5 397B A17B
o4 MiniOpenAI
Coding
No data
Overall
43.0
Output $/M
$4.40
1
Qwen3.5 397B A17BQwen
Coding
No data
Overall
57.1
Output $/M
$2.34
57
vs boards won of 58 shared

o4 Mini or Qwen3.5 397B A17B: which to pick

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

Pick o4 Mini for work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model Ao4 MiniOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5 397B A17BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
58
o4 Mini only
40
Qwen3.5 397B A17B only
7
Like-for-like categories
8 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
o4 Mini not benchmarked, Qwen3.5 397B A17B not benchmarked.
Widest category
Agentic work, 40.1 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: o4 Mini 55.6% against Qwen3.5 397B A17B 95.6%, 40.1 points apart on a 0 to 100 scale.
Output price
o4 Mini $4.40 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.9x apart.
Context window
o4 Mini takes 200K tokens, Qwen3.5 397B A17B 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
Categoryo4 MiniBarsQwen3.5 397B A17BTies
Coding03
Intelligence02
Agentic work01
Reasoning09
Math13
Knowledge04
Web design06
General029
All shared boards157
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for o4 Mini and Qwen3.5 397B A17B
Benchmarko4 MiniQwen3.5 397B A17BLeader
Aider polyglot72.0%Not measuredOnly o4 Mini measured
SWE-bench Verified64.6%Not measuredOnly o4 Mini measured
SWE-bench Multimodal33.9%Not measuredOnly o4 Mini measured
LMArena WebDevNot measured1400Only Qwen3.5 397B A17B measured
LiveCodeBench (Vals AI)82.2%Not measuredOnly o4 Mini measured
LiveCodeBench (Artificial Analysis run)85.9%Not measuredOnly o4 Mini measured
SciCodeNot measured44.8%Only Qwen3.5 397B A17B measured
Terminal-Bench Hard (Artificial Analysis run)15.2%40.9%Qwen3.5 397B A17B
GSO-Bench3.6%Not measuredOnly o4 Mini measured
WeirdML52.6%Not measuredOnly o4 Mini measured
ALE-BenchNot measured621.9Only Qwen3.5 397B A17B measured
LMArena Text: coding13681464Qwen3.5 397B A17B
LMArena Text: software and IT13671463Qwen3.5 397B A17B
LMArena WebDev: ReactNot measured1388Only Qwen3.5 397B A17B measured
LMArena WebDev: HTMLNot measured1410Only Qwen3.5 397B A17B measured
SciCode10.8%Not measuredOnly o4 Mini measured
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for o4 Mini and Qwen3.5 397B A17B
Benchmarko4 MiniQwen3.5 397B A17BLeader
AA Intelligence Index16.721.4Qwen3.5 397B A17B
Epoch Capabilities Index145.5146.8Qwen3.5 397B A17B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for o4 Mini and Qwen3.5 397B A17B
Benchmarko4 MiniQwen3.5 397B A17BLeader
τ²-Bench Telecom55.6%95.6%Qwen3.5 397B A17B
APEX-AgentsNot measured24.9%Only Qwen3.5 397B A17B measured
METR time horizons63.9%Not measuredOnly o4 Mini measured
Berkeley Function Calling Leaderboard (BFCL)53.2%Not measuredOnly o4 Mini measured
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index09
Reasoning benchmarks for o4 Mini and Qwen3.5 397B A17B
Benchmarko4 MiniQwen3.5 397B A17BLeader
GPQA Diamond74.5%Not measuredOnly o4 Mini measured
MMMU Pro (Vals AI)79.7%Not measuredOnly o4 Mini measured
GPQA Diamond (Artificial Analysis run)78.4%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)16.5%29.0%Qwen3.5 397B A17B
GPQA Diamond (Epoch AI run)79.6%86.4%Qwen3.5 397B A17B
ARC-AGI58.7%Not measuredOnly o4 Mini measured
ARC-AGI-26.1%Not measuredOnly o4 Mini measured
SimpleBench38.7%Not measuredOnly o4 Mini measured
Chess puzzles22.0%Not measuredOnly o4 Mini measured
Mystery game puzzles5.0%18.0%Qwen3.5 397B A17B
VPCT57.5%Not measuredOnly o4 Mini measured
LMArena Text: hard prompts13511448Qwen3.5 397B A17B
LMArena Text: expert13431462Qwen3.5 397B A17B
LMArena Text: hard prompts in English13661450Qwen3.5 397B A17B
LMArena Vision: diagrams11971277Qwen3.5 397B A17B
LMArena Vision: homework12341292Qwen3.5 397B A17B
Kagi LLM Benchmark67.6%Not measuredOnly o4 Mini measured
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index13
Math benchmarks for o4 Mini and Qwen3.5 397B A17B
Benchmarko4 MiniQwen3.5 397B A17BLeader
AIME83.7%Not measuredOnly o4 Mini measured
MATH 500 (Vals AI)94.2%Not measuredOnly o4 Mini measured
MGSM (Vals AI)93.4%Not measuredOnly o4 Mini measured
AA Math Index90.7Not measuredOnly o4 Mini measured
AIME 2025 (Artificial Analysis run)90.7%Not measuredOnly o4 Mini measured
OTIS Mock AIME81.7%88.9%Qwen3.5 397B A17B
FrontierMath, tiers 1 to 336.1%31.2%o4 Mini
FrontierMath, tier 44.9%Not measuredOnly o4 Mini measured
MATH level 597.8%Not measuredOnly o4 Mini measured
LMArena Text: math13891453Qwen3.5 397B A17B
LMArena Text: mathematical work13791454Qwen3.5 397B A17B
Neither model has a result on the other 2 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for o4 Mini and Qwen3.5 397B A17B
Benchmarko4 MiniQwen3.5 397B A17BLeader
MMLU Pro80.6%Not measuredOnly o4 Mini measured
LegalBench (Vals AI)79.2%Not measuredOnly o4 Mini measured
CorpFin (Vals AI)59.0%Not measuredOnly o4 Mini measured
TaxEval (Vals AI)74.8%Not measuredOnly o4 Mini measured
MortgageTax (Vals AI)64.8%Not measuredOnly o4 Mini measured
MedQA (Vals AI)96.0%Not measuredOnly o4 Mini measured
MedCode (Vals AI)33.8%Not measuredOnly o4 Mini measured
MMLU Pro (Artificial Analysis run)83.2%Not measuredOnly o4 Mini measured
SimpleQA Verified19.6%Not measuredOnly o4 Mini measured
GeoBench64.0%Not measuredOnly o4 Mini measured
LMArena Text: science13541457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare13611455Qwen3.5 397B A17B
LMArena Text: legal and government13501441Qwen3.5 397B A17B
LMArena Text: business and finance13251435Qwen3.5 397B A17B
FACTS Benchmark Suite28.1%Not measuredOnly o4 Mini measured
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index06
Web design benchmarks for o4 Mini and Qwen3.5 397B A17B
Benchmarko4 MiniQwen3.5 397B A17BLeader
Design Arena: websites9851190Qwen3.5 397B A17B
Design Arena: UI components9831160Qwen3.5 397B A17B
Design Arena: game dev10031140Qwen3.5 397B A17B
Design Arena: data viz9911175Qwen3.5 397B A17B
Design Arena: 3D8541160Qwen3.5 397B A17B
Design Arena: all code categories9761179Qwen3.5 397B A17B
Design Arena: SVGNot measured1139Only Qwen3.5 397B A17B measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index029
General benchmarks for o4 Mini and Qwen3.5 397B A17B
Benchmarko4 MiniQwen3.5 397B A17BLeader
SAGE (Vals AI)41.1%Not measuredOnly o4 Mini measured
MedScribe (Vals AI)69.1%Not measuredOnly o4 Mini measured
IFBench68.7%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)61.0%77.3%Qwen3.5 397B A17B
DTBench77.6%87.5%Qwen3.5 397B A17B
LMCA26.5%37.9%Qwen3.5 397B A17B
Fiction.LiveBench77.8%Not measuredOnly o4 Mini measured
Creative writing (Lech Mazur)75.0%Not measuredOnly o4 Mini measured
LMArena Text13531437Qwen3.5 397B A17B
LMArena Text: instruction following13201424Qwen3.5 397B A17B
LMArena Text: creative writing12941402Qwen3.5 397B A17B
LMArena Vision11941262Qwen3.5 397B A17B
LMArena Text: English13671443Qwen3.5 397B A17B
LMArena Text: non-English13371430Qwen3.5 397B A17B
LMArena Text: Chinese13541498Qwen3.5 397B A17B
LMArena Text: German13361445Qwen3.5 397B A17B
LMArena Text: Japanese13081429Qwen3.5 397B A17B
LMArena Text: Korean13121384Qwen3.5 397B A17B
LMArena Text: French13641462Qwen3.5 397B A17B
LMArena Text: Spanish13461441Qwen3.5 397B A17B
LMArena Text: Russian13341430Qwen3.5 397B A17B
LMArena Text: Polish13581447Qwen3.5 397B A17B
LMArena Text: multi-turn13501446Qwen3.5 397B A17B
LMArena Text: longer queries13151441Qwen3.5 397B A17B
LMArena Text: writing and language13161415Qwen3.5 397B A17B
LMArena Text: entertainment and media13081399Qwen3.5 397B A17B
LMArena Vision: English12001268Qwen3.5 397B A17B
LMArena Vision: Chinese12181314Qwen3.5 397B A17B
LMArena Vision: reading text11971271Qwen3.5 397B A17B
LMArena Vision: humor11991262Qwen3.5 397B A17B
LMArena Vision: captioning12041230Qwen3.5 397B A17B
LMArena Vision: recognizing things12181265Qwen3.5 397B A17B
LMArena Vision: creative writing11661260Qwen3.5 397B A17B
Neither model has a result on the other 13 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for o4 Mini and Qwen3.5 397B A17B
Benchmarko4 MiniQwen3.5 397B A17BLeader
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

o4 Mini$0.0033Fits in one request
Qwen3.5 397B A17B$0.0016Fits in one request

Qwen3.5 397B A17B costs less

Repository review

50K input + 3K output tokens

o4 Mini$0.068Fits in one request
Qwen3.5 397B A17B$0.027Fits in one request

Qwen3.5 397B A17B costs less

Agent loop

200K input + 10K output tokens

o4 Mini$0.264Exceeds the context window
Qwen3.5 397B A17B$0.101Fits in one request

Qwen3.5 397B A17B 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 o4 Mini and Qwen3.5 397B A17B
SpecificationOpenAI o4 MiniQwen3.5 397B A17B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedApril 16, 2025February 16, 2026
Context window200K262K
Input $/Mtok$1.10$0.39
Output $/Mtok$4.40$2.34
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is o4 Mini better than Qwen3.5 397B A17B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick o4 Mini for work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, o4 Mini or Qwen3.5 397B A17B?

o4 Mini costs $1.10 per million input tokens and $4.40 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for neither of them.

Which has the bigger context window, o4 Mini or Qwen3.5 397B A17B?

Qwen3.5 397B A17B, with 262K tokens against 200K.

How many benchmarks is the o4 Mini vs Qwen3.5 397B A17B comparison based on?

58 benchmarks have a result for both models as of October 22, 2026. o4 Mini's coding index rests on 0 independent benchmarks and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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