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o4 Mini vs Qwen3.5-122B-A10B

Updated October 21, 2026. 46 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace o4 Mini Replace Qwen3.5-122B-A10B
o4 MiniOpenAI
Coding
68.6
Overall
16.7
Output $/M
$4.40
0
Qwen3.5-122B-A10BQwen
Coding
22.9
Overall
17.7
Output $/M
$2.08
46
vs boards won of 46 shared

Decision reading

Pick o4 Mini for coding: it scores 68.6 on the Vibe Coding Index against 22.9 for Qwen3.5-122B-A10B.

Pick Qwen3.5-122B-A10B when output tokens cost 2.1x less, or when the context window is larger, 262K against 200K tokens.

Model Ao4 MiniOpenAI68.6/1002 independent benchmarksNo cost per solved taskModel BQwen3.5-122B-A10BQwen22.9/1001 independent benchmarkNo cost per solved task
Shared results
46
o4 Mini only
48
Qwen3.5-122B-A10B only
5
Like-for-like categories
7 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 68.6 from 2 benchmarks, Qwen3.5-122B-A10B 22.9 from 1 benchmark.
Widest category
Agentic work, 38.0 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: o4 Mini 55.6% against Qwen3.5-122B-A10B 93.6%, 38.0 points apart on a 0 to 100 scale.
Output price
o4 Mini $4.40 against Qwen3.5-122B-A10B $2.08 per million output tokens, 2.1x apart.
Context window
o4 Mini takes 200K 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
Categoryo4 MiniBarsQwen3.5-122B-A10BTies
Coding03
Intelligence01
Agentic work01
Reasoning08
Math02
Knowledge04
General027
All shared boards046
CodingLike-for-likeFour of these boards feed the Vibe Coding Index03
Coding benchmarks for o4 Mini and Qwen3.5-122B-A10B
Benchmarko4 MiniQwen3.5-122B-A10BLeader
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 measured1361Only Qwen3.5-122B-A10B measured
AA Coding IndexNot measured43.3Only Qwen3.5-122B-A10B measured
LiveCodeBench (Vals AI)82.2%Not measuredOnly o4 Mini measured
LiveCodeBench (Artificial Analysis run)85.9%Not measuredOnly o4 Mini measured
SciCodeNot measured39.7%Only Qwen3.5-122B-A10B measured
Terminal-Bench Hard (Artificial Analysis run)15.2%31.1%Qwen3.5-122B-A10B
GSO-Bench3.6%Not measuredOnly o4 Mini measured
WeirdML52.6%Not measuredOnly o4 Mini measured
LMArena Text: coding13681436Qwen3.5-122B-A10B
LMArena Text: software and IT13671436Qwen3.5-122B-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 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for o4 Mini and Qwen3.5-122B-A10B
Benchmarko4 MiniQwen3.5-122B-A10BLeader
AA Intelligence Index16.717.7Qwen3.5-122B-A10B
Epoch Capabilities Index145.5Not measuredOnly o4 Mini measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for o4 Mini and Qwen3.5-122B-A10B
Benchmarko4 MiniQwen3.5-122B-A10BLeader
τ²-Bench Telecom55.6%93.6%Qwen3.5-122B-A10B
METR time horizons63.9%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 index08
Reasoning benchmarks for o4 Mini and Qwen3.5-122B-A10B
Benchmarko4 MiniQwen3.5-122B-A10BLeader
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%85.7%Qwen3.5-122B-A10B
Humanity's Last Exam (Artificial Analysis run)16.5%25.2%Qwen3.5-122B-A10B
GPQA Diamond (Epoch AI run)79.6%Not measuredOnly o4 Mini measured
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%17.0%Qwen3.5-122B-A10B
VPCT57.5%Not measuredOnly o4 Mini measured
LMArena Text: hard prompts13511421Qwen3.5-122B-A10B
LMArena Text: expert13431431Qwen3.5-122B-A10B
LMArena Text: hard prompts in English13661431Qwen3.5-122B-A10B
LMArena Vision: diagrams11971249Qwen3.5-122B-A10B
LMArena Vision: homework12341261Qwen3.5-122B-A10B
Neither model has a result on the other 5 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for o4 Mini and Qwen3.5-122B-A10B
Benchmarko4 MiniQwen3.5-122B-A10BLeader
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%Not measuredOnly o4 Mini measured
FrontierMath, tiers 1 to 336.1%Not measuredOnly o4 Mini measured
FrontierMath, tier 44.9%Not measuredOnly o4 Mini measured
MATH level 597.8%Not measuredOnly o4 Mini measured
LMArena Text: math13891431Qwen3.5-122B-A10B
LMArena Text: mathematical work13791431Qwen3.5-122B-A10B
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 Qwen3.5-122B-A10B
Benchmarko4 MiniQwen3.5-122B-A10BLeader
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: science13541432Qwen3.5-122B-A10B
LMArena Text: medicine and healthcare13611423Qwen3.5-122B-A10B
LMArena Text: legal and government13501422Qwen3.5-122B-A10B
LMArena Text: business and finance13251414Qwen3.5-122B-A10B
Neither model has a result on the other 3 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for o4 Mini and Qwen3.5-122B-A10B
Benchmarko4 MiniQwen3.5-122B-A10BLeader
Design Arena: websites985Not measuredOnly o4 Mini measured
Design Arena: UI components983Not measuredOnly o4 Mini measured
Design Arena: game dev1004Not measuredOnly o4 Mini measured
Design Arena: data viz990Not measuredOnly o4 Mini measured
Design Arena: 3D854Not measuredOnly o4 Mini measured
Design Arena: all code categories977Not measuredOnly o4 Mini measured
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index027
General benchmarks for o4 Mini and Qwen3.5-122B-A10B
Benchmarko4 MiniQwen3.5-122B-A10BLeader
SAGE (Vals AI)41.1%Not measuredOnly o4 Mini measured
MedScribe (Vals AI)69.1%Not measuredOnly o4 Mini measured
IFBench68.7%75.7%Qwen3.5-122B-A10B
Long context reasoning (AA-LCR)61.0%76.3%Qwen3.5-122B-A10B
DTBench77.6%84.3%Qwen3.5-122B-A10B
LMCA26.5%32.2%Qwen3.5-122B-A10B
Fiction.LiveBench77.8%Not measuredOnly o4 Mini measured
Creative writing (Lech Mazur)75.0%Not measuredOnly o4 Mini measured
LMArena Text13531417Qwen3.5-122B-A10B
LMArena Text: instruction following13201399Qwen3.5-122B-A10B
LMArena Text: creative writing12941368Qwen3.5-122B-A10B
LMArena Vision11941244Qwen3.5-122B-A10B
LMArena Text: English13671428Qwen3.5-122B-A10B
LMArena Text: non-English13371401Qwen3.5-122B-A10B
LMArena Text: Chinese13541463Qwen3.5-122B-A10B
LMArena Text: German13361425Qwen3.5-122B-A10B
LMArena Text: Japanese13081367Qwen3.5-122B-A10B
LMArena Text: Korean13121353Qwen3.5-122B-A10B
LMArena Text: French13641443Qwen3.5-122B-A10B
LMArena Text: Spanish13461425Qwen3.5-122B-A10B
LMArena Text: Russian13341400Qwen3.5-122B-A10B
LMArena Text: Polish13581398Qwen3.5-122B-A10B
LMArena Text: multi-turn13501416Qwen3.5-122B-A10B
LMArena Text: longer queries13151410Qwen3.5-122B-A10B
LMArena Text: writing and language13161387Qwen3.5-122B-A10B
LMArena Text: entertainment and media13081366Qwen3.5-122B-A10B
LMArena Vision: English12001249Qwen3.5-122B-A10B
LMArena Vision: Chinese12181301Qwen3.5-122B-A10B
LMArena Vision: reading text11971251Qwen3.5-122B-A10B
LMArena Vision: humor11991243Qwen3.5-122B-A10B
LMArena Vision: captioning1204Not measuredOnly o4 Mini measured
LMArena Vision: recognizing things1218Not measuredOnly o4 Mini measured
LMArena Vision: creative writing11661220Qwen3.5-122B-A10B
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 Qwen3.5-122B-A10B
Benchmarko4 MiniQwen3.5-122B-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

o4 Mini$0.0033Fits in one request
Qwen3.5-122B-A10B$0.0013Fits in one request

Qwen3.5-122B-A10B costs less

Repository review

50K input + 3K output tokens

o4 Mini$0.068Fits in one request
Qwen3.5-122B-A10B$0.019Fits in one request

Qwen3.5-122B-A10B costs less

Agent loop

200K input + 10K output tokens

o4 Mini$0.264Exceeds the context window
Qwen3.5-122B-A10B$0.073Fits in one request

Qwen3.5-122B-A10B 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 Qwen3.5-122B-A10B
SpecificationOpenAI o4 MiniQwen3.5-122B-A10B
MakerOpenAIQwen
WeightsNo public weights listedOpen weights
ReleasedApril 16, 2025February 25, 2026
Context window200K262K
Input $/Mtok$1.10$0.26
Output $/Mtok$4.40$2.08
Coding index68.622.9
Independent measurements behind it21
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is o4 Mini better than Qwen3.5-122B-A10B for coding?

Pick o4 Mini for coding: it scores 68.6 on the Vibe Coding Index against 22.9 for Qwen3.5-122B-A10B. Pick Qwen3.5-122B-A10B when output tokens cost 2.1x less, or when the context window is larger, 262K against 200K tokens.

Which is cheaper, o4 Mini or Qwen3.5-122B-A10B?

o4 Mini costs $1.10 per million input tokens and $4.40 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, o4 Mini or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B, with 262K tokens against 200K.

How many benchmarks is the o4 Mini vs Qwen3.5-122B-A10B comparison based on?

46 benchmarks have a result for both models as of October 21, 2026. o4 Mini's coding index rests on 2 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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