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gpt-oss-120b vs Qwen3.5-122B-A10B

Updated October 21, 2026. 40 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace gpt-oss-120b Replace Qwen3.5-122B-A10B
gpt-oss-120bOpenAI
Coding
34.5
Overall
11.6
Output $/M
$0.60
0
Qwen3.5-122B-A10BQwen
Coding
22.9
Overall
17.7
Output $/M
$2.08
40
vs boards won of 40 shared

Decision reading

Pick gpt-oss-120b for coding: it scores 34.5 on the Vibe Coding Index against 22.9 for Qwen3.5-122B-A10B.

Pick Qwen3.5-122B-A10B when the context window is larger, 262K against 131K tokens, or when what matters is AA Coding Index or SciCode, where it leads.

Model Agpt-oss-120bOpenAI34.5/1002 independent benchmarks$0.51 per solved taskModel BQwen3.5-122B-A10BQwen22.9/1001 independent benchmarkNo cost per solved task
Shared results
40
gpt-oss-120b only
38
Qwen3.5-122B-A10B only
11
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
gpt-oss-120b 34.5 from 2 benchmarks, Qwen3.5-122B-A10B 22.9 from 1 benchmark.
Widest category
Agentic work, 27.8 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: gpt-oss-120b 65.8% against Qwen3.5-122B-A10B 93.6%, 27.8 points apart on a 0 to 100 scale.
Output price
gpt-oss-120b $0.60 against Qwen3.5-122B-A10B $2.08 per million output tokens, 3.5x apart.
Context window
gpt-oss-120b takes 131K 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
Categorygpt-oss-120bBarsQwen3.5-122B-A10BTies
Coding05
Intelligence01
Agentic work01
Reasoning06
Math02
Knowledge04
General021
All shared boards040
CodingLike-for-likeFour of these boards feed the Vibe Coding Index05
Coding benchmarks for gpt-oss-120b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-120bQwen3.5-122B-A10BLeader
Aider polyglot41.8%Not measuredOnly gpt-oss-120b measured
SWE-bench Verified26.0%Not measuredOnly gpt-oss-120b measured
LMArena WebDevNot measured1361Only Qwen3.5-122B-A10B measured
AA Coding Index30.443.3Qwen3.5-122B-A10B
LiveCodeBench (Vals AI)83.2%Not measuredOnly gpt-oss-120b measured
SWE-bench Verified (Vals AI)33.6%Not measuredOnly gpt-oss-120b measured
LiveCodeBench (Artificial Analysis run)87.8%Not measuredOnly gpt-oss-120b measured
SciCode34.0%39.7%Qwen3.5-122B-A10B
Terminal-Bench Hard (Artificial Analysis run)23.5%31.1%Qwen3.5-122B-A10B
Terminal-Bench18.7%Not measuredOnly gpt-oss-120b measured
WeirdML48.2%Not measuredOnly gpt-oss-120b measured
ALE-Bench575.6Not measuredOnly gpt-oss-120b measured
LMArena Text: coding13791436Qwen3.5-122B-A10B
LMArena Text: software and IT13871436Qwen3.5-122B-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 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for gpt-oss-120b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-120bQwen3.5-122B-A10BLeader
AA Intelligence Index11.617.7Qwen3.5-122B-A10B
Epoch Capabilities Index139.8Not measuredOnly gpt-oss-120b measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for gpt-oss-120b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-120bQwen3.5-122B-A10BLeader
AA Agentic Index3.7Not measuredOnly gpt-oss-120b measured
τ²-Bench Telecom65.8%93.6%Qwen3.5-122B-A10B
APEX-Agents4.4%Not measuredOnly gpt-oss-120b measured
METR time horizons56.6%Not measuredOnly gpt-oss-120b measured
Vending-Bench 2-$22Not measuredOnly gpt-oss-120b measured
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index06
Reasoning benchmarks for gpt-oss-120b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-120bQwen3.5-122B-A10BLeader
GPQA Diamond78.5%Not measuredOnly gpt-oss-120b measured
GPQA Diamond (Artificial Analysis run)78.2%85.7%Qwen3.5-122B-A10B
Humanity's Last Exam (Artificial Analysis run)19.6%25.2%Qwen3.5-122B-A10B
GPQA Diamond (Epoch AI run)75.8%Not measuredOnly gpt-oss-120b measured
SimpleBench22.1%Not measuredOnly gpt-oss-120b measured
Mystery game puzzles2.0%17.0%Qwen3.5-122B-A10B
LMArena Text: hard prompts13641421Qwen3.5-122B-A10B
LMArena Text: expert13561431Qwen3.5-122B-A10B
LMArena Text: hard prompts in English13681431Qwen3.5-122B-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 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for gpt-oss-120b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-120bQwen3.5-122B-A10BLeader
AIME92.6%Not measuredOnly gpt-oss-120b measured
MATH 500 (Vals AI)94.8%Not measuredOnly gpt-oss-120b measured
MGSM (Vals AI)92.0%Not measuredOnly gpt-oss-120b measured
AA Math Index93.4Not measuredOnly gpt-oss-120b measured
AIME 2025 (Artificial Analysis run)93.4%Not measuredOnly gpt-oss-120b measured
OTIS Mock AIME88.9%Not measuredOnly gpt-oss-120b measured
LMArena Text: math13891431Qwen3.5-122B-A10B
LMArena Text: mathematical work13861431Qwen3.5-122B-A10B
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for gpt-oss-120b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-120bQwen3.5-122B-A10BLeader
MMLU Pro79.2%Not measuredOnly gpt-oss-120b measured
LegalBench (Vals AI)75.9%Not measuredOnly gpt-oss-120b measured
CaseLaw (Vals AI)48.8%Not measuredOnly gpt-oss-120b measured
CorpFin (Vals AI)58.2%Not measuredOnly gpt-oss-120b measured
TaxEval (Vals AI)71.6%Not measuredOnly gpt-oss-120b measured
MedQA (Vals AI)91.4%Not measuredOnly gpt-oss-120b measured
MMLU Pro (Artificial Analysis run)80.8%Not measuredOnly gpt-oss-120b measured
LMArena Text: science13821432Qwen3.5-122B-A10B
LMArena Text: medicine and healthcare13881423Qwen3.5-122B-A10B
LMArena Text: legal and government13551422Qwen3.5-122B-A10B
LMArena Text: business and finance13591414Qwen3.5-122B-A10B
Neither model has a result on the other 6 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Web design benchmarks for gpt-oss-120b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-120bQwen3.5-122B-A10BLeader
Design Arena: websites968Not measuredOnly gpt-oss-120b measured
Design Arena: UI components927Not measuredOnly gpt-oss-120b measured
Design Arena: game dev992Not measuredOnly gpt-oss-120b measured
Design Arena: data viz987Not measuredOnly gpt-oss-120b measured
Design Arena: 3D901Not measuredOnly gpt-oss-120b measured
Design Arena: all code categories963Not measuredOnly gpt-oss-120b measured
Neither model has a result on the other 3 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index021
General benchmarks for gpt-oss-120b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-120bQwen3.5-122B-A10BLeader
IFBench69.0%75.7%Qwen3.5-122B-A10B
Long context reasoning (AA-LCR)52.0%76.3%Qwen3.5-122B-A10B
DTBench76.3%84.3%Qwen3.5-122B-A10B
LMCA22.1%32.2%Qwen3.5-122B-A10B
Fiction.LiveBench44.4%Not measuredOnly gpt-oss-120b measured
Creative writing (Lech Mazur)77.1%Not measuredOnly gpt-oss-120b measured
Surface Evolver Bench25.0%Not measuredOnly gpt-oss-120b measured
LMArena Text13661417Qwen3.5-122B-A10B
LMArena Text: instruction following13181399Qwen3.5-122B-A10B
LMArena Text: creative writing12741368Qwen3.5-122B-A10B
LMArena VisionNot measured1244Only Qwen3.5-122B-A10B measured
LMArena Text: English13751428Qwen3.5-122B-A10B
LMArena Text: non-English13511401Qwen3.5-122B-A10B
LMArena Text: Chinese13851463Qwen3.5-122B-A10B
LMArena Text: German13531425Qwen3.5-122B-A10B
LMArena Text: Japanese13311367Qwen3.5-122B-A10B
LMArena Text: Korean12821353Qwen3.5-122B-A10B
LMArena Text: French13701443Qwen3.5-122B-A10B
LMArena Text: Spanish13891425Qwen3.5-122B-A10B
LMArena Text: Russian13431400Qwen3.5-122B-A10B
LMArena Text: Polish13531398Qwen3.5-122B-A10B
LMArena Text: multi-turn13401416Qwen3.5-122B-A10B
LMArena Text: longer queries13191410Qwen3.5-122B-A10B
LMArena Text: writing and language13061387Qwen3.5-122B-A10B
LMArena Text: entertainment and media12921366Qwen3.5-122B-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 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for gpt-oss-120b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-120bQwen3.5-122B-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

gpt-oss-120b$0.0004Fits in one request
Qwen3.5-122B-A10B$0.0013Fits in one request

gpt-oss-120b costs less

Repository review

50K input + 3K output tokens

gpt-oss-120b$0.0093Fits in one request
Qwen3.5-122B-A10B$0.019Fits in one request

gpt-oss-120b costs less

Agent loop

200K input + 10K output tokens

gpt-oss-120b$0.036Exceeds the context window
Qwen3.5-122B-A10B$0.073Fits in one request

gpt-oss-120b 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 gpt-oss-120b and Qwen3.5-122B-A10B
SpecificationOpenAI gpt-oss-120bQwen3.5-122B-A10B
MakerOpenAIQwen
WeightsOpen weightsOpen weights
ReleasedAugust 5, 2025February 25, 2026
Context window131K262K
Input $/Mtok$0.15$0.26
Output $/Mtok$0.60$2.08
Coding index34.522.9
Independent measurements behind it21
Cost per solved task$0.51Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is gpt-oss-120b better than Qwen3.5-122B-A10B for coding?

Pick gpt-oss-120b for coding: it scores 34.5 on the Vibe Coding Index against 22.9 for Qwen3.5-122B-A10B. Pick Qwen3.5-122B-A10B when the context window is larger, 262K against 131K tokens, or when what matters is AA Coding Index or SciCode, where it leads.

Which is cheaper, gpt-oss-120b or Qwen3.5-122B-A10B?

gpt-oss-120b costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for gpt-oss-120b only.

Which has the bigger context window, gpt-oss-120b or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B, with 262K tokens against 131K.

How many benchmarks is the gpt-oss-120b vs Qwen3.5-122B-A10B comparison based on?

40 benchmarks have a result for both models as of October 21, 2026. gpt-oss-120b'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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