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gpt-oss-120b vs Qwen3.5 397B A17B

Updated October 21, 2026. 50 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 397B A17B
gpt-oss-120bOpenAI
Coding
34.5
Overall
11.6
Output $/M
$0.60
0
Qwen3.5 397B A17BQwen
Coding
28.6
Overall
21.4
Output $/M
$2.34
49
vs boards won of 50 shared, 1 tied

Decision reading

Pick gpt-oss-120b for coding: it scores 34.5 on the Vibe Coding Index against 28.6 for Qwen3.5 397B A17B.

Pick Qwen3.5 397B A17B when the context window is larger, 262K against 131K tokens, or when what matters is SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model Agpt-oss-120bOpenAI34.5/1002 independent benchmarks$0.51 per solved taskModel BQwen3.5 397B A17BQwen28.6/1001 independent benchmarkNo cost per solved task
Shared results
50
gpt-oss-120b only
28
Qwen3.5 397B A17B only
15
Like-for-like categories
8 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 397B A17B 28.6 from 1 benchmark.
Widest category
Web design, 26.5 points apart on average over 6 shared benchmarks.
Widest single gap
Design Arena: 3D: gpt-oss-120b 901 against Qwen3.5 397B A17B 1161, 32.5 points apart on a 0 to 100 scale.
Output price
gpt-oss-120b $0.60 against Qwen3.5 397B A17B $2.34 per million output tokens, 3.9x apart.
Context window
gpt-oss-120b takes 131K 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
Categorygpt-oss-120bBarsQwen3.5 397B A17BTies
Coding05
Intelligence02
Agentic work02
Reasoning07
Math021 tie
Knowledge04
Web design06
General021
All shared boards0491 tie
CodingLike-for-likeFour of these boards feed the Vibe Coding Index05
Coding benchmarks for gpt-oss-120b and Qwen3.5 397B A17B
Benchmarkgpt-oss-120bQwen3.5 397B A17BLeader
Aider polyglot41.8%Not measuredOnly gpt-oss-120b measured
SWE-bench Verified26.0%Not measuredOnly gpt-oss-120b measured
LMArena WebDevNot measured1400Only Qwen3.5 397B A17B measured
AA Coding Index30.4Not measuredOnly gpt-oss-120b measured
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%44.8%Qwen3.5 397B A17B
Terminal-Bench Hard (Artificial Analysis run)23.5%40.9%Qwen3.5 397B A17B
Terminal-Bench18.7%Not measuredOnly gpt-oss-120b measured
WeirdML48.2%Not measuredOnly gpt-oss-120b measured
ALE-Bench575.6621.9Qwen3.5 397B A17B
LMArena Text: coding13791464Qwen3.5 397B A17B
LMArena Text: software and IT13871463Qwen3.5 397B A17B
LMArena WebDev: ReactNot measured1388Only Qwen3.5 397B A17B measured
LMArena WebDev: HTMLNot measured1410Only Qwen3.5 397B A17B measured
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for gpt-oss-120b and Qwen3.5 397B A17B
Benchmarkgpt-oss-120bQwen3.5 397B A17BLeader
AA Intelligence Index11.621.4Qwen3.5 397B A17B
Epoch Capabilities Index139.8146.8Qwen3.5 397B A17B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for gpt-oss-120b and Qwen3.5 397B A17B
Benchmarkgpt-oss-120bQwen3.5 397B A17BLeader
AA Agentic Index3.7Not measuredOnly gpt-oss-120b measured
τ²-Bench Telecom65.8%95.6%Qwen3.5 397B A17B
APEX-Agents4.4%24.9%Qwen3.5 397B A17B
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 index07
Reasoning benchmarks for gpt-oss-120b and Qwen3.5 397B A17B
Benchmarkgpt-oss-120bQwen3.5 397B A17BLeader
GPQA Diamond78.5%Not measuredOnly gpt-oss-120b measured
GPQA Diamond (Artificial Analysis run)78.2%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)19.6%29.0%Qwen3.5 397B A17B
GPQA Diamond (Epoch AI run)75.8%86.4%Qwen3.5 397B A17B
SimpleBench22.1%Not measuredOnly gpt-oss-120b measured
Mystery game puzzles2.0%18.0%Qwen3.5 397B A17B
LMArena Text: hard prompts13641448Qwen3.5 397B A17B
LMArena Text: expert13561462Qwen3.5 397B A17B
LMArena Text: hard prompts in English13681450Qwen3.5 397B A17B
LMArena Vision: diagramsNot measured1277Only Qwen3.5 397B A17B measured
LMArena Vision: homeworkNot measured1292Only Qwen3.5 397B A17B 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 397B A17B
Benchmarkgpt-oss-120bQwen3.5 397B A17BLeader
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%88.9%Tie
FrontierMath, tiers 1 to 3Not measured31.2%Only Qwen3.5 397B A17B measured
LMArena Text: math13891453Qwen3.5 397B A17B
LMArena Text: mathematical work13861454Qwen3.5 397B A17B
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for gpt-oss-120b and Qwen3.5 397B A17B
Benchmarkgpt-oss-120bQwen3.5 397B A17BLeader
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: science13821457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare13881455Qwen3.5 397B A17B
LMArena Text: legal and government13551441Qwen3.5 397B A17B
LMArena Text: business and finance13591435Qwen3.5 397B A17B
Neither model has a result on the other 6 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index06
Web design benchmarks for gpt-oss-120b and Qwen3.5 397B A17B
Benchmarkgpt-oss-120bQwen3.5 397B A17BLeader
Design Arena: websites9681191Qwen3.5 397B A17B
Design Arena: UI components9271163Qwen3.5 397B A17B
Design Arena: game dev9921141Qwen3.5 397B A17B
Design Arena: data viz9871175Qwen3.5 397B A17B
Design Arena: 3D9011161Qwen3.5 397B A17B
Design Arena: all code categories9631180Qwen3.5 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 index021
General benchmarks for gpt-oss-120b and Qwen3.5 397B A17B
Benchmarkgpt-oss-120bQwen3.5 397B A17BLeader
IFBench69.0%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)52.0%77.3%Qwen3.5 397B A17B
DTBench76.3%87.5%Qwen3.5 397B A17B
LMCA22.1%37.9%Qwen3.5 397B A17B
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 Text13661437Qwen3.5 397B A17B
LMArena Text: instruction following13181424Qwen3.5 397B A17B
LMArena Text: creative writing12741402Qwen3.5 397B A17B
LMArena VisionNot measured1262Only Qwen3.5 397B A17B measured
LMArena Text: English13751443Qwen3.5 397B A17B
LMArena Text: non-English13511430Qwen3.5 397B A17B
LMArena Text: Chinese13851498Qwen3.5 397B A17B
LMArena Text: German13531445Qwen3.5 397B A17B
LMArena Text: Japanese13311429Qwen3.5 397B A17B
LMArena Text: Korean12821384Qwen3.5 397B A17B
LMArena Text: French13701462Qwen3.5 397B A17B
LMArena Text: Spanish13891441Qwen3.5 397B A17B
LMArena Text: Russian13431430Qwen3.5 397B A17B
LMArena Text: Polish13531447Qwen3.5 397B A17B
LMArena Text: multi-turn13401446Qwen3.5 397B A17B
LMArena Text: longer queries13191441Qwen3.5 397B A17B
LMArena Text: writing and language13061415Qwen3.5 397B A17B
LMArena Text: entertainment and media12921399Qwen3.5 397B A17B
LMArena Vision: EnglishNot measured1268Only Qwen3.5 397B A17B measured
LMArena Vision: ChineseNot measured1314Only Qwen3.5 397B A17B measured
LMArena Vision: reading textNot measured1271Only Qwen3.5 397B A17B measured
LMArena Vision: humorNot measured1262Only Qwen3.5 397B A17B measured
LMArena Vision: captioningNot measured1230Only Qwen3.5 397B A17B measured
LMArena Vision: recognizing thingsNot measured1265Only Qwen3.5 397B A17B measured
LMArena Vision: creative writingNot measured1260Only Qwen3.5 397B A17B measured
Neither model has a result on the other 14 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 397B A17B
Benchmarkgpt-oss-120bQwen3.5 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

gpt-oss-120b$0.0004Fits in one request
Qwen3.5 397B A17B$0.0016Fits 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 397B A17B$0.027Fits 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 397B A17B$0.101Fits 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 397B A17B
SpecificationOpenAI gpt-oss-120bQwen3.5 397B A17B
MakerOpenAIQwen
WeightsOpen weightsOpen weights
ReleasedAugust 5, 2025February 16, 2026
Context window131K262K
Input $/Mtok$0.15$0.39
Output $/Mtok$0.60$2.34
Coding index34.528.6
Independent measurements behind it21
Cost per solved task$0.51Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is gpt-oss-120b better than Qwen3.5 397B A17B for coding?

Pick gpt-oss-120b for coding: it scores 34.5 on the Vibe Coding Index against 28.6 for Qwen3.5 397B A17B. Pick Qwen3.5 397B A17B when the context window is larger, 262K against 131K tokens, or when what matters is SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, gpt-oss-120b or Qwen3.5 397B A17B?

gpt-oss-120b costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for gpt-oss-120b only.

Which has the bigger context window, gpt-oss-120b or Qwen3.5 397B A17B?

Qwen3.5 397B A17B, with 262K tokens against 131K.

How many benchmarks is the gpt-oss-120b vs Qwen3.5 397B A17B comparison based on?

50 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 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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