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gpt-oss-120b vs Qwen3.5-27B

Qwen3.5-27B leads gpt-oss-120b on the Artificial Analysis Intelligence Index, 22.9 to 11.6, and gpt-oss-120b costs 3.1x less per output token ($0.50 vs $1.56 per million). Updated October 22, 2026; 40 benchmarks have a result for both.

Replace gpt-oss-120b Replace Qwen3.5-27B
gpt-oss-120bOpenAI
Coding
No data
Overall
50.0
Output $/M
$0.50
2
Qwen3.5-27BQwen
Coding
No data
Overall
No data
Output $/M
$1.56
38
vs boards won of 40 shared

gpt-oss-120b or Qwen3.5-27B: which to pick

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

Pick gpt-oss-120b for output tokens 3.1x cheaper ($0.50 vs $1.56 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Model Agpt-oss-120bOpenAINot benchmarked1 independent benchmark$0.51 per solved taskModel BQwen3.5-27BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
40
gpt-oss-120b only
41
Qwen3.5-27B only
13
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
gpt-oss-120b not benchmarked, Qwen3.5-27B not benchmarked.
Widest category
Agentic work, 14.7 points apart on average over 2 shared benchmarks.
Widest single gap
τ²-Bench Telecom: gpt-oss-120b 65.8% against Qwen3.5-27B 93.9%, 28.1 points apart on a 0 to 100 scale.
Output price
gpt-oss-120b $0.50 against Qwen3.5-27B $1.56 per million output tokens, 3.1x apart.
Context window
gpt-oss-120b takes 131K tokens, Qwen3.5-27B 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-120bBarsQwen3.5-27BTies
Coding23
Intelligence01
Agentic work02
Reasoning05
Math02
Knowledge04
General021
All shared boards238
CodingLike-for-likeSix of these boards feed the Vibe Coding Index23
Coding benchmarks for gpt-oss-120b and Qwen3.5-27B
Benchmarkgpt-oss-120bQwen3.5-27BLeader
Aider polyglot41.8%Not measuredOnly gpt-oss-120b measured
SWE-bench Verified26.0%Not measuredOnly gpt-oss-120b measured
LMArena WebDevNot measured1358Only Qwen3.5-27B 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%Not measuredOnly gpt-oss-120b measured
Terminal-Bench Hard (Artificial Analysis run)23.5%32.6%Qwen3.5-27B
Terminal-Bench18.7%Not measuredOnly gpt-oss-120b measured
WeirdML48.2%39.5%gpt-oss-120b
ALE-Bench575.6349.4gpt-oss-120b
LMArena Text: coding13791427Qwen3.5-27B
LMArena Text: software and IT13871431Qwen3.5-27B
LMArena WebDev: ReactNot measured1346Only Qwen3.5-27B measured
LMArena WebDev: HTMLNot measured1386Only Qwen3.5-27B measured
SciCode7.7%Not measuredOnly gpt-oss-120b 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 Qwen3.5-27B
Benchmarkgpt-oss-120bQwen3.5-27BLeader
AA Intelligence Index11.622.9Qwen3.5-27B
Epoch Capabilities Index139.8Not measuredOnly gpt-oss-120b measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for gpt-oss-120b and Qwen3.5-27B
Benchmarkgpt-oss-120bQwen3.5-27BLeader
AA Agentic Index3.7Not measuredOnly gpt-oss-120b measured
τ²-Bench Telecom65.8%93.9%Qwen3.5-27B
APEX-Agents4.4%Not measuredOnly gpt-oss-120b measured
METR time horizons56.6%Not measuredOnly gpt-oss-120b measured
Vending-Bench 2-$22$202Qwen3.5-27B
BrowseComp-Plus67.0%Not measuredOnly gpt-oss-120b measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for gpt-oss-120b and Qwen3.5-27B
Benchmarkgpt-oss-120bQwen3.5-27BLeader
GPQA Diamond78.5%Not measuredOnly gpt-oss-120b measured
GPQA Diamond (Artificial Analysis run)78.2%85.8%Qwen3.5-27B
Humanity's Last Exam (Artificial Analysis run)19.6%23.9%Qwen3.5-27B
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%Not measuredOnly gpt-oss-120b measured
LMArena Text: hard prompts13641415Qwen3.5-27B
LMArena Text: expert13561427Qwen3.5-27B
LMArena Text: hard prompts in English13681429Qwen3.5-27B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-27B measured
LMArena Vision: homeworkNot measured1256Only Qwen3.5-27B measured
Kagi LLM Benchmark58.6%Not measuredOnly gpt-oss-120b 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 Qwen3.5-27B
Benchmarkgpt-oss-120bQwen3.5-27BLeader
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: math13891429Qwen3.5-27B
LMArena Text: mathematical work13861429Qwen3.5-27B
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 Qwen3.5-27B
Benchmarkgpt-oss-120bQwen3.5-27BLeader
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: science13821437Qwen3.5-27B
LMArena Text: medicine and healthcare13881425Qwen3.5-27B
LMArena Text: legal and government13551405Qwen3.5-27B
LMArena Text: business and finance13591399Qwen3.5-27B
Neither model has a result on the other 7 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 Qwen3.5-27B
Benchmarkgpt-oss-120bQwen3.5-27BLeader
Design Arena: websites968Not measuredOnly gpt-oss-120b measured
Design Arena: UI components924Not 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: 3D900Not measuredOnly gpt-oss-120b measured
Design Arena: all code categories962Not 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 Qwen3.5-27B
Benchmarkgpt-oss-120bQwen3.5-27BLeader
IFBench69.0%75.6%Qwen3.5-27B
Long context reasoning (AA-LCR)52.0%77.7%Qwen3.5-27B
DTBench76.3%82.4%Qwen3.5-27B
LMCA22.1%34.0%Qwen3.5-27B
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 Text13661409Qwen3.5-27B
LMArena Text: instruction following13181393Qwen3.5-27B
LMArena Text: creative writing12741362Qwen3.5-27B
LMArena VisionNot measured1241Only Qwen3.5-27B measured
LMArena Text: English13751425Qwen3.5-27B
LMArena Text: non-English13511390Qwen3.5-27B
LMArena Text: Chinese13851478Qwen3.5-27B
LMArena Text: German13531392Qwen3.5-27B
LMArena Text: Japanese13311346Qwen3.5-27B
LMArena Text: Korean12821359Qwen3.5-27B
LMArena Text: French13701410Qwen3.5-27B
LMArena Text: Spanish13891407Qwen3.5-27B
LMArena Text: Russian13431390Qwen3.5-27B
LMArena Text: Polish13531375Qwen3.5-27B
LMArena Text: multi-turn13401410Qwen3.5-27B
LMArena Text: longer queries13191413Qwen3.5-27B
LMArena Text: writing and language13061380Qwen3.5-27B
LMArena Text: entertainment and media12921363Qwen3.5-27B
LMArena Vision: EnglishNot measured1246Only Qwen3.5-27B measured
LMArena Vision: ChineseNot measured1301Only Qwen3.5-27B measured
LMArena Vision: reading textNot measured1246Only Qwen3.5-27B measured
LMArena Vision: humorNot measured1227Only Qwen3.5-27B measured
LMArena Vision: captioningNot measured1170Only Qwen3.5-27B measured
LMArena Vision: recognizing thingsNot measured1210Only Qwen3.5-27B measured
LMArena Vision: creative writingNot measured1224Only Qwen3.5-27B 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 Qwen3.5-27B
Benchmarkgpt-oss-120bQwen3.5-27BLeader
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$0.0003Fits in one request
Qwen3.5-27B$0.0010Fits in one request

gpt-oss-120b costs less

Repository review

50K input + 3K output tokens

gpt-oss-120b$0.0065Fits in one request
Qwen3.5-27B$0.014Fits in one request

gpt-oss-120b costs less

Agent loop

200K input + 10K output tokens

gpt-oss-120b$0.025Exceeds the context window
Qwen3.5-27B$0.055Fits 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 Qwen3.5-27B
SpecificationOpenAI gpt-oss-120bQwen3.5-27B
MakerOpenAIQwen
WeightsOpen weightsOpen weights
ReleasedAugust 5, 2025February 25, 2026
Context window131K262K
Input $/Mtok$0.10$0.195
Output $/Mtok$0.50$1.56
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it11
Cost per solved task$0.51Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is gpt-oss-120b better than Qwen3.5-27B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick gpt-oss-120b for output tokens 3.1x cheaper ($0.50 vs $1.56 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or LMArena Text: coding, where it leads.

Which is cheaper, gpt-oss-120b or Qwen3.5-27B?

gpt-oss-120b costs $0.10 per million input tokens and $0.50 per million output tokens; Qwen3.5-27B costs $0.195 and $1.56. Cost per solved task is known for gpt-oss-120b only.

Which has the bigger context window, gpt-oss-120b or Qwen3.5-27B?

Qwen3.5-27B, with 262K tokens against 131K.

How many benchmarks is the gpt-oss-120b vs Qwen3.5-27B comparison based on?

40 benchmarks have a result for both models as of October 22, 2026. gpt-oss-120b's coding index rests on 1 independent benchmark and Qwen3.5-27B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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