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gpt-oss-20b vs Qwen3.5-122B-A10B

Qwen3.5-122B-A10B leads gpt-oss-20b on the Artificial Analysis Intelligence Index, 17.7 to 10.0, and gpt-oss-20b costs 13.9x less per output token ($0.15 vs $2.08 per million). Updated October 22, 2026; 37 benchmarks have a result for both.

Replace gpt-oss-20b Replace Qwen3.5-122B-A10B
gpt-oss-20bOpenAI
Coding
No data
Overall
38.5
Output $/M
$0.15
0
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
37
vs boards won of 37 shared

gpt-oss-20b or Qwen3.5-122B-A10B: which to pick

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

Pick gpt-oss-20b for output tokens 13.9x cheaper ($0.15 vs $2.08 per million) and work measured by SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model Agpt-oss-20bOpenAINot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-122B-A10BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
37
gpt-oss-20b only
26
Qwen3.5-122B-A10B only
14
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-20b not benchmarked, Qwen3.5-122B-A10B not benchmarked.
Widest category
Agentic work, 33.3 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): gpt-oss-20b 34.7% against Qwen3.5-122B-A10B 76.3%, 41.7 points apart on a 0 to 100 scale.
Output price
gpt-oss-20b $0.15 against Qwen3.5-122B-A10B $2.08 per million output tokens, 13.9x apart.
Context window
gpt-oss-20b 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-20bBarsQwen3.5-122B-A10BTies
Coding04
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
General020
All shared boards037
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for gpt-oss-20b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-20bQwen3.5-122B-A10BLeader
LMArena WebDevNot measured1361Only Qwen3.5-122B-A10B measured
AA Coding IndexNot measured43.3Only Qwen3.5-122B-A10B measured
LiveCodeBench (Vals AI)80.4%Not measuredOnly gpt-oss-20b measured
LiveCodeBench (Artificial Analysis run)77.7%Not measuredOnly gpt-oss-20b measured
SciCode38.9%39.7%Qwen3.5-122B-A10B
Terminal-Bench Hard (Artificial Analysis run)10.6%31.1%Qwen3.5-122B-A10B
Terminal-Bench3.4%Not measuredOnly gpt-oss-20b measured
WeirdML40.9%Not measuredOnly gpt-oss-20b measured
ALE-Bench566.0Not measuredOnly gpt-oss-20b measured
LMArena Text: coding13071436Qwen3.5-122B-A10B
LMArena Text: software and IT13111436Qwen3.5-122B-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 30 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for gpt-oss-20b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-20bQwen3.5-122B-A10BLeader
AA Intelligence Index10.017.7Qwen3.5-122B-A10B
Epoch Capabilities Index137.7Not measuredOnly gpt-oss-20b measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for gpt-oss-20b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-20bQwen3.5-122B-A10BLeader
τ²-Bench Telecom60.2%93.6%Qwen3.5-122B-A10B
BrowseComp-Plus50.6%Not measuredOnly gpt-oss-20b measured
Neither model has a result on the other 40 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for gpt-oss-20b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-20bQwen3.5-122B-A10BLeader
GPQA Diamond68.9%Not measuredOnly gpt-oss-20b measured
GPQA Diamond (Artificial Analysis run)68.8%85.7%Qwen3.5-122B-A10B
Humanity's Last Exam (Artificial Analysis run)11.0%25.2%Qwen3.5-122B-A10B
GPQA Diamond (Epoch AI run)60.8%Not measuredOnly gpt-oss-20b measured
Mystery game puzzlesNot measured17.0%Only Qwen3.5-122B-A10B measured
LMArena Text: hard prompts12741421Qwen3.5-122B-A10B
LMArena Text: expert12581431Qwen3.5-122B-A10B
LMArena Text: hard prompts in English12921431Qwen3.5-122B-A10B
LMArena Vision: diagramsNot measured1249Only Qwen3.5-122B-A10B measured
LMArena Vision: homeworkNot measured1261Only Qwen3.5-122B-A10B measured
Kagi LLM Benchmark53.2%Not measuredOnly gpt-oss-20b measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for gpt-oss-20b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-20bQwen3.5-122B-A10BLeader
AIME86.0%Not measuredOnly gpt-oss-20b measured
MATH 500 (Vals AI)94.2%Not measuredOnly gpt-oss-20b measured
MGSM (Vals AI)89.0%Not measuredOnly gpt-oss-20b measured
AA Math Index89.3Not measuredOnly gpt-oss-20b measured
AIME 2025 (Artificial Analysis run)89.3%Not measuredOnly gpt-oss-20b measured
OTIS Mock AIME65.3%Not measuredOnly gpt-oss-20b measured
LMArena Text: math13171431Qwen3.5-122B-A10B
LMArena Text: mathematical work13051431Qwen3.5-122B-A10B
MathArena12.8%Not measuredOnly gpt-oss-20b measured
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-20b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-20bQwen3.5-122B-A10BLeader
MMLU Pro71.6%Not measuredOnly gpt-oss-20b measured
LegalBench (Vals AI)70.8%Not measuredOnly gpt-oss-20b measured
CaseLaw (Vals AI)43.8%Not measuredOnly gpt-oss-20b measured
CorpFin (Vals AI)53.1%Not measuredOnly gpt-oss-20b measured
TaxEval (Vals AI)63.7%Not measuredOnly gpt-oss-20b measured
MedQA (Vals AI)82.9%Not measuredOnly gpt-oss-20b measured
MMLU Pro (Artificial Analysis run)74.8%Not measuredOnly gpt-oss-20b measured
LMArena Text: science12961432Qwen3.5-122B-A10B
LMArena Text: medicine and healthcare13101423Qwen3.5-122B-A10B
LMArena Text: legal and government12801422Qwen3.5-122B-A10B
LMArena Text: business and finance12841414Qwen3.5-122B-A10B
Neither model has a result on the other 9 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
Web design benchmarks for gpt-oss-20b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-20bQwen3.5-122B-A10BLeader
Design Arena: websites852Not measuredOnly gpt-oss-20b measured
Design Arena: data viz933Not measuredOnly gpt-oss-20b measured
Neither model has a result on the other 7 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for gpt-oss-20b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-20bQwen3.5-122B-A10BLeader
IFBench65.1%75.7%Qwen3.5-122B-A10B
Long context reasoning (AA-LCR)34.7%76.3%Qwen3.5-122B-A10B
DTBench68.0%84.3%Qwen3.5-122B-A10B
LMCA14.5%32.2%Qwen3.5-122B-A10B
LMArena Text12881417Qwen3.5-122B-A10B
LMArena Text: instruction following12361399Qwen3.5-122B-A10B
LMArena Text: creative writing12011368Qwen3.5-122B-A10B
LMArena VisionNot measured1244Only Qwen3.5-122B-A10B measured
LMArena Text: English13031428Qwen3.5-122B-A10B
LMArena Text: non-English12691401Qwen3.5-122B-A10B
LMArena Text: Chinese13151463Qwen3.5-122B-A10B
LMArena Text: German12551425Qwen3.5-122B-A10B
LMArena Text: Japanese12441367Qwen3.5-122B-A10B
LMArena Text: Korean12361353Qwen3.5-122B-A10B
LMArena Text: FrenchNot measured1443Only Qwen3.5-122B-A10B measured
LMArena Text: Spanish12671425Qwen3.5-122B-A10B
LMArena Text: Russian12781400Qwen3.5-122B-A10B
LMArena Text: Polish12521398Qwen3.5-122B-A10B
LMArena Text: multi-turn12681416Qwen3.5-122B-A10B
LMArena Text: longer queries12501410Qwen3.5-122B-A10B
LMArena Text: writing and language12381387Qwen3.5-122B-A10B
LMArena Text: entertainment and media12161366Qwen3.5-122B-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 21 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for gpt-oss-20b and Qwen3.5-122B-A10B
Benchmarkgpt-oss-20bQwen3.5-122B-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-20b$0.0001Fits in one request
Qwen3.5-122B-A10B$0.0013Fits in one request

gpt-oss-20b costs less

Repository review

50K input + 3K output tokens

gpt-oss-20b$0.0019Fits in one request
Qwen3.5-122B-A10B$0.019Fits in one request

gpt-oss-20b costs less

Agent loop

200K input + 10K output tokens

gpt-oss-20b$0.0075Exceeds the context window
Qwen3.5-122B-A10B$0.073Fits in one request

gpt-oss-20b 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-20b and Qwen3.5-122B-A10B
SpecificationOpenAI gpt-oss-20bQwen3.5-122B-A10B
MakerOpenAIQwen
WeightsOpen weightsOpen weights
ReleasedAugust 5, 2025February 25, 2026
Context window131K262K
Input $/Mtok$0.03$0.26
Output $/Mtok$0.15$2.08
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is gpt-oss-20b better than Qwen3.5-122B-A10B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick gpt-oss-20b for output tokens 13.9x cheaper ($0.15 vs $2.08 per million) and work measured by SciCode or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, gpt-oss-20b or Qwen3.5-122B-A10B?

gpt-oss-20b costs $0.03 per million input tokens and $0.15 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, gpt-oss-20b or Qwen3.5-122B-A10B?

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

How many benchmarks is the gpt-oss-20b vs Qwen3.5-122B-A10B comparison based on?

37 benchmarks have a result for both models as of October 22, 2026. gpt-oss-20b's coding index rests on 0 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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