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Qwen3.5-122B-A10B vs Qwen3.7 Plus

Qwen3.7 Plus leads Qwen3.5-122B-A10B on the Artificial Analysis Intelligence Index, 25.2 to 17.7, and Qwen3.7 Plus costs 1.6x less per output token ($1.28 vs $2.08 per million). Updated October 22, 2026; 48 benchmarks have a result for both.

Replace Qwen3.5-122B-A10B Replace Qwen3.7 Plus
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
3
Qwen3.7 PlusQwen
Coding
33.2
Overall
54.0
Output $/M
$1.28
44
vs boards won of 48 shared, 1 tied

Qwen3.5-122B-A10B or Qwen3.7 Plus: which to pick

Only Qwen3.7 Plus has a coding score, 33.2 on the Vibe Coding Index; Qwen3.5-122B-A10B has none, so the two cannot be ranked on coding.

Pick Qwen3.5-122B-A10B for work measured by τ²-Bench Telecom or Long context reasoning (AA-LCR), where it leads.

Model AQwen3.5-122B-A10BQwenNot benchmarked1 independent benchmarkNo cost per solved taskModel BQwen3.7 PlusQwen33.2/1002 independent benchmarksNo cost per solved task
Shared results
48
Qwen3.5-122B-A10B only
3
Qwen3.7 Plus only
27
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
Qwen3.5-122B-A10B not benchmarked, Qwen3.7 Plus 33.2 from 2 benchmarks.
Widest category
Coding, 9.4 points apart on average over 5 shared benchmarks.
Widest single gap
Terminal-Bench Hard (Artificial Analysis run): Qwen3.5-122B-A10B 31.1% against Qwen3.7 Plus 47.0%, 15.9 points apart on a 0 to 100 scale.
Output price
Qwen3.5-122B-A10B $2.08 against Qwen3.7 Plus $1.28 per million output tokens, 1.6x apart.
Context window
Qwen3.5-122B-A10B takes 262K tokens, Qwen3.7 Plus 1M.

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
CategoryQwen3.5-122B-A10BBarsQwen3.7 PlusTies
Coding05
Intelligence01
Agentic work10
Reasoning071 tie
Math02
Knowledge04
General225
All shared boards3441 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
Coding benchmarks for Qwen3.5-122B-A10B and Qwen3.7 Plus
BenchmarkQwen3.5-122B-A10BQwen3.7 PlusLeader
LMArena WebDev1361Not measuredOnly Qwen3.5-122B-A10B measured
AA Coding Index43.355.9Qwen3.7 Plus
Vibe Code Bench (Vals AI)Not measured46.4%Only Qwen3.7 Plus measured
Code migration (Vals AI)Not measured12.9%Only Qwen3.7 Plus measured
Terminal-Bench 2.1 (Vals AI)Not measured52.8%Only Qwen3.7 Plus measured
SkillsBench (Vals AI)Not measured54.3%Only Qwen3.7 Plus measured
SciCode39.7%46.1%Qwen3.7 Plus
Terminal-Bench Hard (Artificial Analysis run)31.1%47.0%Qwen3.7 Plus
FrontierCodeNot measured10.2%Only Qwen3.7 Plus measured
LMArena Text: coding14361473Qwen3.7 Plus
LMArena Text: software and IT14361473Qwen3.7 Plus
LMArena WebDev: React1353Not measuredOnly Qwen3.5-122B-A10B measured
LMArena WebDev: HTML1351Not measuredOnly 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 Qwen3.5-122B-A10B and Qwen3.7 Plus
BenchmarkQwen3.5-122B-A10BQwen3.7 PlusLeader
AA Intelligence Index17.725.2Qwen3.7 Plus
Epoch Capabilities IndexNot measured147.0Only Qwen3.7 Plus measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for Qwen3.5-122B-A10B and Qwen3.7 Plus
BenchmarkQwen3.5-122B-A10BQwen3.7 PlusLeader
AA Agentic IndexNot measured17.5Only Qwen3.7 Plus measured
Finance Agent (Vals AI)Not measured38.2%Only Qwen3.7 Plus measured
Tax Agent Bench (Vals AI)Not measured10.5%Only Qwen3.7 Plus measured
Legal Research Bench (Vals AI)Not measured16.4%Only Qwen3.7 Plus measured
Excel Modeling Benchmark (Vals AI)Not measured49.3%Only Qwen3.7 Plus measured
τ²-Bench Telecom93.6%93.0%Qwen3.5-122B-A10B
OSWorld 2.0Not measured2.8%Only Qwen3.7 Plus measured
Neither model has a result on the other 35 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for Qwen3.5-122B-A10B and Qwen3.7 Plus
BenchmarkQwen3.5-122B-A10BQwen3.7 PlusLeader
GPQA Diamond (Artificial Analysis run)85.7%90.0%Qwen3.7 Plus
Humanity's Last Exam (Artificial Analysis run)25.2%35.6%Qwen3.7 Plus
GPQA Diamond (Epoch AI run)Not measured87.9%Only Qwen3.7 Plus measured
Mystery game puzzles17.0%17.0%Tie
LMArena Text: hard prompts14211460Qwen3.7 Plus
LMArena Text: expert14311466Qwen3.7 Plus
LMArena Text: hard prompts in English14311460Qwen3.7 Plus
LMArena Vision: diagrams12491292Qwen3.7 Plus
LMArena Vision: homework12611295Qwen3.7 Plus
Neither model has a result on the other 14 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Qwen3.5-122B-A10B and Qwen3.7 Plus
BenchmarkQwen3.5-122B-A10BQwen3.7 PlusLeader
OTIS Mock AIMENot measured93.3%Only Qwen3.7 Plus measured
FrontierMath, tiers 1 to 3Not measured34.4%Only Qwen3.7 Plus measured
LMArena Text: math14311467Qwen3.7 Plus
LMArena Text: mathematical work14311470Qwen3.7 Plus
Neither model has a result on the other 10 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Qwen3.5-122B-A10B and Qwen3.7 Plus
BenchmarkQwen3.5-122B-A10BQwen3.7 PlusLeader
MortgageTax (Vals AI)Not measured66.2%Only Qwen3.7 Plus measured
LMArena Text: science14321464Qwen3.7 Plus
LMArena Text: medicine and healthcare14231464Qwen3.7 Plus
LMArena Text: legal and government14221451Qwen3.7 Plus
LMArena Text: business and finance14141435Qwen3.7 Plus
Neither model has a result on the other 15 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Qwen3.5-122B-A10B and Qwen3.7 Plus
BenchmarkQwen3.5-122B-A10BQwen3.7 PlusLeader
Design Arena: websitesNot measured1269Only Qwen3.7 Plus measured
Design Arena: UI componentsNot measured1258Only Qwen3.7 Plus measured
Design Arena: game devNot measured1262Only Qwen3.7 Plus measured
Design Arena: data vizNot measured1277Only Qwen3.7 Plus measured
Design Arena: 3DNot measured1238Only Qwen3.7 Plus measured
Design Arena: all code categoriesNot measured1268Only Qwen3.7 Plus measured
Design Arena: SVGNot measured1207Only Qwen3.7 Plus measured
Design Arena: ASCII artNot measured1143Only Qwen3.7 Plus measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index225
General benchmarks for Qwen3.5-122B-A10B and Qwen3.7 Plus
BenchmarkQwen3.5-122B-A10BQwen3.7 PlusLeader
SAGE (Vals AI)Not measured39.3%Only Qwen3.7 Plus measured
Vals Multimodal IndexNot measured53.9%Only Qwen3.7 Plus measured
IFBench75.7%78.0%Qwen3.7 Plus
Long context reasoning (AA-LCR)76.3%73.0%Qwen3.5-122B-A10B
DTBench84.3%84.0%Qwen3.5-122B-A10B
LMCA32.2%37.6%Qwen3.7 Plus
LMArena Text14171454Qwen3.7 Plus
LMArena Text: instruction following13991439Qwen3.7 Plus
LMArena Text: creative writing13681438Qwen3.7 Plus
LMArena Vision12441278Qwen3.7 Plus
LMArena Text: English14281458Qwen3.7 Plus
LMArena Text: non-English14011445Qwen3.7 Plus
LMArena Text: Chinese14631509Qwen3.7 Plus
LMArena Text: German14251471Qwen3.7 Plus
LMArena Text: Japanese13671412Qwen3.7 Plus
LMArena Text: Korean13531413Qwen3.7 Plus
LMArena Text: French14431472Qwen3.7 Plus
LMArena Text: Spanish14251457Qwen3.7 Plus
LMArena Text: Russian14001457Qwen3.7 Plus
LMArena Text: Polish13981449Qwen3.7 Plus
LMArena Text: multi-turn14161459Qwen3.7 Plus
LMArena Text: longer queries14101454Qwen3.7 Plus
LMArena Text: writing and language13871437Qwen3.7 Plus
LMArena Text: entertainment and media13661422Qwen3.7 Plus
LMArena Vision: English12491280Qwen3.7 Plus
LMArena Vision: Chinese13011332Qwen3.7 Plus
LMArena Vision: reading text12511288Qwen3.7 Plus
LMArena Vision: humor12431261Qwen3.7 Plus
LMArena Vision: creative writing12201258Qwen3.7 Plus
LMArena DocumentNot measured1444Only Qwen3.7 Plus measured
Neither model has a result on the other 18 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Qwen3.5-122B-A10B and Qwen3.7 Plus
BenchmarkQwen3.5-122B-A10BQwen3.7 PlusLeader
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

Qwen3.5-122B-A10B$0.0013Fits in one request
Qwen3.7 Plus$0.0010Fits in one request

Qwen3.7 Plus costs less

Repository review

50K input + 3K output tokens

Qwen3.5-122B-A10B$0.019Fits in one request
Qwen3.7 Plus$0.020Fits in one request

Qwen3.5-122B-A10B costs less

Agent loop

200K input + 10K output tokens

Qwen3.5-122B-A10B$0.073Fits in one request
Qwen3.7 Plus$0.077Fits 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 Qwen3.5-122B-A10B and Qwen3.7 Plus
SpecificationQwen3.5-122B-A10BQwen3.7 Plus
MakerQwenQwen
WeightsOpen weightsNo public weights listed
ReleasedFebruary 25, 2026June 3, 2026
Context window262K1M
Input $/Mtok$0.26$0.32
Output $/Mtok$2.08$1.28
Coding indexNot benchmarked33.2
Independent measurements behind it12
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is Qwen3.5-122B-A10B better than Qwen3.7 Plus for coding?

Only Qwen3.7 Plus has a coding score, 33.2 on the Vibe Coding Index; Qwen3.5-122B-A10B has none, so the two cannot be ranked on coding. Pick Qwen3.5-122B-A10B for work measured by τ²-Bench Telecom or Long context reasoning (AA-LCR), where it leads.

Which is cheaper, Qwen3.5-122B-A10B or Qwen3.7 Plus?

Qwen3.5-122B-A10B costs $0.26 per million input tokens and $2.08 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for neither of them.

Which has the bigger context window, Qwen3.5-122B-A10B or Qwen3.7 Plus?

Qwen3.7 Plus, with 1M tokens against 262K.

How many benchmarks is the Qwen3.5-122B-A10B vs Qwen3.7 Plus comparison based on?

48 benchmarks have a result for both models as of October 22, 2026. Qwen3.5-122B-A10B's coding index rests on 1 independent benchmark and Qwen3.7 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.

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