Enter opens the highlighted result. Esc closes.

DeepSeek V3.2 vs Qwen3.5-122B-A10B

DeepSeek V3.2 leads Qwen3.5-122B-A10B on the Artificial Analysis Intelligence Index, 21.5 to 17.7, and DeepSeek V3.2 costs 2.4x less per output token ($0.88 vs $2.08 per million). Updated October 22, 2026; 41 benchmarks have a result for both.

Replace DeepSeek V3.2 Replace Qwen3.5-122B-A10B
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
37.9
Output $/M
$0.88
29
Qwen3.5-122B-A10BQwen
Coding
No data
Overall
No data
Output $/M
$2.08
12
vs boards won of 41 shared

DeepSeek V3.2 or Qwen3.5-122B-A10B: which to pick

Only DeepSeek V3.2 has a coding score, 30.0 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 a larger context window (262K against 164K tokens) and work measured by LMArena WebDev: React or τ²-Bench Telecom, where it leads.

Model ADeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved taskModel BQwen3.5-122B-A10BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
41
DeepSeek V3.2 only
34
Qwen3.5-122B-A10B only
10
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
DeepSeek V3.2 30.0 from 3 benchmarks, Qwen3.5-122B-A10B not benchmarked.
Widest category
Intelligence, 3.8 points apart on average over 1 shared benchmark.
Widest single gap
IFBench: DeepSeek V3.2 60.7% against Qwen3.5-122B-A10B 75.7%, 15.0 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.88 against Qwen3.5-122B-A10B $2.08 per million output tokens, 2.4x apart.
Context window
DeepSeek V3.2 takes 164K 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
CategoryDeepSeek V3.2BarsQwen3.5-122B-A10BTies
Coding61
Intelligence10
Agentic work01
Reasoning32
Math20
Knowledge31
General147
All shared boards2912
CodingLike-for-likeSix of these boards feed the Vibe Coding Index61
Coding benchmarks for DeepSeek V3.2 and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.2Qwen3.5-122B-A10BLeader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev13621361DeepSeek V3.2
AA Coding Index44.243.3DeepSeek V3.2
LiveCodeBench (Vals AI)80.7%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Verified (Vals AI)67.6%Not measuredOnly DeepSeek V3.2 measured
Vibe Code Bench (Vals AI)5.1%Not measuredOnly DeepSeek V3.2 measured
LiveCodeBench (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
SciCodeNot measured39.7%Only Qwen3.5-122B-A10B measured
Terminal-Bench Hard (Artificial Analysis run)35.6%31.1%DeepSeek V3.2
Terminal-Bench39.6%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: coding14531436DeepSeek V3.2
LMArena Text: software and IT14491436DeepSeek V3.2
LMArena WebDev: React13501353Qwen3.5-122B-A10B
LMArena WebDev: HTML13711351DeepSeek V3.2
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.2Qwen3.5-122B-A10BLeader
AA Intelligence Index21.517.7DeepSeek V3.2
Epoch Capabilities Index146.1Not measuredOnly DeepSeek V3.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.2 and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.2Qwen3.5-122B-A10BLeader
τ²-Bench Telecom90.6%93.6%Qwen3.5-122B-A10B
APEX-Agents21.3%Not measuredOnly DeepSeek V3.2 measured
Vending-Bench 2$1,034Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index32
Reasoning benchmarks for DeepSeek V3.2 and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.2Qwen3.5-122B-A10BLeader
GPQA Diamond80.3%Not measuredOnly DeepSeek V3.2 measured
GPQA Diamond (Artificial Analysis run)84.0%85.7%Qwen3.5-122B-A10B
Humanity's Last Exam (Artificial Analysis run)24.6%25.2%Qwen3.5-122B-A10B
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
Mystery game puzzlesNot measured17.0%Only Qwen3.5-122B-A10B measured
LMArena Text: hard prompts14341421DeepSeek V3.2
LMArena Text: expert14361431DeepSeek V3.2
LMArena Text: hard prompts in English14461431DeepSeek V3.2
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 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for DeepSeek V3.2 and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.2Qwen3.5-122B-A10BLeader
AIME84.6%Not measuredOnly DeepSeek V3.2 measured
MGSM (Vals AI)86.0%Not measuredOnly DeepSeek V3.2 measured
AA Math Index92.0Not measuredOnly DeepSeek V3.2 measured
AIME 2025 (Artificial Analysis run)92.0%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: math14351431DeepSeek V3.2
LMArena Text: mathematical work14401431DeepSeek V3.2
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index31
Knowledge benchmarks for DeepSeek V3.2 and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.2Qwen3.5-122B-A10BLeader
MMLU Pro84.9%Not measuredOnly DeepSeek V3.2 measured
LegalBench (Vals AI)76.1%Not measuredOnly DeepSeek V3.2 measured
CaseLaw (Vals AI)55.4%Not measuredOnly DeepSeek V3.2 measured
CorpFin (Vals AI)51.0%Not measuredOnly DeepSeek V3.2 measured
TaxEval (Vals AI)68.2%Not measuredOnly DeepSeek V3.2 measured
MedQA (Vals AI)93.9%Not measuredOnly DeepSeek V3.2 measured
MMLU Pro (Artificial Analysis run)86.2%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: science14461432DeepSeek V3.2
LMArena Text: medicine and healthcare14401423DeepSeek V3.2
LMArena Text: legal and government14251422DeepSeek V3.2
LMArena Text: business and finance14101414Qwen3.5-122B-A10B
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for DeepSeek V3.2 and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.2Qwen3.5-122B-A10BLeader
Design Arena: websites1175Not measuredOnly DeepSeek V3.2 measured
Design Arena: UI components1146Not measuredOnly DeepSeek V3.2 measured
Design Arena: game dev1131Not measuredOnly DeepSeek V3.2 measured
Design Arena: data viz1158Not measuredOnly DeepSeek V3.2 measured
Design Arena: 3D1132Not measuredOnly DeepSeek V3.2 measured
Design Arena: all code categories1163Not measuredOnly DeepSeek V3.2 measured
Design Arena: SVG1032Not measuredOnly DeepSeek V3.2 measured
Design Arena: ASCII art1083Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index147
General benchmarks for DeepSeek V3.2 and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.2Qwen3.5-122B-A10BLeader
IFBench60.7%75.7%Qwen3.5-122B-A10B
Long context reasoning (AA-LCR)73.3%76.3%Qwen3.5-122B-A10B
DTBench85.6%84.3%DeepSeek V3.2
LMCA28.8%32.2%Qwen3.5-122B-A10B
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251417DeepSeek V3.2
LMArena Text: instruction following14131399DeepSeek V3.2
LMArena Text: creative writing14001368DeepSeek V3.2
LMArena VisionNot measured1244Only Qwen3.5-122B-A10B measured
LMArena Text: English14391428DeepSeek V3.2
LMArena Text: non-English14091401DeepSeek V3.2
LMArena Text: Chinese14551463Qwen3.5-122B-A10B
LMArena Text: German14131425Qwen3.5-122B-A10B
LMArena Text: Japanese13741367DeepSeek V3.2
LMArena Text: Korean13701353DeepSeek V3.2
LMArena Text: French14291443Qwen3.5-122B-A10B
LMArena Text: Spanish14241425Qwen3.5-122B-A10B
LMArena Text: Russian14241400DeepSeek V3.2
LMArena Text: Polish14191398DeepSeek V3.2
LMArena Text: multi-turn14271416DeepSeek V3.2
LMArena Text: longer queries14281410DeepSeek V3.2
LMArena Text: writing and language14071387DeepSeek V3.2
LMArena Text: entertainment and media13951366DeepSeek V3.2
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 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 and Qwen3.5-122B-A10B
BenchmarkDeepSeek V3.2Qwen3.5-122B-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

DeepSeek V3.2$0.0007Fits in one request
Qwen3.5-122B-A10B$0.0013Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.017Fits in one request
Qwen3.5-122B-A10B$0.019Fits in one request

DeepSeek V3.2 costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.067Exceeds the context window
Qwen3.5-122B-A10B$0.073Fits in one request

DeepSeek V3.2 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 DeepSeek V3.2 and Qwen3.5-122B-A10B
SpecificationDeepSeek V3.2Qwen3.5-122B-A10B
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedDecember 1, 2025February 25, 2026
Context window164K262K
Input $/Mtok$0.29$0.26
Output $/Mtok$0.88$2.08
Coding index30.0Not benchmarked
Independent measurements behind it31
Cost per solved task$0.99Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 better than Qwen3.5-122B-A10B for coding?

Only DeepSeek V3.2 has a coding score, 30.0 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 a larger context window (262K against 164K tokens) and work measured by LMArena WebDev: React or τ²-Bench Telecom, where it leads.

Which is cheaper, DeepSeek V3.2 or Qwen3.5-122B-A10B?

DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Qwen3.5-122B-A10B costs $0.26 and $2.08. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, DeepSeek V3.2 or Qwen3.5-122B-A10B?

Qwen3.5-122B-A10B, with 262K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 vs Qwen3.5-122B-A10B comparison based on?

41 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2's coding index rests on 3 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.

Compare next