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DeepSeek V3.2 vs Qwen3.5 397B A17B

DeepSeek V3.2 leads Qwen3.5 397B A17B on the Artificial Analysis Intelligence Index, 21.5 to 21.4, and DeepSeek V3.2 costs 2.7x less per output token ($0.88 vs $2.34 per million). Updated October 22, 2026; 49 benchmarks have a result for both.

Replace DeepSeek V3.2 Replace Qwen3.5 397B A17B
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
37.9
Output $/M
$0.88
1
Qwen3.5 397B A17BQwen
Coding
No data
Overall
57.1
Output $/M
$2.34
48
vs boards won of 49 shared

DeepSeek V3.2 or Qwen3.5 397B A17B: which to pick

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Qwen3.5 397B A17B has none, so the two cannot be ranked on coding.

Pick Qwen3.5 397B A17B for a larger context window (262K against 164K tokens) and work measured by LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model ADeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved taskModel BQwen3.5 397B A17BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
49
DeepSeek V3.2 only
26
Qwen3.5 397B A17B only
16
Like-for-like categories
8 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 397B A17B not benchmarked.
Widest category
Coding, 4.6 points apart on average over 6 shared benchmarks.
Widest single gap
IFBench: DeepSeek V3.2 60.7% against Qwen3.5 397B A17B 78.8%, 18.1 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.88 against Qwen3.5 397B A17B $2.34 per million output tokens, 2.7x apart.
Context window
DeepSeek V3.2 takes 164K 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
CategoryDeepSeek V3.2BarsQwen3.5 397B A17BTies
Coding06
Intelligence11
Agentic work02
Reasoning05
Math02
Knowledge04
Web design07
General021
All shared boards148
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
Coding benchmarks for DeepSeek V3.2 and Qwen3.5 397B A17B
BenchmarkDeepSeek V3.2Qwen3.5 397B A17BLeader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev13621400Qwen3.5 397B A17B
AA Coding Index44.2Not measuredOnly DeepSeek V3.2 measured
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 measured44.8%Only Qwen3.5 397B A17B measured
Terminal-Bench Hard (Artificial Analysis run)35.6%40.9%Qwen3.5 397B A17B
Terminal-Bench39.6%Not measuredOnly DeepSeek V3.2 measured
ALE-BenchNot measured621.9Only Qwen3.5 397B A17B measured
LMArena Text: coding14531464Qwen3.5 397B A17B
LMArena Text: software and IT14491463Qwen3.5 397B A17B
LMArena WebDev: React13501388Qwen3.5 397B A17B
LMArena WebDev: HTML13711410Qwen3.5 397B A17B
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
Intelligence benchmarks for DeepSeek V3.2 and Qwen3.5 397B A17B
BenchmarkDeepSeek V3.2Qwen3.5 397B A17BLeader
AA Intelligence Index21.521.4DeepSeek V3.2
Epoch Capabilities Index146.1146.8Qwen3.5 397B A17B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index02
Agentic work benchmarks for DeepSeek V3.2 and Qwen3.5 397B A17B
BenchmarkDeepSeek V3.2Qwen3.5 397B A17BLeader
τ²-Bench Telecom90.6%95.6%Qwen3.5 397B A17B
APEX-Agents21.3%24.9%Qwen3.5 397B A17B
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 index05
Reasoning benchmarks for DeepSeek V3.2 and Qwen3.5 397B A17B
BenchmarkDeepSeek V3.2Qwen3.5 397B A17BLeader
GPQA Diamond80.3%Not measuredOnly DeepSeek V3.2 measured
GPQA Diamond (Artificial Analysis run)84.0%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)24.6%29.0%Qwen3.5 397B A17B
GPQA Diamond (Epoch AI run)Not measured86.4%Only Qwen3.5 397B A17B measured
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
Mystery game puzzlesNot measured18.0%Only Qwen3.5 397B A17B measured
LMArena Text: hard prompts14341448Qwen3.5 397B A17B
LMArena Text: expert14361462Qwen3.5 397B A17B
LMArena Text: hard prompts in English14461450Qwen3.5 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 DeepSeek V3.2 and Qwen3.5 397B A17B
BenchmarkDeepSeek V3.2Qwen3.5 397B A17BLeader
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
OTIS Mock AIMENot measured88.9%Only Qwen3.5 397B A17B measured
FrontierMath, tiers 1 to 3Not measured31.2%Only Qwen3.5 397B A17B measured
LMArena Text: math14351453Qwen3.5 397B A17B
LMArena Text: mathematical work14401454Qwen3.5 397B A17B
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 DeepSeek V3.2 and Qwen3.5 397B A17B
BenchmarkDeepSeek V3.2Qwen3.5 397B A17BLeader
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: science14461457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare14401455Qwen3.5 397B A17B
LMArena Text: legal and government14251441Qwen3.5 397B A17B
LMArena Text: business and finance14101435Qwen3.5 397B A17B
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index07
Web design benchmarks for DeepSeek V3.2 and Qwen3.5 397B A17B
BenchmarkDeepSeek V3.2Qwen3.5 397B A17BLeader
Design Arena: websites11751190Qwen3.5 397B A17B
Design Arena: UI components11461160Qwen3.5 397B A17B
Design Arena: game dev11311140Qwen3.5 397B A17B
Design Arena: data viz11581175Qwen3.5 397B A17B
Design Arena: 3D11321160Qwen3.5 397B A17B
Design Arena: all code categories11631179Qwen3.5 397B A17B
Design Arena: SVG10321139Qwen3.5 397B A17B
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 index021
General benchmarks for DeepSeek V3.2 and Qwen3.5 397B A17B
BenchmarkDeepSeek V3.2Qwen3.5 397B A17BLeader
IFBench60.7%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)73.3%77.3%Qwen3.5 397B A17B
DTBench85.6%87.5%Qwen3.5 397B A17B
LMCA28.8%37.9%Qwen3.5 397B A17B
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251437Qwen3.5 397B A17B
LMArena Text: instruction following14131424Qwen3.5 397B A17B
LMArena Text: creative writing14001402Qwen3.5 397B A17B
LMArena VisionNot measured1262Only Qwen3.5 397B A17B measured
LMArena Text: English14391443Qwen3.5 397B A17B
LMArena Text: non-English14091430Qwen3.5 397B A17B
LMArena Text: Chinese14551498Qwen3.5 397B A17B
LMArena Text: German14131445Qwen3.5 397B A17B
LMArena Text: Japanese13741429Qwen3.5 397B A17B
LMArena Text: Korean13701384Qwen3.5 397B A17B
LMArena Text: French14291462Qwen3.5 397B A17B
LMArena Text: Spanish14241441Qwen3.5 397B A17B
LMArena Text: Russian14241430Qwen3.5 397B A17B
LMArena Text: Polish14191447Qwen3.5 397B A17B
LMArena Text: multi-turn14271446Qwen3.5 397B A17B
LMArena Text: longer queries14281441Qwen3.5 397B A17B
LMArena Text: writing and language14071415Qwen3.5 397B A17B
LMArena Text: entertainment and media13951399Qwen3.5 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 15 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 397B A17B
BenchmarkDeepSeek V3.2Qwen3.5 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

DeepSeek V3.2$0.0007Fits in one request
Qwen3.5 397B A17B$0.0016Fits 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 397B A17B$0.027Fits 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 397B A17B$0.101Fits 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 397B A17B
SpecificationDeepSeek V3.2Qwen3.5 397B A17B
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedDecember 1, 2025February 16, 2026
Context window164K262K
Input $/Mtok$0.29$0.39
Output $/Mtok$0.88$2.34
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 397B A17B for coding?

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Qwen3.5 397B A17B has none, so the two cannot be ranked on coding. Pick Qwen3.5 397B A17B for a larger context window (262K against 164K tokens) and work measured by LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, DeepSeek V3.2 or Qwen3.5 397B A17B?

DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, DeepSeek V3.2 or Qwen3.5 397B A17B?

Qwen3.5 397B A17B, with 262K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 vs Qwen3.5 397B A17B comparison based on?

49 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 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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