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DeepSeek V3.2 vs Qwen3.5-35B-A3B

Updated October 21, 2026. 42 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace DeepSeek V3.2 Replace Qwen3.5-35B-A3B
DeepSeek V3.2DeepSeek
Coding
54.4
Overall
21.5
Output $/M
$0.42
38
Qwen3.5-35B-A3BQwen
Coding
7.7
Overall
19.3
Output $/M
$1.00
4
vs boards won of 42 shared

Decision reading

Pick DeepSeek V3.2 for coding: it scores 54.4 on the Vibe Coding Index against 7.7 for Qwen3.5-35B-A3B.

Pick Qwen3.5-35B-A3B when the context window is larger, 262K against 164K tokens, or when what matters is GPQA Diamond (Artificial Analysis run) or IFBench, where it leads.

Model ADeepSeek V3.2DeepSeek54.4/1002 independent benchmarks$0.99 per solved taskModel BQwen3.5-35B-A3BQwen7.7/1001 independent benchmarkNo cost per solved task
Shared results
42
DeepSeek V3.2 only
33
Qwen3.5-35B-A3B only
3
Like-for-like categories
7 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 54.4 from 2 benchmarks, Qwen3.5-35B-A3B 7.7 from 1 benchmark.
Widest category
Coding, 11.3 points apart on average over 6 shared benchmarks.
Widest single gap
LMArena WebDev: React: DeepSeek V3.2 1350 against Qwen3.5-35B-A3B 1238, 18.6 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.42 against Qwen3.5-35B-A3B $1.00 per million output tokens, 2.4x apart.
Context window
DeepSeek V3.2 takes 164K tokens, Qwen3.5-35B-A3B 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 V3.2BarsQwen3.5-35B-A3BTies
Coding60
Intelligence20
Agentic work20
Reasoning41
Math20
Knowledge40
General183
All shared boards384
CodingLike-for-likeFour of these boards feed the Vibe Coding Index60
Coding benchmarks for DeepSeek V3.2 and Qwen3.5-35B-A3B
BenchmarkDeepSeek V3.2Qwen3.5-35B-A3BLeader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev13621254DeepSeek V3.2
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
Terminal-Bench Hard (Artificial Analysis run)35.6%26.5%DeepSeek V3.2
Terminal-Bench39.6%Not measuredOnly DeepSeek V3.2 measured
ALE-BenchNot measured221.8Only Qwen3.5-35B-A3B measured
LMArena Text: coding14531410DeepSeek V3.2
LMArena Text: software and IT14491415DeepSeek V3.2
LMArena WebDev: React13501238DeepSeek V3.2
LMArena WebDev: HTML13711301DeepSeek V3.2
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
Intelligence benchmarks for DeepSeek V3.2 and Qwen3.5-35B-A3B
BenchmarkDeepSeek V3.2Qwen3.5-35B-A3BLeader
AA Intelligence Index21.519.3DeepSeek V3.2
Epoch Capabilities Index146.1142.5DeepSeek V3.2
Agentic workLike-for-likePublic boards, shown as published, kept out of the index20
Agentic work benchmarks for DeepSeek V3.2 and Qwen3.5-35B-A3B
BenchmarkDeepSeek V3.2Qwen3.5-35B-A3BLeader
τ²-Bench Telecom90.6%89.2%DeepSeek V3.2
APEX-Agents21.3%Not measuredOnly DeepSeek V3.2 measured
Vending-Bench 2$1,034$463DeepSeek V3.2
Neither model has a result on the other 36 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index41
Reasoning benchmarks for DeepSeek V3.2 and Qwen3.5-35B-A3B
BenchmarkDeepSeek V3.2Qwen3.5-35B-A3BLeader
GPQA Diamond80.3%Not measuredOnly DeepSeek V3.2 measured
GPQA Diamond (Artificial Analysis run)84.0%84.5%Qwen3.5-35B-A3B
Humanity's Last Exam (Artificial Analysis run)24.6%21.0%DeepSeek V3.2
GPQA Diamond (Epoch AI run)Not measured83.5%Only Qwen3.5-35B-A3B measured
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: hard prompts14341400DeepSeek V3.2
LMArena Text: expert14361408DeepSeek V3.2
LMArena Text: hard prompts in English14461411DeepSeek V3.2
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index20
Math benchmarks for DeepSeek V3.2 and Qwen3.5-35B-A3B
BenchmarkDeepSeek V3.2Qwen3.5-35B-A3BLeader
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 measured70.0%Only Qwen3.5-35B-A3B measured
LMArena Text: math14351404DeepSeek V3.2
LMArena Text: mathematical work14401410DeepSeek V3.2
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
Knowledge benchmarks for DeepSeek V3.2 and Qwen3.5-35B-A3B
BenchmarkDeepSeek V3.2Qwen3.5-35B-A3BLeader
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: science14461413DeepSeek V3.2
LMArena Text: medicine and healthcare14401421DeepSeek V3.2
LMArena Text: legal and government14251399DeepSeek V3.2
LMArena Text: business and finance14101394DeepSeek V3.2
Neither model has a result on the other 6 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 Qwen3.5-35B-A3B
BenchmarkDeepSeek V3.2Qwen3.5-35B-A3BLeader
Design Arena: websites1175Not measuredOnly DeepSeek V3.2 measured
Design Arena: UI components1149Not measuredOnly DeepSeek V3.2 measured
Design Arena: game dev1132Not measuredOnly DeepSeek V3.2 measured
Design Arena: data viz1158Not measuredOnly DeepSeek V3.2 measured
Design Arena: 3D1133Not measuredOnly DeepSeek V3.2 measured
Design Arena: all code categories1164Not measuredOnly DeepSeek V3.2 measured
Design Arena: SVG1032Not measuredOnly DeepSeek V3.2 measured
Design Arena: ASCII art1082Not 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 index183
General benchmarks for DeepSeek V3.2 and Qwen3.5-35B-A3B
BenchmarkDeepSeek V3.2Qwen3.5-35B-A3BLeader
IFBench60.7%72.5%Qwen3.5-35B-A3B
Long context reasoning (AA-LCR)73.3%72.0%DeepSeek V3.2
DTBench85.6%80.0%DeepSeek V3.2
LMCA28.8%29.5%Qwen3.5-35B-A3B
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251395DeepSeek V3.2
LMArena Text: instruction following14131379DeepSeek V3.2
LMArena Text: creative writing14001347DeepSeek V3.2
LMArena Text: English14391407DeepSeek V3.2
LMArena Text: non-English14091378DeepSeek V3.2
LMArena Text: Chinese14551457Qwen3.5-35B-A3B
LMArena Text: German14131367DeepSeek V3.2
LMArena Text: Japanese13741326DeepSeek V3.2
LMArena Text: Korean13701355DeepSeek V3.2
LMArena Text: French14291412DeepSeek V3.2
LMArena Text: Spanish14241390DeepSeek V3.2
LMArena Text: Russian14241376DeepSeek V3.2
LMArena Text: Polish14191385DeepSeek V3.2
LMArena Text: multi-turn14271390DeepSeek V3.2
LMArena Text: longer queries14281389DeepSeek V3.2
LMArena Text: writing and language14071365DeepSeek V3.2
LMArena Text: entertainment and media13951347DeepSeek V3.2
Neither model has a result on the other 23 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 Qwen3.5-35B-A3B
BenchmarkDeepSeek V3.2Qwen3.5-35B-A3BLeader
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$0.0005Fits in one request
Qwen3.5-35B-A3B$0.0006Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.015Fits in one request
Qwen3.5-35B-A3B$0.010Fits in one request

Qwen3.5-35B-A3B costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.060Exceeds the context window
Qwen3.5-35B-A3B$0.038Fits in one request

Qwen3.5-35B-A3B 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 Qwen3.5-35B-A3B
SpecificationDeepSeek V3.2Qwen3.5-35B-A3B
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedDecember 1, 2025February 25, 2026
Context window164K262K
Input $/Mtok$0.28$0.14
Output $/Mtok$0.42$1.00
Coding index54.47.7
Independent measurements behind it21
Cost per solved task$0.99Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 better than Qwen3.5-35B-A3B for coding?

Pick DeepSeek V3.2 for coding: it scores 54.4 on the Vibe Coding Index against 7.7 for Qwen3.5-35B-A3B. Pick Qwen3.5-35B-A3B when the context window is larger, 262K against 164K tokens, or when what matters is GPQA Diamond (Artificial Analysis run) or IFBench, where it leads.

Which is cheaper, DeepSeek V3.2 or Qwen3.5-35B-A3B?

DeepSeek V3.2 costs $0.28 per million input tokens and $0.42 per million output tokens; Qwen3.5-35B-A3B costs $0.14 and $1.00. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, DeepSeek V3.2 or Qwen3.5-35B-A3B?

Qwen3.5-35B-A3B, with 262K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 vs Qwen3.5-35B-A3B comparison based on?

42 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2's coding index rests on 2 independent benchmarks and Qwen3.5-35B-A3B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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