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DeepSeek V3.2 vs Qwen3 VL 235B A22B Instruct

DeepSeek V3.2 leads Qwen3 VL 235B A22B Instruct on the Artificial Analysis Intelligence Index, 21.5 to 9.9, and DeepSeek V3.2 costs 2.2x less per output token ($0.88 vs $1.90 per million). Updated October 22, 2026; 39 benchmarks have a result for both.

Replace DeepSeek V3.2 Replace Qwen3 VL 235B A22B Instruct
DeepSeek V3.2DeepSeek
Coding
30.0
Overall
37.9
Output $/M
$0.88
28
Qwen3 VL 235B A22B InstructQwen
Coding
No data
Overall
No data
Output $/M
$1.90
11
vs boards won of 39 shared

DeepSeek V3.2 or Qwen3 VL 235B A22B Instruct: which to pick

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Qwen3 VL 235B A22B Instruct has none, so the two cannot be ranked on coding.

Pick Qwen3 VL 235B A22B Instruct for a larger context window (262K against 164K tokens) and work measured by LMArena Text: expert or LMArena Text: legal and government, where it leads.

Model ADeepSeek V3.2DeepSeek30.0/1003 independent benchmarks$0.99 per solved taskModel BQwen3 VL 235B A22B InstructQwenNot benchmarkedNo coding benchmarkNo cost per solved task
Shared results
39
DeepSeek V3.2 only
36
Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct not benchmarked.
Widest category
Agentic work, 36.5 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: DeepSeek V3.2 90.6% against Qwen3 VL 235B A22B Instruct 54.1%, 36.5 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 $0.88 against Qwen3 VL 235B A22B Instruct $1.90 per million output tokens, 2.2x apart.
Context window
DeepSeek V3.2 takes 164K tokens, Qwen3 VL 235B A22B Instruct 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 VL 235B A22B InstructTies
Coding40
Intelligence10
Agentic work10
Reasoning41
Math40
Knowledge32
General118
All shared boards2811
CodingLike-for-likeSix of these boards feed the Vibe Coding Index40
Coding benchmarks for DeepSeek V3.2 and Qwen3 VL 235B A22B Instruct
BenchmarkDeepSeek V3.2Qwen3 VL 235B A22B InstructLeader
SWE-bench Verified70.0%Not measuredOnly DeepSeek V3.2 measured
SWE-bench Multilingual59.0%Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev1362Not measuredOnly DeepSeek V3.2 measured
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%64.6%DeepSeek V3.2
Terminal-Bench Hard (Artificial Analysis run)35.6%11.4%DeepSeek V3.2
Terminal-Bench39.6%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: coding14531438DeepSeek V3.2
LMArena Text: software and IT14491442DeepSeek V3.2
LMArena WebDev: React1350Not measuredOnly DeepSeek V3.2 measured
LMArena WebDev: HTML1371Not measuredOnly DeepSeek V3.2 measured
Neither model has a result on the other 25 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 and Qwen3 VL 235B A22B Instruct
BenchmarkDeepSeek V3.2Qwen3 VL 235B A22B InstructLeader
AA Intelligence Index21.59.9DeepSeek V3.2
Epoch Capabilities Index146.1Not measuredOnly DeepSeek V3.2 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
Agentic work benchmarks for DeepSeek V3.2 and Qwen3 VL 235B A22B Instruct
BenchmarkDeepSeek V3.2Qwen3 VL 235B A22B InstructLeader
τ²-Bench Telecom90.6%54.1%DeepSeek V3.2
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 index41
Reasoning benchmarks for DeepSeek V3.2 and Qwen3 VL 235B A22B Instruct
BenchmarkDeepSeek V3.2Qwen3 VL 235B A22B InstructLeader
GPQA Diamond80.3%Not measuredOnly DeepSeek V3.2 measured
GPQA Diamond (Artificial Analysis run)84.0%77.2%DeepSeek V3.2
Humanity's Last Exam (Artificial Analysis run)24.6%11.9%DeepSeek V3.2
ARC-AGI57.0%Not measuredOnly DeepSeek V3.2 measured
ARC-AGI-24.0%Not measuredOnly DeepSeek V3.2 measured
LMArena Text: hard prompts14341428DeepSeek V3.2
LMArena Text: expert14361442Qwen3 VL 235B A22B Instruct
LMArena Text: hard prompts in English14461439DeepSeek V3.2
LMArena Vision: diagramsNot measured1255Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: homeworkNot measured1277Only Qwen3 VL 235B A22B Instruct measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for DeepSeek V3.2 and Qwen3 VL 235B A22B Instruct
BenchmarkDeepSeek V3.2Qwen3 VL 235B A22B InstructLeader
AIME84.6%Not measuredOnly DeepSeek V3.2 measured
MGSM (Vals AI)86.0%Not measuredOnly DeepSeek V3.2 measured
AA Math Index92.088.3DeepSeek V3.2
AIME 2025 (Artificial Analysis run)92.0%88.3%DeepSeek V3.2
LMArena Text: math14351425DeepSeek V3.2
LMArena Text: mathematical work14401419DeepSeek V3.2
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index32
Knowledge benchmarks for DeepSeek V3.2 and Qwen3 VL 235B A22B Instruct
BenchmarkDeepSeek V3.2Qwen3 VL 235B A22B InstructLeader
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%83.6%DeepSeek V3.2
LMArena Text: science14461444DeepSeek V3.2
LMArena Text: medicine and healthcare14401431DeepSeek V3.2
LMArena Text: legal and government14251438Qwen3 VL 235B A22B Instruct
LMArena Text: business and finance14101433Qwen3 VL 235B A22B Instruct
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 VL 235B A22B Instruct
BenchmarkDeepSeek V3.2Qwen3 VL 235B A22B InstructLeader
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 index118
General benchmarks for DeepSeek V3.2 and Qwen3 VL 235B A22B Instruct
BenchmarkDeepSeek V3.2Qwen3 VL 235B A22B InstructLeader
IFBench60.7%56.5%DeepSeek V3.2
Long context reasoning (AA-LCR)73.3%63.7%DeepSeek V3.2
DTBench85.6%Not measuredOnly DeepSeek V3.2 measured
LMCA28.8%Not measuredOnly DeepSeek V3.2 measured
CL-bench12.4%Not measuredOnly DeepSeek V3.2 measured
CL-bench Life7.4%Not measuredOnly DeepSeek V3.2 measured
LMArena Text14251420DeepSeek V3.2
LMArena Text: instruction following14131406DeepSeek V3.2
LMArena Text: creative writing14001366DeepSeek V3.2
LMArena VisionNot measured1247Only Qwen3 VL 235B A22B Instruct measured
LMArena Text: English14391429DeepSeek V3.2
LMArena Text: non-English14091405DeepSeek V3.2
LMArena Text: Chinese14551463Qwen3 VL 235B A22B Instruct
LMArena Text: German14131424Qwen3 VL 235B A22B Instruct
LMArena Text: Japanese13741385Qwen3 VL 235B A22B Instruct
LMArena Text: Korean13701394Qwen3 VL 235B A22B Instruct
LMArena Text: French14291452Qwen3 VL 235B A22B Instruct
LMArena Text: Spanish14241428Qwen3 VL 235B A22B Instruct
LMArena Text: Russian14241408DeepSeek V3.2
LMArena Text: Polish14191419Qwen3 VL 235B A22B Instruct
LMArena Text: multi-turn14271428Qwen3 VL 235B A22B Instruct
LMArena Text: longer queries14281419DeepSeek V3.2
LMArena Text: writing and language14071392DeepSeek V3.2
LMArena Text: entertainment and media13951368DeepSeek V3.2
LMArena Vision: EnglishNot measured1256Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: ChineseNot measured1283Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: reading textNot measured1253Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: humorNot measured1209Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: captioningNot measured1281Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: recognizing thingsNot measured1249Only Qwen3 VL 235B A22B Instruct measured
LMArena Vision: creative writingNot measured1220Only Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct
BenchmarkDeepSeek V3.2Qwen3 VL 235B A22B InstructLeader
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 VL 235B A22B Instruct$0.0012Fits in one request

DeepSeek V3.2 costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2$0.017Fits in one request
Qwen3 VL 235B A22B Instruct$0.016Fits in one request

Qwen3 VL 235B A22B Instruct costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2$0.067Exceeds the context window
Qwen3 VL 235B A22B Instruct$0.061Fits in one request

Qwen3 VL 235B A22B Instruct 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 VL 235B A22B Instruct
SpecificationDeepSeek V3.2Qwen3 VL 235B A22B Instruct
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedDecember 1, 2025September 23, 2025
Context window164K262K
Input $/Mtok$0.29$0.21
Output $/Mtok$0.88$1.90
Coding index30.0Not benchmarked
Independent measurements behind it30
Cost per solved task$0.99Not computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 better than Qwen3 VL 235B A22B Instruct for coding?

Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; Qwen3 VL 235B A22B Instruct has none, so the two cannot be ranked on coding. Pick Qwen3 VL 235B A22B Instruct for a larger context window (262K against 164K tokens) and work measured by LMArena Text: expert or LMArena Text: legal and government, where it leads.

Which is cheaper, DeepSeek V3.2 or Qwen3 VL 235B A22B Instruct?

DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; Qwen3 VL 235B A22B Instruct costs $0.21 and $1.90. Cost per solved task is known for DeepSeek V3.2 only.

Which has the bigger context window, DeepSeek V3.2 or Qwen3 VL 235B A22B Instruct?

Qwen3 VL 235B A22B Instruct, with 262K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 vs Qwen3 VL 235B A22B Instruct comparison based on?

39 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 VL 235B A22B Instruct's on 0. Where only one model has a result, the page says so instead of naming a winner.

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