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DeepSeek V3.2 Exp vs Qwen3 Coder 480B A35B

DeepSeek V3.2 Exp leads Qwen3 Coder 480B A35B on the Artificial Analysis Intelligence Index, 16.6 to 11.9, and DeepSeek V3.2 Exp costs 3.7x less per output token ($0.41 vs $1.50 per million). Updated October 22, 2026; 48 benchmarks have a result for both.

DeepSeek V3.2 ExpDeepSeek
Coding
No data
Overall
No data
Output $/M
$0.41
44
Qwen3 Coder 480B A35BQwen
Coding
No data
Overall
No data
Output $/M
$1.50
4
vs boards won of 48 shared

DeepSeek V3.2 Exp or Qwen3 Coder 480B A35B: which to pick

Neither model has a coding score yet, so the two cannot be ranked on coding.

Pick DeepSeek V3.2 Exp for output tokens 3.7x cheaper ($0.41 vs $1.50 per million) and work measured by LMArena WebDev or WeirdML, where it leads.

Shared results
48
DeepSeek V3.2 Exp only
11
Qwen3 Coder 480B A35B only
5
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 Exp not benchmarked, Qwen3 Coder 480B A35B not benchmarked.
Widest category
Math, 29.3 points apart on average over 4 shared benchmarks.
Widest single gap
AA Math Index: DeepSeek V3.2 Exp 87.7 against Qwen3 Coder 480B A35B 39.3, 48.4 points apart on a 0 to 100 scale.
Output price
DeepSeek V3.2 Exp $0.41 against Qwen3 Coder 480B A35B $1.50 per million output tokens, 3.7x apart.
Context window
DeepSeek V3.2 Exp takes 164K tokens, Qwen3 Coder 480B A35B 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 V3.2 ExpBarsQwen3 Coder 480B A35BTies
Coding43
Intelligence10
Agentic work01
Reasoning60
Math40
Knowledge50
Web design50
General190
All shared boards444
CodingLike-for-likeSix of these boards feed the Vibe Coding Index43
Coding benchmarks for DeepSeek V3.2 Exp and Qwen3 Coder 480B A35B
BenchmarkDeepSeek V3.2 ExpQwen3 Coder 480B A35BLeader
Aider polyglot74.2%Not measuredOnly DeepSeek V3.2 Exp measured
SWE-bench VerifiedNot measured69.6%Only Qwen3 Coder 480B A35B measured
LMArena WebDev12721275Qwen3 Coder 480B A35B
LiveCodeBench (Artificial Analysis run)78.9%58.5%DeepSeek V3.2 Exp
Terminal-Bench Hard (Artificial Analysis run)31.1%18.9%DeepSeek V3.2 Exp
Terminal-BenchNot measured27.2%Only Qwen3 Coder 480B A35B measured
GSO-BenchNot measured4.9%Only Qwen3 Coder 480B A35B measured
WeirdML39.5%41.2%Qwen3 Coder 480B A35B
ALE-BenchNot measured461.4Only Qwen3 Coder 480B A35B measured
LMArena Text: coding14381412DeepSeek V3.2 Exp
LMArena Text: software and IT14381393DeepSeek V3.2 Exp
LMArena WebDev: ReactNot measured1257Only Qwen3 Coder 480B A35B measured
LMArena WebDev: HTML12811287Qwen3 Coder 480B A35B
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
Intelligence benchmarks for DeepSeek V3.2 Exp and Qwen3 Coder 480B A35B
BenchmarkDeepSeek V3.2 ExpQwen3 Coder 480B A35BLeader
AA Intelligence Index16.611.9DeepSeek V3.2 Exp
Epoch Capabilities Index144.9Not measuredOnly DeepSeek V3.2 Exp measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for DeepSeek V3.2 Exp and Qwen3 Coder 480B A35B
BenchmarkDeepSeek V3.2 ExpQwen3 Coder 480B A35BLeader
τ²-Bench Telecom33.9%43.6%Qwen3 Coder 480B A35B
TheAgentCompany42.9%Not measuredOnly DeepSeek V3.2 Exp measured
Berkeley Function Calling Leaderboard (BFCL)56.7%Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 38 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index60
Reasoning benchmarks for DeepSeek V3.2 Exp and Qwen3 Coder 480B A35B
BenchmarkDeepSeek V3.2 ExpQwen3 Coder 480B A35BLeader
GPQA Diamond (Artificial Analysis run)79.7%61.8%DeepSeek V3.2 Exp
Humanity's Last Exam (Artificial Analysis run)14.9%4.5%DeepSeek V3.2 Exp
LMArena Text: hard prompts14291372DeepSeek V3.2 Exp
LMArena Text: expert14221338DeepSeek V3.2 Exp
LMArena Text: hard prompts in English14441380DeepSeek V3.2 Exp
Kagi LLM Benchmark52.2%49.5%DeepSeek V3.2 Exp
Neither model has a result on the other 16 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index40
Math benchmarks for DeepSeek V3.2 Exp and Qwen3 Coder 480B A35B
BenchmarkDeepSeek V3.2 ExpQwen3 Coder 480B A35BLeader
AA Math Index87.739.3DeepSeek V3.2 Exp
AIME 2025 (Artificial Analysis run)87.7%39.3%DeepSeek V3.2 Exp
LMArena Text: math14221365DeepSeek V3.2 Exp
LMArena Text: mathematical work14331367DeepSeek V3.2 Exp
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index50
Knowledge benchmarks for DeepSeek V3.2 Exp and Qwen3 Coder 480B A35B
BenchmarkDeepSeek V3.2 ExpQwen3 Coder 480B A35BLeader
MMLU Pro (Artificial Analysis run)85.0%78.8%DeepSeek V3.2 Exp
LMArena Text: science14431347DeepSeek V3.2 Exp
LMArena Text: medicine and healthcare14681347DeepSeek V3.2 Exp
LMArena Text: legal and government14191358DeepSeek V3.2 Exp
LMArena Text: business and finance14121343DeepSeek V3.2 Exp
Neither model has a result on the other 13 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index50
Web design benchmarks for DeepSeek V3.2 Exp and Qwen3 Coder 480B A35B
BenchmarkDeepSeek V3.2 ExpQwen3 Coder 480B A35BLeader
Design Arena: websites11781158DeepSeek V3.2 Exp
Design Arena: UI components11641110DeepSeek V3.2 Exp
Design Arena: game dev11441098DeepSeek V3.2 Exp
Design Arena: data viz11521084DeepSeek V3.2 Exp
Design Arena: 3D1148Not measuredOnly DeepSeek V3.2 Exp measured
Design Arena: all code categories11701142DeepSeek V3.2 Exp
Design Arena: SVG1031Not measuredOnly DeepSeek V3.2 Exp measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index190
General benchmarks for DeepSeek V3.2 Exp and Qwen3 Coder 480B A35B
BenchmarkDeepSeek V3.2 ExpQwen3 Coder 480B A35BLeader
IFBench54.1%40.5%DeepSeek V3.2 Exp
Long context reasoning (AA-LCR)72.3%45.7%DeepSeek V3.2 Exp
DTBench87.7%Not measuredOnly DeepSeek V3.2 Exp measured
LMCA29.1%Not measuredOnly DeepSeek V3.2 Exp measured
Fiction.LiveBench83.3%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench13.2%Not measuredOnly DeepSeek V3.2 Exp measured
CL-bench Life9.5%Not measuredOnly DeepSeek V3.2 Exp measured
LMArena Text14251357DeepSeek V3.2 Exp
LMArena Text: instruction following14011355DeepSeek V3.2 Exp
LMArena Text: creative writing14041333DeepSeek V3.2 Exp
LMArena Text: English14411359DeepSeek V3.2 Exp
LMArena Text: non-English14041346DeepSeek V3.2 Exp
LMArena Text: Chinese14611357DeepSeek V3.2 Exp
LMArena Text: German14401325DeepSeek V3.2 Exp
LMArena Text: Japanese13311311DeepSeek V3.2 Exp
LMArena Text: Korean13721305DeepSeek V3.2 Exp
LMArena Text: French14341398DeepSeek V3.2 Exp
LMArena Text: Spanish14411360DeepSeek V3.2 Exp
LMArena Text: Russian14121366DeepSeek V3.2 Exp
LMArena Text: Polish14351344DeepSeek V3.2 Exp
LMArena Text: multi-turn14221366DeepSeek V3.2 Exp
LMArena Text: longer queries14181378DeepSeek V3.2 Exp
LMArena Text: writing and language14071340DeepSeek V3.2 Exp
LMArena Text: entertainment and media14071317DeepSeek V3.2 Exp
Neither model has a result on the other 22 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for DeepSeek V3.2 Exp and Qwen3 Coder 480B A35B
BenchmarkDeepSeek V3.2 ExpQwen3 Coder 480B A35BLeader
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 Exp$0.0005Fits in one request
Qwen3 Coder 480B A35B$0.0010Fits in one request

DeepSeek V3.2 Exp costs less

Repository review

50K input + 3K output tokens

DeepSeek V3.2 Exp$0.015Fits in one request
Qwen3 Coder 480B A35B$0.019Fits in one request

DeepSeek V3.2 Exp costs less

Agent loop

200K input + 10K output tokens

DeepSeek V3.2 Exp$0.058Exceeds the context window
Qwen3 Coder 480B A35B$0.075Fits in one request

DeepSeek V3.2 Exp 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 Exp and Qwen3 Coder 480B A35B
SpecificationDeepSeek V3.2 ExpQwen3 Coder 480B A35B
MakerDeepSeekQwen
WeightsOpen weightsOpen weights
ReleasedSeptember 29, 2025July 23, 2025
Context window164K262K
Input $/Mtok$0.27$0.30
Output $/Mtok$0.41$1.50
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it11
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is DeepSeek V3.2 Exp better than Qwen3 Coder 480B A35B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 3.7x cheaper ($0.41 vs $1.50 per million) and work measured by LMArena WebDev or WeirdML, where it leads.

Which is cheaper, DeepSeek V3.2 Exp or Qwen3 Coder 480B A35B?

DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; Qwen3 Coder 480B A35B costs $0.30 and $1.50. Cost per solved task is known for neither of them.

Which has the bigger context window, DeepSeek V3.2 Exp or Qwen3 Coder 480B A35B?

Qwen3 Coder 480B A35B, with 262K tokens against 164K.

How many benchmarks is the DeepSeek V3.2 Exp vs Qwen3 Coder 480B A35B comparison based on?

48 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and Qwen3 Coder 480B A35B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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