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Qwen3 32B vs Qwen3.5-Flash

Qwen3.5-Flash costs 1.5x less per output token than Qwen3 32B ($0.26 vs $0.40 per million). Updated October 22, 2026; 28 benchmarks have a result for both.

Replace Qwen3 32B Replace Qwen3.5-Flash
Qwen3 32BQwen
Coding
No data
Overall
40.8
Output $/M
$0.40
1
Qwen3.5-FlashQwen
Coding
41.1
Overall
50.1
Output $/M
$0.26
27
vs boards won of 28 shared

Qwen3 32B or Qwen3.5-Flash: which to pick

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; Qwen3 32B has none, so the two cannot be ranked on coding.

Pick Qwen3 32B for work measured by LMArena Text: mathematical work, where it leads.

Model AQwen3 32BQwenNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5-FlashQwen41.1/1002 independent benchmarks$0.40 per solved task
Shared results
28
Qwen3 32B only
17
Qwen3.5-Flash only
27
Like-for-like categories
6 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
Qwen3 32B not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
Widest category
Reasoning, 11.4 points apart on average over 4 shared benchmarks.
Widest single gap
OTIS Mock AIME: Qwen3 32B 66.9% against Qwen3.5-Flash 84.4%, 17.5 points apart on a 0 to 100 scale.
Output price
Qwen3 32B $0.40 against Qwen3.5-Flash $0.26 per million output tokens, 1.5x apart.
Context window
Qwen3 32B takes 131K tokens, Qwen3.5-Flash 1M.

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
CategoryQwen3 32BBarsQwen3.5-FlashTies
Coding02
Intelligence01
Reasoning04
Math12
Knowledge04
General014
All shared boards127
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Qwen3 32B and Qwen3.5-Flash
BenchmarkQwen3 32BQwen3.5-FlashLeader
Aider polyglot40.0%Not measuredOnly Qwen3 32B measured
LMArena WebDevNot measured1244Only Qwen3.5-Flash measured
LiveCodeBench (Vals AI)Not measured83.3%Only Qwen3.5-Flash measured
SWE-bench Verified (Vals AI)Not measured64.4%Only Qwen3.5-Flash measured
LiveCodeBench (Artificial Analysis run)54.6%Not measuredOnly Qwen3 32B measured
SciCode36.0%Not measuredOnly Qwen3 32B measured
Terminal-Bench Hard (Artificial Analysis run)3.0%Not measuredOnly Qwen3 32B measured
ALE-BenchNot measured265.9Only Qwen3.5-Flash measured
LMArena Text: coding13571412Qwen3.5-Flash
LMArena Text: software and IT13661416Qwen3.5-Flash
LMArena WebDev: ReactNot measured1229Only Qwen3.5-Flash measured
LMArena WebDev: HTMLNot measured1291Only Qwen3.5-Flash measured
Neither model has a result on the other 31 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Qwen3 32B and Qwen3.5-Flash
BenchmarkQwen3 32BQwen3.5-FlashLeader
AA Intelligence Index7.3Not measuredOnly Qwen3 32B measured
Epoch Capabilities Index138.4143.5Qwen3.5-Flash
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
Agentic work benchmarks for Qwen3 32B and Qwen3.5-Flash
BenchmarkQwen3 32BQwen3.5-FlashLeader
τ²-Bench Telecom29.8%Not measuredOnly Qwen3 32B measured
Berkeley Function Calling Leaderboard (BFCL)48.7%Not measuredOnly Qwen3 32B measured
BrowseComp-Plus10.7%Not measuredOnly Qwen3 32B measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for Qwen3 32B and Qwen3.5-Flash
BenchmarkQwen3 32BQwen3.5-FlashLeader
GPQA DiamondNot measured82.8%Only Qwen3.5-Flash measured
MMMU Pro (Vals AI)Not measured81.9%Only Qwen3.5-Flash measured
GPQA Diamond (Artificial Analysis run)66.8%Not measuredOnly Qwen3 32B measured
Humanity's Last Exam (Artificial Analysis run)7.4%Not measuredOnly Qwen3 32B measured
GPQA Diamond (Epoch AI run)65.7%82.3%Qwen3.5-Flash
Chess puzzlesNot measured14.0%Only Qwen3.5-Flash measured
Mystery game puzzlesNot measured20.0%Only Qwen3.5-Flash measured
LMArena Text: hard prompts13341403Qwen3.5-Flash
LMArena Text: expert13621407Qwen3.5-Flash
LMArena Text: hard prompts in English13501409Qwen3.5-Flash
Kagi LLM Benchmark48.7%Not measuredOnly Qwen3 32B measured
Neither model has a result on the other 12 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index12
Math benchmarks for Qwen3 32B and Qwen3.5-Flash
BenchmarkQwen3 32BQwen3.5-FlashLeader
AIMENot measured92.5%Only Qwen3.5-Flash measured
AA Math Index73.0Not measuredOnly Qwen3 32B measured
AIME 2025 (Artificial Analysis run)73.0%Not measuredOnly Qwen3 32B measured
OTIS Mock AIME66.9%84.4%Qwen3.5-Flash
FrontierMath, tiers 1 to 3Not measured18.2%Only Qwen3.5-Flash measured
LMArena Text: math13991407Qwen3.5-Flash
LMArena Text: mathematical work14201413Qwen3 32B
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Qwen3 32B and Qwen3.5-Flash
BenchmarkQwen3 32BQwen3.5-FlashLeader
MMLU ProNot measured84.1%Only Qwen3.5-Flash measured
LegalBench (Vals AI)Not measured84.3%Only Qwen3.5-Flash measured
CaseLaw (Vals AI)Not measured56.0%Only Qwen3.5-Flash measured
CorpFin (Vals AI)Not measured63.6%Only Qwen3.5-Flash measured
TaxEval (Vals AI)Not measured72.2%Only Qwen3.5-Flash measured
MortgageTax (Vals AI)Not measured67.4%Only Qwen3.5-Flash measured
MedCode (Vals AI)Not measured33.0%Only Qwen3.5-Flash measured
MMLU Pro (Artificial Analysis run)79.8%Not measuredOnly Qwen3 32B measured
SimpleQA VerifiedNot measured20.3%Only Qwen3.5-Flash measured
LMArena Text: science13721415Qwen3.5-Flash
LMArena Text: medicine and healthcare13681407Qwen3.5-Flash
LMArena Text: legal and government13241398Qwen3.5-Flash
LMArena Text: business and finance13181394Qwen3.5-Flash
Neither model has a result on the other 7 boards here
Web designNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Web design benchmarks for Qwen3 32B and Qwen3.5-Flash
BenchmarkQwen3 32BQwen3.5-FlashLeader
Neither model has a result on 9 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index014
General benchmarks for Qwen3 32B and Qwen3.5-Flash
BenchmarkQwen3 32BQwen3.5-FlashLeader
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)Not measured70.6%Only Qwen3.5-Flash measured
IFBench36.3%Not measuredOnly Qwen3 32B measured
Long context reasoning (AA-LCR)0.0%Not measuredOnly Qwen3 32B measured
DTBench67.5%82.9%Qwen3.5-Flash
LMCA17.3%29.1%Qwen3.5-Flash
Fiction.LiveBench74.2%Not measuredOnly Qwen3 32B measured
LMArena Text13401397Qwen3.5-Flash
LMArena Text: instruction following13051374Qwen3.5-Flash
LMArena Text: creative writing12971343Qwen3.5-Flash
LMArena Text: English13591404Qwen3.5-Flash
LMArena Text: non-English13171385Qwen3.5-Flash
LMArena Text: Chinese13571445Qwen3.5-Flash
LMArena Text: German13411390Qwen3.5-Flash
LMArena Text: JapaneseNot measured1369Only Qwen3.5-Flash measured
LMArena Text: KoreanNot measured1344Only Qwen3.5-Flash measured
LMArena Text: FrenchNot measured1413Only Qwen3.5-Flash measured
LMArena Text: SpanishNot measured1400Only Qwen3.5-Flash measured
LMArena Text: Russian13111380Qwen3.5-Flash
LMArena Text: PolishNot measured1376Only Qwen3.5-Flash measured
LMArena Text: multi-turn13311393Qwen3.5-Flash
LMArena Text: longer queries13271392Qwen3.5-Flash
LMArena Text: writing and language12981369Qwen3.5-Flash
LMArena Text: entertainment and media12791347Qwen3.5-Flash
Neither model has a result on the other 24 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Qwen3 32B and Qwen3.5-Flash
BenchmarkQwen3 32BQwen3.5-FlashLeader
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

Qwen3 32B$0.0003Fits in one request
Qwen3.5-Flash$0.0002Fits in one request

Qwen3.5-Flash costs less

Repository review

50K input + 3K output tokens

Qwen3 32B$0.0082Fits in one request
Qwen3.5-Flash$0.0040Fits in one request

Qwen3.5-Flash costs less

Agent loop

200K input + 10K output tokens

Qwen3 32B$0.032Exceeds the context window
Qwen3.5-Flash$0.016Fits in one request

Qwen3.5-Flash 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 Qwen3 32B and Qwen3.5-Flash
SpecificationQwen3 32BQwen3.5-Flash
MakerQwenQwen
WeightsOpen weightsNo public weights listed
ReleasedApril 28, 2025February 25, 2026
Context window131K1M
Input $/Mtok$0.14$0.065
Output $/Mtok$0.40$0.26
Coding indexNot benchmarked41.1
Independent measurements behind it02
Cost per solved taskNot computable$0.40
SourceOpenRouter listingOpenRouter listing

Questions

Is Qwen3 32B better than Qwen3.5-Flash for coding?

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; Qwen3 32B has none, so the two cannot be ranked on coding. Pick Qwen3 32B for work measured by LMArena Text: mathematical work, where it leads.

Which is cheaper, Qwen3 32B or Qwen3.5-Flash?

Qwen3 32B costs $0.14 per million input tokens and $0.40 per million output tokens; Qwen3.5-Flash costs $0.065 and $0.26. Cost per solved task is known for Qwen3.5-Flash only.

Which has the bigger context window, Qwen3 32B or Qwen3.5-Flash?

Qwen3.5-Flash, with 1M tokens against 131K.

How many benchmarks is the Qwen3 32B vs Qwen3.5-Flash comparison based on?

28 benchmarks have a result for both models as of October 22, 2026. Qwen3 32B's coding index rests on 0 independent benchmarks and Qwen3.5-Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.

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