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Qwen3 30B A3B vs Qwen3.5-Flash

Qwen3.5-Flash costs 1.9x less per output token than Qwen3 30B A3B ($0.26 vs $0.50 per million). Updated October 22, 2026; 33 benchmarks have a result for both.

Qwen3 30B A3BQwen
Coding
No data
Overall
38.2
Output $/M
$0.50
0
Qwen3.5-FlashQwen
Coding
41.1
Overall
50.1
Output $/M
$0.26
33
vs boards won of 33 shared

Qwen3 30B A3B or Qwen3.5-Flash: which to pick

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; Qwen3 30B A3B has none, so the two cannot be ranked on coding.

Compare them on price, context and the boards below.

Shared results
33
Qwen3 30B A3B only
20
Qwen3.5-Flash only
22
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 30B A3B not benchmarked, Qwen3.5-Flash 41.1 from 2 benchmarks.
Widest category
Reasoning, 16.1 points apart on average over 4 shared benchmarks.
Widest single gap
DTBench: Qwen3 30B A3B 60.3% against Qwen3.5-Flash 82.9%, 22.7 points apart on a 0 to 100 scale.
Output price
Qwen3 30B A3B $0.50 against Qwen3.5-Flash $0.26 per million output tokens, 1.9x apart.
Context window
Qwen3 30B A3B takes 41K 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 30B A3BBarsQwen3.5-FlashTies
Coding02
Intelligence01
Reasoning04
Math03
Knowledge04
General019
All shared boards033
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
Coding benchmarks for Qwen3 30B A3B and Qwen3.5-Flash
BenchmarkQwen3 30B A3BQwen3.5-FlashLeader
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)50.6%Not measuredOnly Qwen3 30B A3B measured
Terminal-Bench Hard (Artificial Analysis run)6.8%Not measuredOnly Qwen3 30B A3B measured
WeirdML29.8%Not measuredOnly Qwen3 30B A3B measured
ALE-BenchNot measured265.9Only Qwen3.5-Flash measured
LMArena Text: coding13371412Qwen3.5-Flash
LMArena Text: software and IT13401416Qwen3.5-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 32 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for Qwen3 30B A3B and Qwen3.5-Flash
BenchmarkQwen3 30B A3BQwen3.5-FlashLeader
AA Intelligence Index7.6Not measuredOnly Qwen3 30B A3B measured
Epoch Capabilities Index137.3143.5Qwen3.5-Flash
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Agentic work benchmarks for Qwen3 30B A3B and Qwen3.5-Flash
BenchmarkQwen3 30B A3BQwen3.5-FlashLeader
τ²-Bench Telecom26.0%Not measuredOnly Qwen3 30B A3B measured
Neither model has a result on the other 41 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index04
Reasoning benchmarks for Qwen3 30B A3B and Qwen3.5-Flash
BenchmarkQwen3 30B A3BQwen3.5-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)61.6%Not measuredOnly Qwen3 30B A3B measured
Humanity's Last Exam (Artificial Analysis run)6.2%Not measuredOnly Qwen3 30B A3B measured
GPQA Diamond (Epoch AI run)61.7%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 prompts13141403Qwen3.5-Flash
LMArena Text: expert13141407Qwen3.5-Flash
LMArena Text: hard prompts in English13291409Qwen3.5-Flash
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index03
Math benchmarks for Qwen3 30B A3B and Qwen3.5-Flash
BenchmarkQwen3 30B A3BQwen3.5-FlashLeader
AIMENot measured92.5%Only Qwen3.5-Flash measured
AA Math Index72.3Not measuredOnly Qwen3 30B A3B measured
AIME 2025 (Artificial Analysis run)72.3%Not measuredOnly Qwen3 30B A3B measured
OTIS Mock AIME62.8%84.4%Qwen3.5-Flash
FrontierMath, tiers 1 to 3Not measured18.2%Only Qwen3.5-Flash measured
LMArena Text: math13551407Qwen3.5-Flash
LMArena Text: mathematical work13631413Qwen3.5-Flash
MathArena9.4%Not measuredOnly Qwen3 30B A3B measured
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Qwen3 30B A3B and Qwen3.5-Flash
BenchmarkQwen3 30B A3BQwen3.5-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)77.7%Not measuredOnly Qwen3 30B A3B measured
SimpleQA VerifiedNot measured20.3%Only Qwen3.5-Flash measured
LMArena Text: science13211415Qwen3.5-Flash
LMArena Text: medicine and healthcare13181407Qwen3.5-Flash
LMArena Text: legal and government13141398Qwen3.5-Flash
LMArena Text: business and finance13091394Qwen3.5-Flash
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
Web design benchmarks for Qwen3 30B A3B and Qwen3.5-Flash
BenchmarkQwen3 30B A3BQwen3.5-FlashLeader
Design Arena: websites954Not measuredOnly Qwen3 30B A3B measured
Design Arena: UI components943Not measuredOnly Qwen3 30B A3B measured
Design Arena: game dev898Not measuredOnly Qwen3 30B A3B measured
Design Arena: data viz967Not measuredOnly Qwen3 30B A3B measured
Design Arena: all code categories941Not measuredOnly Qwen3 30B A3B measured
Neither model has a result on the other 4 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index019
General benchmarks for Qwen3 30B A3B and Qwen3.5-Flash
BenchmarkQwen3 30B A3BQwen3.5-FlashLeader
SAGE (Vals AI)Not measured42.5%Only Qwen3.5-Flash measured
MedScribe (Vals AI)Not measured70.6%Only Qwen3.5-Flash measured
IFBench41.5%Not measuredOnly Qwen3 30B A3B measured
Long context reasoning (AA-LCR)0.0%Not measuredOnly Qwen3 30B A3B measured
DTBench60.3%82.9%Qwen3.5-Flash
LMCA15.8%29.1%Qwen3.5-Flash
Fiction.LiveBench40.6%Not measuredOnly Qwen3 30B A3B measured
Creative writing (Lech Mazur)75.3%Not measuredOnly Qwen3 30B A3B measured
LMArena Text13171397Qwen3.5-Flash
LMArena Text: instruction following12831374Qwen3.5-Flash
LMArena Text: creative writing12711343Qwen3.5-Flash
LMArena Text: English13351404Qwen3.5-Flash
LMArena Text: non-English12951385Qwen3.5-Flash
LMArena Text: Chinese13471445Qwen3.5-Flash
LMArena Text: German13071390Qwen3.5-Flash
LMArena Text: Japanese12541369Qwen3.5-Flash
LMArena Text: Korean12611344Qwen3.5-Flash
LMArena Text: French13521413Qwen3.5-Flash
LMArena Text: Spanish13171400Qwen3.5-Flash
LMArena Text: Russian12911380Qwen3.5-Flash
LMArena Text: Polish12991376Qwen3.5-Flash
LMArena Text: multi-turn13071393Qwen3.5-Flash
LMArena Text: longer queries13121392Qwen3.5-Flash
LMArena Text: writing and language12901369Qwen3.5-Flash
LMArena Text: entertainment and media12581347Qwen3.5-Flash
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 Qwen3 30B A3B and Qwen3.5-Flash
BenchmarkQwen3 30B A3BQwen3.5-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 30B A3B$0.0004Fits in one request
Qwen3.5-Flash$0.0002Fits in one request

Qwen3.5-Flash costs less

Repository review

50K input + 3K output tokens

Qwen3 30B A3B$0.0075Exceeds the context window
Qwen3.5-Flash$0.0040Fits in one request

Qwen3.5-Flash costs less

Agent loop

200K input + 10K output tokens

Qwen3 30B A3B$0.029Exceeds 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 30B A3B and Qwen3.5-Flash
SpecificationQwen3 30B A3BQwen3.5-Flash
MakerQwenQwen
WeightsOpen weightsNo public weights listed
ReleasedApril 28, 2025February 25, 2026
Context window41K1M
Input $/Mtok$0.12$0.065
Output $/Mtok$0.50$0.26
Coding indexNot benchmarked41.1
Independent measurements behind it02
Cost per solved taskNot computable$0.40
SourceOpenRouter listingOpenRouter listing

Questions

Is Qwen3 30B A3B better than Qwen3.5-Flash for coding?

Only Qwen3.5-Flash has a coding score, 41.1 on the Vibe Coding Index; Qwen3 30B A3B has none, so the two cannot be ranked on coding. Compare them on price, context and the boards below.

Which is cheaper, Qwen3 30B A3B or Qwen3.5-Flash?

Qwen3 30B A3B costs $0.12 per million input tokens and $0.50 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 30B A3B or Qwen3.5-Flash?

Qwen3.5-Flash, with 1M tokens against 41K.

How many benchmarks is the Qwen3 30B A3B vs Qwen3.5-Flash comparison based on?

33 benchmarks have a result for both models as of October 22, 2026. Qwen3 30B A3B'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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