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Qwen3.5-35B-A3B vs Qwen3.8 27B

Qwen3.8 27B leads Qwen3.5-35B-A3B on the Artificial Analysis Intelligence Index, 33.7 to 19.3, and Qwen3.5-35B-A3B costs 2.0x less per output token ($1.30 vs $2.55 per million). Updated October 22, 2026; 38 benchmarks have a result for both.

Replace Qwen3.5-35B-A3B Replace Qwen3.8 27B
Qwen3.5-35B-A3BQwen
Coding
No data
Overall
53.5
Output $/M
$1.30
0
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
38
vs boards won of 38 shared

Qwen3.5-35B-A3B or Qwen3.8 27B: which to pick

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Qwen3.5-35B-A3B has none, so the two cannot be ranked on coding.

Pick Qwen3.5-35B-A3B for output tokens 2.0x cheaper ($1.30 vs $2.55 per million).

Model AQwen3.5-35B-A3BQwenNot benchmarked1 independent benchmarkNo cost per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
38
Qwen3.5-35B-A3B only
8
Qwen3.8 27B only
47
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
Qwen3.5-35B-A3B not benchmarked, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Coding, 35.3 points apart on average over 5 shared benchmarks.
Widest single gap
LMArena WebDev: React: Qwen3.5-35B-A3B 1238 against Qwen3.8 27B 1602, 60.3 points apart on a 0 to 100 scale.
Output price
Qwen3.5-35B-A3B $1.30 against Qwen3.8 27B $2.55 per million output tokens, 2.0x apart.
Context window
Qwen3.5-35B-A3B takes 262K tokens, Qwen3.8 27B 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
CategoryQwen3.5-35B-A3BBarsQwen3.8 27BTies
Coding05
Intelligence02
Reasoning05
Math02
Knowledge04
General020
All shared boards038
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
Coding benchmarks for Qwen3.5-35B-A3B and Qwen3.8 27B
BenchmarkQwen3.5-35B-A3BQwen3.8 27BLeader
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDev12541593Qwen3.8 27B
AA Coding IndexNot measured68.1Only Qwen3.8 27B measured
LiveCodeBench (Vals AI)Not measured84.0%Only Qwen3.8 27B measured
SWE-bench Verified (Vals AI)Not measured86.0%Only Qwen3.8 27B measured
Vibe Code Bench (Vals AI)Not measured64.8%Only Qwen3.8 27B measured
Code migration (Vals AI)Not measured14.2%Only Qwen3.8 27B measured
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
IOI (Vals AI)Not measured39.1%Only Qwen3.8 27B measured
Terminal-Bench 2.1 (Vals AI)Not measured58.4%Only Qwen3.8 27B measured
SkillsBench (Vals AI)Not measured38.1%Only Qwen3.8 27B measured
CyberBench (Vals AI)Not measured67.8%Only Qwen3.8 27B measured
SciCodeNot measured46.6%Only Qwen3.8 27B measured
Terminal-Bench Hard (Artificial Analysis run)26.5%Not measuredOnly Qwen3.5-35B-A3B measured
ALE-Bench221.8Not measuredOnly Qwen3.5-35B-A3B measured
LMArena Text: coding14101483Qwen3.8 27B
LMArena Text: software and IT14151478Qwen3.8 27B
LMArena WebDev: React12381602Qwen3.8 27B
LMArena WebDev: HTML13011570Qwen3.8 27B
LMArena WebDev: from an imageNot measured1577Only Qwen3.8 27B measured
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for Qwen3.5-35B-A3B and Qwen3.8 27B
BenchmarkQwen3.5-35B-A3BQwen3.8 27BLeader
AA Intelligence Index19.333.7Qwen3.8 27B
Epoch Capabilities Index142.5151.7Qwen3.8 27B
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
Agentic work benchmarks for Qwen3.5-35B-A3B and Qwen3.8 27B
BenchmarkQwen3.5-35B-A3BQwen3.8 27BLeader
AA Agentic IndexNot measured45.8Only Qwen3.8 27B measured
LiveBench researchNot measured59.4%Only Qwen3.8 27B measured
LiveBench browserNot measured32.0%Only Qwen3.8 27B measured
LiveBench long agenticNot measured27.9%Only Qwen3.8 27B measured
Finance Agent (Vals AI)Not measured48.5%Only Qwen3.8 27B measured
Tax Agent Bench (Vals AI)Not measured30.9%Only Qwen3.8 27B measured
Legal Research Bench (Vals AI)Not measured36.1%Only Qwen3.8 27B measured
Excel Modeling Benchmark (Vals AI)Not measured59.7%Only Qwen3.8 27B measured
Terminal-Bench-Science (Vals AI)Not measured0.0%Only Qwen3.8 27B measured
τ²-Bench Telecom89.2%Not measuredOnly Qwen3.5-35B-A3B measured
APEX-AgentsNot measured47.5%Only Qwen3.8 27B measured
Vending-Bench 2$463Not measuredOnly Qwen3.5-35B-A3B measured
Neither model has a result on the other 30 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for Qwen3.5-35B-A3B and Qwen3.8 27B
BenchmarkQwen3.5-35B-A3BQwen3.8 27BLeader
GPQA DiamondNot measured88.9%Only Qwen3.8 27B measured
LiveBench reasoningNot measured80.0%Only Qwen3.8 27B measured
MMMU Pro (Vals AI)Not measured83.9%Only Qwen3.8 27B measured
GPQA Diamond (Artificial Analysis run)84.5%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)21.0%33.9%Qwen3.8 27B
GPQA Diamond (Epoch AI run)83.5%Not measuredOnly Qwen3.5-35B-A3B measured
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
LMArena Text: hard prompts14001459Qwen3.8 27B
LMArena Text: expert14081479Qwen3.8 27B
LMArena Text: hard prompts in English14111461Qwen3.8 27B
LMArena Vision: diagramsNot measured1285Only Qwen3.8 27B measured
LMArena Vision: homeworkNot measured1295Only Qwen3.8 27B measured
Neither model has a result on the other 10 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Qwen3.5-35B-A3B and Qwen3.8 27B
BenchmarkQwen3.5-35B-A3BQwen3.8 27BLeader
OTIS Mock AIME70.0%Not measuredOnly Qwen3.5-35B-A3B measured
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
LMArena Text: math14041455Qwen3.8 27B
LMArena Text: mathematical work14101458Qwen3.8 27B
MathArena14.7%Not measuredOnly Qwen3.5-35B-A3B measured
Neither model has a result on the other 9 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for Qwen3.5-35B-A3B and Qwen3.8 27B
BenchmarkQwen3.5-35B-A3BQwen3.8 27BLeader
MMLU ProNot measured84.3%Only Qwen3.8 27B measured
LegalBench (Vals AI)Not measured82.4%Only Qwen3.8 27B measured
TaxEval (Vals AI)Not measured70.8%Only Qwen3.8 27B measured
MortgageTax (Vals AI)Not measured64.9%Only Qwen3.8 27B measured
MedCode (Vals AI)Not measured28.7%Only Qwen3.8 27B measured
LMArena Text: science14131467Qwen3.8 27B
LMArena Text: medicine and healthcare14211437Qwen3.8 27B
LMArena Text: legal and government13991447Qwen3.8 27B
LMArena Text: business and finance13941438Qwen3.8 27B
Neither model has a result on the other 11 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
Web design benchmarks for Qwen3.5-35B-A3B and Qwen3.8 27B
BenchmarkQwen3.5-35B-A3BQwen3.8 27BLeader
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
Neither model has a result on the other 8 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index020
General benchmarks for Qwen3.5-35B-A3B and Qwen3.8 27B
BenchmarkQwen3.5-35B-A3BQwen3.8 27BLeader
LiveBench data analysisNot measured76.6%Only Qwen3.8 27B measured
SAGE (Vals AI)Not measured52.4%Only Qwen3.8 27B measured
MedScribe (Vals AI)Not measured83.8%Only Qwen3.8 27B measured
IFBench72.5%Not measuredOnly Qwen3.5-35B-A3B measured
Long context reasoning (AA-LCR)72.0%82.0%Qwen3.8 27B
DTBench80.0%88.0%Qwen3.8 27B
LMCA29.5%41.4%Qwen3.8 27B
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text13951440Qwen3.8 27B
LMArena Text: instruction following13791436Qwen3.8 27B
LMArena Text: creative writing13471383Qwen3.8 27B
LMArena VisionNot measured1269Only Qwen3.8 27B measured
LMArena Text: English14071448Qwen3.8 27B
LMArena Text: non-English13781428Qwen3.8 27B
LMArena Text: Chinese14571501Qwen3.8 27B
LMArena Text: German13671443Qwen3.8 27B
LMArena Text: Japanese13261387Qwen3.8 27B
LMArena Text: Korean13551384Qwen3.8 27B
LMArena Text: French14121465Qwen3.8 27B
LMArena Text: Spanish13901449Qwen3.8 27B
LMArena Text: Russian13761415Qwen3.8 27B
LMArena Text: Polish13851439Qwen3.8 27B
LMArena Text: multi-turn13901440Qwen3.8 27B
LMArena Text: longer queries13891448Qwen3.8 27B
LMArena Text: writing and language13651405Qwen3.8 27B
LMArena Text: entertainment and media13471393Qwen3.8 27B
LMArena Vision: EnglishNot measured1267Only Qwen3.8 27B measured
LMArena Vision: ChineseNot measured1326Only Qwen3.8 27B measured
LMArena Vision: reading textNot measured1281Only Qwen3.8 27B measured
LMArena Vision: humorNot measured1235Only Qwen3.8 27B measured
LMArena Vision: creative writingNot measured1237Only Qwen3.8 27B measured
Neither model has a result on the other 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for Qwen3.5-35B-A3B and Qwen3.8 27B
BenchmarkQwen3.5-35B-A3BQwen3.8 27BLeader
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

Qwen3.5-35B-A3B$0.0008Fits in one request
Qwen3.8 27B$0.0017Fits in one request

Qwen3.5-35B-A3B costs less

Repository review

50K input + 3K output tokens

Qwen3.5-35B-A3B$0.012Fits in one request
Qwen3.8 27B$0.029Fits in one request

Qwen3.5-35B-A3B costs less

Agent loop

200K input + 10K output tokens

Qwen3.5-35B-A3B$0.045Fits in one request
Qwen3.8 27B$0.111Fits 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 Qwen3.5-35B-A3B and Qwen3.8 27B
SpecificationQwen3.5-35B-A3BQwen3.8 27B
MakerQwenQwen
WeightsOpen weightsOpen weights
ReleasedFebruary 25, 2026August 14, 2026
Context window262K1M
Input $/Mtok$0.1625$0.425
Output $/Mtok$1.30$2.55
Coding indexNot benchmarked74.2
Independent measurements behind it14
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is Qwen3.5-35B-A3B better than Qwen3.8 27B for coding?

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Qwen3.5-35B-A3B has none, so the two cannot be ranked on coding. Pick Qwen3.5-35B-A3B for output tokens 2.0x cheaper ($1.30 vs $2.55 per million).

Which is cheaper, Qwen3.5-35B-A3B or Qwen3.8 27B?

Qwen3.5-35B-A3B costs $0.1625 per million input tokens and $1.30 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Cost per solved task is known for Qwen3.8 27B only.

Which has the bigger context window, Qwen3.5-35B-A3B or Qwen3.8 27B?

Qwen3.8 27B, with 1M tokens against 262K.

How many benchmarks is the Qwen3.5-35B-A3B vs Qwen3.8 27B comparison based on?

38 benchmarks have a result for both models as of October 22, 2026. Qwen3.5-35B-A3B's coding index rests on 1 independent benchmark and Qwen3.8 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.

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