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Qwen3.5 397B A17B vs Qwen3.8 27B

Qwen3.8 27B leads Qwen3.5 397B A17B on the Artificial Analysis Intelligence Index, 33.7 to 21.4, at about the same output price ($2.55 vs $2.34 per million). Updated October 22, 2026; 48 benchmarks have a result for both.

Replace Qwen3.5 397B A17B Replace Qwen3.8 27B
Qwen3.5 397B A17BQwen
Coding
No data
Overall
57.1
Output $/M
$2.34
14
Qwen3.8 27BQwen
Coding
74.2
Overall
59.6
Output $/M
$2.55
34
vs boards won of 48 shared

Qwen3.5 397B A17B or Qwen3.8 27B: which to pick

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Qwen3.5 397B A17B has none, so the two cannot be ranked on coding.

Pick Qwen3.5 397B A17B for output tokens 1.1x cheaper ($2.34 vs $2.55 per million) and work measured by LMArena Text: medicine and healthcare or LMArena Text: creative writing, where it leads.

Model AQwen3.5 397B A17BQwenNot benchmarked1 independent benchmarkNo cost per solved taskModel BQwen3.8 27BQwen74.2/1004 independent benchmarks$1.26 per solved task
Shared results
48
Qwen3.5 397B A17B only
18
Qwen3.8 27B only
37
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
Qwen3.5 397B A17B not benchmarked, Qwen3.8 27B 74.2 from 4 benchmarks.
Widest category
Agentic work, 22.6 points apart on average over 1 shared benchmark.
Widest single gap
LMArena WebDev: React: Qwen3.5 397B A17B 1388 against Qwen3.8 27B 1602, 35.4 points apart on a 0 to 100 scale.
Output price
Qwen3.5 397B A17B $2.34 against Qwen3.8 27B $2.55 per million output tokens, 1.1x apart.
Context window
Qwen3.5 397B A17B 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 397B A17BBarsQwen3.8 27BTies
Coding06
Intelligence02
Agentic work01
Reasoning07
Math02
Knowledge13
General1313
All shared boards1434
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
Coding benchmarks for Qwen3.5 397B A17B and Qwen3.8 27B
BenchmarkQwen3.5 397B A17BQwen3.8 27BLeader
LiveBench codingNot measured86.8%Only Qwen3.8 27B measured
LMArena WebDev14001593Qwen3.8 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
SciCode44.8%46.6%Qwen3.8 27B
Terminal-Bench Hard (Artificial Analysis run)40.9%Not measuredOnly Qwen3.5 397B A17B measured
ALE-Bench621.9Not measuredOnly Qwen3.5 397B A17B measured
LMArena Text: coding14641483Qwen3.8 27B
LMArena Text: software and IT14631478Qwen3.8 27B
LMArena WebDev: React13881602Qwen3.8 27B
LMArena WebDev: HTML14101570Qwen3.8 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 397B A17B and Qwen3.8 27B
BenchmarkQwen3.5 397B A17BQwen3.8 27BLeader
AA Intelligence Index21.433.7Qwen3.8 27B
Epoch Capabilities Index146.8151.7Qwen3.8 27B
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for Qwen3.5 397B A17B and Qwen3.8 27B
BenchmarkQwen3.5 397B A17BQwen3.8 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 Telecom95.6%Not measuredOnly Qwen3.5 397B A17B measured
APEX-Agents24.9%47.5%Qwen3.8 27B
Neither model has a result on the other 31 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
Reasoning benchmarks for Qwen3.5 397B A17B and Qwen3.8 27B
BenchmarkQwen3.5 397B A17BQwen3.8 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)89.3%90.5%Qwen3.8 27B
Humanity's Last Exam (Artificial Analysis run)29.0%33.9%Qwen3.8 27B
GPQA Diamond (Epoch AI run)86.4%Not measuredOnly Qwen3.5 397B A17B measured
ARC-AGINot measured87.5%Only Qwen3.8 27B measured
ARC-AGI-2Not measured42.4%Only Qwen3.8 27B measured
Mystery game puzzles18.0%Not measuredOnly Qwen3.5 397B A17B measured
LMArena Text: hard prompts14481459Qwen3.8 27B
LMArena Text: expert14621479Qwen3.8 27B
LMArena Text: hard prompts in English14501461Qwen3.8 27B
LMArena Vision: diagrams12771285Qwen3.8 27B
LMArena Vision: homework12921295Qwen3.8 27B
Neither model has a result on the other 9 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for Qwen3.5 397B A17B and Qwen3.8 27B
BenchmarkQwen3.5 397B A17BQwen3.8 27BLeader
OTIS Mock AIME88.9%Not measuredOnly Qwen3.5 397B A17B measured
FrontierMath, tiers 1 to 331.2%Not measuredOnly Qwen3.5 397B A17B measured
ProofBenchNot measured16.0%Only Qwen3.8 27B measured
LMArena Text: math14531455Qwen3.8 27B
LMArena Text: mathematical work14541458Qwen3.8 27B
MathArena19.2%Not measuredOnly Qwen3.5 397B A17B measured
Neither model has a result on the other 8 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
Knowledge benchmarks for Qwen3.5 397B A17B and Qwen3.8 27B
BenchmarkQwen3.5 397B A17BQwen3.8 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: science14571467Qwen3.8 27B
LMArena Text: medicine and healthcare14551437Qwen3.5 397B A17B
LMArena Text: legal and government14411447Qwen3.8 27B
LMArena Text: business and finance14351438Qwen3.8 27B
Neither model has a result on the other 11 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for Qwen3.5 397B A17B and Qwen3.8 27B
BenchmarkQwen3.5 397B A17BQwen3.8 27BLeader
Design Arena: websites1190Not measuredOnly Qwen3.5 397B A17B measured
Design Arena: UI components1161Not measuredOnly Qwen3.5 397B A17B measured
Design Arena: game dev1140Not measuredOnly Qwen3.5 397B A17B measured
Design Arena: data viz1175Not measuredOnly Qwen3.5 397B A17B measured
Design Arena: 3D1160Not measuredOnly Qwen3.5 397B A17B measured
Design Arena: all code categories1179Not measuredOnly Qwen3.5 397B A17B measured
Design Arena: SVG1139Not measuredOnly Qwen3.5 397B A17B measured
LiveBench designNot measured75.9%Only Qwen3.8 27B measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index1313
General benchmarks for Qwen3.5 397B A17B and Qwen3.8 27B
BenchmarkQwen3.5 397B A17BQwen3.8 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
IFBench78.8%Not measuredOnly Qwen3.5 397B A17B measured
Long context reasoning (AA-LCR)77.3%82.0%Qwen3.8 27B
DTBench87.5%88.0%Qwen3.8 27B
LMCA37.9%41.4%Qwen3.8 27B
Surface Evolver BenchNot measured45.0%Only Qwen3.8 27B measured
LMArena Text14371440Qwen3.8 27B
LMArena Text: instruction following14241436Qwen3.8 27B
LMArena Text: creative writing14021383Qwen3.5 397B A17B
LMArena Vision12621269Qwen3.8 27B
LMArena Text: English14431448Qwen3.8 27B
LMArena Text: non-English14301428Qwen3.5 397B A17B
LMArena Text: Chinese14981501Qwen3.8 27B
LMArena Text: German14451443Qwen3.5 397B A17B
LMArena Text: Japanese14291387Qwen3.5 397B A17B
LMArena Text: Korean13841384Qwen3.5 397B A17B
LMArena Text: French14621465Qwen3.8 27B
LMArena Text: Spanish14411449Qwen3.8 27B
LMArena Text: Russian14301415Qwen3.5 397B A17B
LMArena Text: Polish14471439Qwen3.5 397B A17B
LMArena Text: multi-turn14461440Qwen3.5 397B A17B
LMArena Text: longer queries14411448Qwen3.8 27B
LMArena Text: writing and language14151405Qwen3.5 397B A17B
LMArena Text: entertainment and media13991393Qwen3.5 397B A17B
LMArena Vision: English12681267Qwen3.5 397B A17B
LMArena Vision: Chinese13141326Qwen3.8 27B
LMArena Vision: reading text12711281Qwen3.8 27B
LMArena Vision: humor12621235Qwen3.5 397B A17B
LMArena Vision: captioning1230Not measuredOnly Qwen3.5 397B A17B measured
LMArena Vision: recognizing things1265Not measuredOnly Qwen3.5 397B A17B measured
LMArena Vision: creative writing12601237Qwen3.5 397B A17B
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 Qwen3.5 397B A17B and Qwen3.8 27B
BenchmarkQwen3.5 397B A17BQwen3.8 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 397B A17B$0.0016Fits in one request
Qwen3.8 27B$0.0017Fits in one request

Qwen3.5 397B A17B costs less

Repository review

50K input + 3K output tokens

Qwen3.5 397B A17B$0.027Fits in one request
Qwen3.8 27B$0.029Fits in one request

Qwen3.5 397B A17B costs less

Agent loop

200K input + 10K output tokens

Qwen3.5 397B A17B$0.101Fits in one request
Qwen3.8 27B$0.111Fits in one request

Qwen3.5 397B A17B 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 397B A17B and Qwen3.8 27B
SpecificationQwen3.5 397B A17BQwen3.8 27B
MakerQwenQwen
WeightsOpen weightsOpen weights
ReleasedFebruary 16, 2026August 14, 2026
Context window262K1M
Input $/Mtok$0.39$0.425
Output $/Mtok$2.34$2.55
Coding indexNot benchmarked74.2
Independent measurements behind it14
Cost per solved taskNot computable$1.26
SourceOpenRouter listingOpenRouter listing

Questions

Is Qwen3.5 397B A17B better than Qwen3.8 27B for coding?

Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; Qwen3.5 397B A17B has none, so the two cannot be ranked on coding. Pick Qwen3.5 397B A17B for output tokens 1.1x cheaper ($2.34 vs $2.55 per million) and work measured by LMArena Text: medicine and healthcare or LMArena Text: creative writing, where it leads.

Which is cheaper, Qwen3.5 397B A17B or Qwen3.8 27B?

Qwen3.5 397B A17B costs $0.39 per million input tokens and $2.34 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 397B A17B or Qwen3.8 27B?

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

How many benchmarks is the Qwen3.5 397B A17B vs Qwen3.8 27B comparison based on?

48 benchmarks have a result for both models as of October 22, 2026. Qwen3.5 397B A17B'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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