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GLM 4.5 vs Qwen3.5 397B A17B

Qwen3.5 397B A17B leads GLM 4.5 on the Artificial Analysis Intelligence Index, 21.4 to 12.8, at about the same output price ($2.34 vs $2.20 per million). Updated October 22, 2026; 43 benchmarks have a result for both.

Replace GLM 4.5 Replace Qwen3.5 397B A17B
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
4
Qwen3.5 397B A17BQwen
Coding
No data
Overall
57.1
Output $/M
$2.34
39
vs boards won of 43 shared

GLM 4.5 or Qwen3.5 397B A17B: which to pick

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

Pick GLM 4.5 for output tokens 1.1x cheaper ($2.20 vs $2.34 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or ALE-Bench, where it leads.

Model AGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.5 397B A17BQwenNot benchmarked1 independent benchmarkNo cost per solved task
Shared results
43
GLM 4.5 only
18
Qwen3.5 397B A17B only
22
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
GLM 4.5 not benchmarked, Qwen3.5 397B A17B not benchmarked.
Widest category
Agentic work, 52.6 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.5 43.0% against Qwen3.5 397B A17B 95.6%, 52.6 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.1x apart.
Context window
GLM 4.5 takes 131K tokens, Qwen3.5 397B A17B 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
CategoryGLM 4.5BarsQwen3.5 397B A17BTies
Coding04
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
Web design25
General217
All shared boards439
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for GLM 4.5 and Qwen3.5 397B A17B
BenchmarkGLM 4.5Qwen3.5 397B A17BLeader
SWE-bench Verified64.2%Not measuredOnly GLM 4.5 measured
LMArena WebDevNot measured1400Only Qwen3.5 397B A17B measured
LiveCodeBench (Vals AI)67.5%Not measuredOnly GLM 4.5 measured
LiveCodeBench (Artificial Analysis run)73.8%Not measuredOnly GLM 4.5 measured
SciCodeNot measured44.8%Only Qwen3.5 397B A17B measured
Terminal-Bench Hard (Artificial Analysis run)22.0%40.9%Qwen3.5 397B A17B
WeirdML40.6%Not measuredOnly GLM 4.5 measured
ALE-Bench344.8621.9Qwen3.5 397B A17B
LMArena Text: coding14341464Qwen3.5 397B A17B
LMArena Text: software and IT14401463Qwen3.5 397B A17B
LMArena WebDev: ReactNot measured1388Only Qwen3.5 397B A17B measured
LMArena WebDev: HTMLNot measured1410Only Qwen3.5 397B A17B measured
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.5 and Qwen3.5 397B A17B
BenchmarkGLM 4.5Qwen3.5 397B A17BLeader
AA Intelligence Index12.821.4Qwen3.5 397B A17B
Epoch Capabilities IndexNot measured146.8Only Qwen3.5 397B A17B measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.5 and Qwen3.5 397B A17B
BenchmarkGLM 4.5Qwen3.5 397B A17BLeader
τ²-Bench Telecom43.0%95.6%Qwen3.5 397B A17B
APEX-AgentsNot measured24.9%Only Qwen3.5 397B A17B measured
Neither model has a result on the other 39 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GLM 4.5 and Qwen3.5 397B A17B
BenchmarkGLM 4.5Qwen3.5 397B A17BLeader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)78.2%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)13.0%29.0%Qwen3.5 397B A17B
GPQA Diamond (Epoch AI run)Not measured86.4%Only Qwen3.5 397B A17B measured
Mystery game puzzlesNot measured18.0%Only Qwen3.5 397B A17B measured
LMArena Text: hard prompts14291448Qwen3.5 397B A17B
LMArena Text: expert14331462Qwen3.5 397B A17B
LMArena Text: hard prompts in English14291450Qwen3.5 397B A17B
LMArena Vision: diagramsNot measured1277Only Qwen3.5 397B A17B measured
LMArena Vision: homeworkNot measured1292Only Qwen3.5 397B A17B measured
Kagi LLM Benchmark57.9%Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 11 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.5 and Qwen3.5 397B A17B
BenchmarkGLM 4.5Qwen3.5 397B A17BLeader
AIME86.7%Not measuredOnly GLM 4.5 measured
MATH 500 (Vals AI)94.0%Not measuredOnly GLM 4.5 measured
MGSM (Vals AI)90.8%Not measuredOnly GLM 4.5 measured
AA Math Index73.7Not measuredOnly GLM 4.5 measured
AIME 2025 (Artificial Analysis run)73.7%Not measuredOnly GLM 4.5 measured
OTIS Mock AIMENot measured88.9%Only Qwen3.5 397B A17B measured
FrontierMath, tiers 1 to 3Not measured31.2%Only Qwen3.5 397B A17B measured
LMArena Text: math14271453Qwen3.5 397B A17B
LMArena Text: mathematical work14301454Qwen3.5 397B A17B
Neither model has a result on the other 4 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
Knowledge benchmarks for GLM 4.5 and Qwen3.5 397B A17B
BenchmarkGLM 4.5Qwen3.5 397B A17BLeader
MMLU Pro81.2%Not measuredOnly GLM 4.5 measured
LegalBench (Vals AI)75.6%Not measuredOnly GLM 4.5 measured
CorpFin (Vals AI)61.0%Not measuredOnly GLM 4.5 measured
TaxEval (Vals AI)72.4%Not measuredOnly GLM 4.5 measured
MedQA (Vals AI)90.0%Not measuredOnly GLM 4.5 measured
MMLU Pro (Artificial Analysis run)83.5%Not measuredOnly GLM 4.5 measured
LMArena Text: science14441457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare14551455Qwen3.5 397B A17B
LMArena Text: legal and government14381441Qwen3.5 397B A17B
LMArena Text: business and finance14201435Qwen3.5 397B A17B
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index25
Web design benchmarks for GLM 4.5 and Qwen3.5 397B A17B
BenchmarkGLM 4.5Qwen3.5 397B A17BLeader
Design Arena: websites11691190Qwen3.5 397B A17B
Design Arena: UI components11411160Qwen3.5 397B A17B
Design Arena: game dev11441140GLM 4.5
Design Arena: data viz11591175Qwen3.5 397B A17B
Design Arena: 3D11701160GLM 4.5
Design Arena: all code categories11651179Qwen3.5 397B A17B
Design Arena: SVG10961139Qwen3.5 397B A17B
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index217
General benchmarks for GLM 4.5 and Qwen3.5 397B A17B
BenchmarkGLM 4.5Qwen3.5 397B A17BLeader
IFBench44.1%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)52.7%77.3%Qwen3.5 397B A17B
DTBenchNot measured87.5%Only Qwen3.5 397B A17B measured
LMCANot measured37.9%Only Qwen3.5 397B A17B measured
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
LMArena Text14301437Qwen3.5 397B A17B
LMArena Text: instruction following14041424Qwen3.5 397B A17B
LMArena Text: creative writing13951402Qwen3.5 397B A17B
LMArena VisionNot measured1262Only Qwen3.5 397B A17B measured
LMArena Text: English14371443Qwen3.5 397B A17B
LMArena Text: non-English14171430Qwen3.5 397B A17B
LMArena Text: Chinese14651498Qwen3.5 397B A17B
LMArena Text: German14071445Qwen3.5 397B A17B
LMArena Text: Japanese14151429Qwen3.5 397B A17B
LMArena Text: Korean13801384Qwen3.5 397B A17B
LMArena Text: French14181462Qwen3.5 397B A17B
LMArena Text: Spanish14541441GLM 4.5
LMArena Text: Russian14131430Qwen3.5 397B A17B
LMArena Text: Polish14221447Qwen3.5 397B A17B
LMArena Text: multi-turn14151446Qwen3.5 397B A17B
LMArena Text: longer queries14121441Qwen3.5 397B A17B
LMArena Text: writing and language14031415Qwen3.5 397B A17B
LMArena Text: entertainment and media14041399GLM 4.5
LMArena Vision: EnglishNot measured1268Only Qwen3.5 397B A17B measured
LMArena Vision: ChineseNot measured1314Only Qwen3.5 397B A17B measured
LMArena Vision: reading textNot measured1271Only Qwen3.5 397B A17B measured
LMArena Vision: humorNot measured1262Only Qwen3.5 397B A17B measured
LMArena Vision: captioningNot measured1230Only Qwen3.5 397B A17B measured
LMArena Vision: recognizing thingsNot measured1265Only Qwen3.5 397B A17B measured
LMArena Vision: creative writingNot measured1260Only Qwen3.5 397B A17B measured
Neither model has a result on the other 16 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.5 and Qwen3.5 397B A17B
BenchmarkGLM 4.5Qwen3.5 397B A17BLeader
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

GLM 4.5$0.0017Fits in one request
Qwen3.5 397B A17B$0.0016Fits in one request

Qwen3.5 397B A17B costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
Qwen3.5 397B A17B$0.027Fits in one request

Qwen3.5 397B A17B costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window
Qwen3.5 397B A17B$0.101Fits 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 GLM 4.5 and Qwen3.5 397B A17B
SpecificationZ.ai GLM 4.5Qwen3.5 397B A17B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedJuly 25, 2025February 16, 2026
Context window131K262K
Input $/Mtok$0.60$0.39
Output $/Mtok$2.20$2.34
Coding indexNot benchmarkedNot benchmarked
Independent measurements behind it01
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than Qwen3.5 397B A17B for coding?

Neither model has a coding score yet, so the two cannot be ranked on coding. Pick GLM 4.5 for output tokens 1.1x cheaper ($2.20 vs $2.34 per million) and work measured by Terminal-Bench Hard (Artificial Analysis run) or ALE-Bench, where it leads.

Which is cheaper, GLM 4.5 or Qwen3.5 397B A17B?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.5 397B A17B costs $0.39 and $2.34. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.5 or Qwen3.5 397B A17B?

Qwen3.5 397B A17B, with 262K tokens against 131K.

How many benchmarks is the GLM 4.5 vs Qwen3.5 397B A17B comparison based on?

43 benchmarks have a result for both models as of October 22, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and Qwen3.5 397B A17B's on 1. Where only one model has a result, the page says so instead of naming a winner.

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