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GLM 4.6 vs Qwen3.5 397B A17B

Updated October 21, 2026. 45 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

Replace GLM 4.6 Replace Qwen3.5 397B A17B
GLM 4.6Z.ai
Coding
52.2
Overall
18.5
Output $/M
$2.20
9
Qwen3.5 397B A17BQwen
Coding
28.6
Overall
21.4
Output $/M
$2.34
36
vs boards won of 45 shared

Decision reading

Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 28.6 for Qwen3.5 397B A17B.

Pick Qwen3.5 397B A17B when the context window is larger, 262K against 205K tokens, or when what matters is LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Model AGLM 4.6Z.ai52.2/1002 independent benchmarksNo cost per solved taskModel BQwen3.5 397B A17BQwen28.6/1001 independent benchmarkNo cost per solved task
Shared results
45
GLM 4.6 only
21
Qwen3.5 397B A17B only
20
Like-for-like categories
8 of 9

Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.

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 4.6 52.2 from 2 benchmarks, Qwen3.5 397B A17B 28.6 from 1 benchmark.
Widest category
Agentic work, 18.7 points apart on average over 1 shared benchmark.
Widest single gap
IFBench: GLM 4.6 43.4% against Qwen3.5 397B A17B 78.8%, 35.4 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against Qwen3.5 397B A17B $2.34 per million output tokens, 1.1x apart.
Context window
GLM 4.6 takes 205K 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 4.6BarsQwen3.5 397B A17BTies
Coding06
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge13
Web design16
General712
All shared boards936
CodingLike-for-likeFour of these boards feed the Vibe Coding Index06
Coding benchmarks for GLM 4.6 and Qwen3.5 397B A17B
BenchmarkGLM 4.6Qwen3.5 397B A17BLeader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev13411400Qwen3.5 397B A17B
AA Coding Index45.8Not measuredOnly GLM 4.6 measured
LiveCodeBench (Vals AI)81.0%Not measuredOnly GLM 4.6 measured
Vibe Code Bench (Vals AI)3.1%Not measuredOnly GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
SciCodeNot measured44.8%Only Qwen3.5 397B A17B measured
Terminal-Bench Hard (Artificial Analysis run)28.8%40.9%Qwen3.5 397B A17B
Terminal-Bench24.5%Not measuredOnly GLM 4.6 measured
ALE-Bench340.8621.9Qwen3.5 397B A17B
LMArena Text: coding14491464Qwen3.5 397B A17B
LMArena Text: software and IT14551463Qwen3.5 397B A17B
LMArena WebDev: ReactNot measured1388Only Qwen3.5 397B A17B measured
LMArena WebDev: HTML13501410Qwen3.5 397B A17B
Neither model has a result on the other 24 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6 and Qwen3.5 397B A17B
BenchmarkGLM 4.6Qwen3.5 397B A17BLeader
AA Intelligence Index18.521.4Qwen3.5 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 4.6 and Qwen3.5 397B A17B
BenchmarkGLM 4.6Qwen3.5 397B A17BLeader
Design Arena: agents building full-stack apps1029Not measuredOnly GLM 4.6 measured
Design Arena: agents building mobile apps1096Not measuredOnly GLM 4.6 measured
Design Arena: agents building Android apps1090Not measuredOnly GLM 4.6 measured
Design Arena: agents building Godot games1180Not measuredOnly GLM 4.6 measured
τ²-Bench Telecom76.9%95.6%Qwen3.5 397B A17B
APEX-AgentsNot measured24.9%Only Qwen3.5 397B A17B measured
Neither model has a result on the other 33 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GLM 4.6 and Qwen3.5 397B A17B
BenchmarkGLM 4.6Qwen3.5 397B A17BLeader
GPQA Diamond74.5%Not measuredOnly GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)78.0%89.3%Qwen3.5 397B A17B
Humanity's Last Exam (Artificial Analysis run)14.5%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 prompts14401448Qwen3.5 397B A17B
LMArena Text: expert14321462Qwen3.5 397B A17B
LMArena Text: hard prompts in English14451450Qwen3.5 397B A17B
LMArena Vision: diagramsNot measured1277Only Qwen3.5 397B A17B measured
LMArena Vision: homeworkNot measured1292Only Qwen3.5 397B A17B 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 4.6 and Qwen3.5 397B A17B
BenchmarkGLM 4.6Qwen3.5 397B A17BLeader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 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: math14321453Qwen3.5 397B A17B
LMArena Text: mathematical work14471454Qwen3.5 397B A17B
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
Knowledge benchmarks for GLM 4.6 and Qwen3.5 397B A17B
BenchmarkGLM 4.6Qwen3.5 397B A17BLeader
MMLU Pro82.2%Not measuredOnly GLM 4.6 measured
LegalBench (Vals AI)79.6%Not measuredOnly GLM 4.6 measured
CorpFin (Vals AI)56.8%Not measuredOnly GLM 4.6 measured
TaxEval (Vals AI)66.2%Not measuredOnly GLM 4.6 measured
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
LMArena Text: science14561457Qwen3.5 397B A17B
LMArena Text: medicine and healthcare14551455Qwen3.5 397B A17B
LMArena Text: legal and government14481441GLM 4.6
LMArena Text: business and finance14161435Qwen3.5 397B A17B
Neither model has a result on the other 7 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index16
Web design benchmarks for GLM 4.6 and Qwen3.5 397B A17B
BenchmarkGLM 4.6Qwen3.5 397B A17BLeader
Design Arena: websites11741191Qwen3.5 397B A17B
Design Arena: UI components11471163Qwen3.5 397B A17B
Design Arena: game dev11511141GLM 4.6
Design Arena: data viz11611175Qwen3.5 397B A17B
Design Arena: 3D11291161Qwen3.5 397B A17B
Design Arena: all code categories11661180Qwen3.5 397B A17B
Design Arena: SVG11081139Qwen3.5 397B A17B
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index712
General benchmarks for GLM 4.6 and Qwen3.5 397B A17B
BenchmarkGLM 4.6Qwen3.5 397B A17BLeader
IFBench43.4%78.8%Qwen3.5 397B A17B
Long context reasoning (AA-LCR)54.0%77.3%Qwen3.5 397B A17B
DTBenchNot measured87.5%Only Qwen3.5 397B A17B measured
LMCANot measured37.9%Only Qwen3.5 397B A17B measured
LMArena Text14401437GLM 4.6
LMArena Text: instruction following14101424Qwen3.5 397B A17B
LMArena Text: creative writing14121402GLM 4.6
LMArena VisionNot measured1262Only Qwen3.5 397B A17B measured
LMArena Text: English14491443GLM 4.6
LMArena Text: non-English14261430Qwen3.5 397B A17B
LMArena Text: Chinese14991498GLM 4.6
LMArena Text: German14471445GLM 4.6
LMArena Text: Japanese13931429Qwen3.5 397B A17B
LMArena Text: Korean14001384GLM 4.6
LMArena Text: French14591462Qwen3.5 397B A17B
LMArena Text: Spanish14361441Qwen3.5 397B A17B
LMArena Text: Russian14181430Qwen3.5 397B A17B
LMArena Text: Polish14471447Qwen3.5 397B A17B
LMArena Text: multi-turn14271446Qwen3.5 397B A17B
LMArena Text: longer queries14221441Qwen3.5 397B A17B
LMArena Text: writing and language14151415Qwen3.5 397B A17B
LMArena Text: entertainment and media14091399GLM 4.6
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 17 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and Qwen3.5 397B A17B
BenchmarkGLM 4.6Qwen3.5 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 4.6$0.0016Fits 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 4.6$0.034Fits 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 4.6$0.132Exceeds 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 4.6 and Qwen3.5 397B A17B
SpecificationZ.ai GLM 4.6Qwen3.5 397B A17B
MakerZ.aiQwen
WeightsOpen weightsOpen weights
ReleasedSeptember 30, 2025February 16, 2026
Context window205K262K
Input $/Mtok$0.55$0.39
Output $/Mtok$2.20$2.34
Coding index52.228.6
Independent measurements behind it21
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than Qwen3.5 397B A17B for coding?

Pick GLM 4.6 for coding: it scores 52.2 on the Vibe Coding Index against 28.6 for Qwen3.5 397B A17B. Pick Qwen3.5 397B A17B when the context window is larger, 262K against 205K tokens, or when what matters is LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.

Which is cheaper, GLM 4.6 or Qwen3.5 397B A17B?

GLM 4.6 costs $0.55 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 4.6 or Qwen3.5 397B A17B?

Qwen3.5 397B A17B, with 262K tokens against 205K.

How many benchmarks is the GLM 4.6 vs Qwen3.5 397B A17B comparison based on?

45 benchmarks have a result for both models as of October 21, 2026. GLM 4.6's coding index rests on 2 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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