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GLM 4.5V vs Qwen3.6 Plus

Qwen3.6 Plus leads GLM 4.5V on the Artificial Analysis Intelligence Index, 27.0 to 7.6, at about the same output price ($1.95 vs $1.80 per million). Updated October 21, 2026; 31 benchmarks have a result for both.

Replace GLM 4.5V Replace Qwen3.6 Plus
GLM 4.5VZ.ai
Coding
No data
Overall
7.6
Output $/M
$1.80
0
Qwen3.6 PlusQwen
Coding
44.1
Overall
27.0
Output $/M
$1.95
31
vs boards won of 31 shared

GLM 4.5V or Qwen3.6 Plus: which to pick

Only Qwen3.6 Plus has a coding score, 44.1 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding.

Pick GLM 4.5V for output tokens 1.1x cheaper ($1.80 vs $1.95 per million).

Model AGLM 4.5VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BQwen3.6 PlusQwen44.1/1003 independent benchmarks$1.19 per solved task
Shared results
31
GLM 4.5V only
9
Qwen3.6 Plus only
52
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
GLM 4.5V not benchmarked, Qwen3.6 Plus 44.1 from 3 benchmarks.
Widest category
Agentic work, 75.1 points apart on average over 1 shared benchmark.
Widest single gap
Long context reasoning (AA-LCR): GLM 4.5V 0.0% against Qwen3.6 Plus 78.3%, 78.3 points apart on a 0 to 100 scale.
Output price
GLM 4.5V $1.80 against Qwen3.6 Plus $1.95 per million output tokens, 1.1x apart.
Context window
GLM 4.5V takes 66K tokens, Qwen3.6 Plus 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
CategoryGLM 4.5VBarsQwen3.6 PlusTies
Coding03
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge04
General015
All shared boards031
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
Coding benchmarks for GLM 4.5V and Qwen3.6 Plus
BenchmarkGLM 4.5VQwen3.6 PlusLeader
LMArena WebDevNot measured1461Only Qwen3.6 Plus measured
AA Coding IndexNot measured54.5Only Qwen3.6 Plus measured
LiveCodeBench (Vals AI)Not measured86.0%Only Qwen3.6 Plus measured
SWE-bench Verified (Vals AI)Not measured73.4%Only Qwen3.6 Plus measured
Vibe Code Bench (Vals AI)Not measured25.6%Only Qwen3.6 Plus measured
Code migration (Vals AI)Not measured11.1%Only Qwen3.6 Plus measured
ProgramBench (Vals AI)Not measured0.0%Only Qwen3.6 Plus measured
Terminal-Bench 2.1 (Vals AI)Not measured53.2%Only Qwen3.6 Plus measured
LiveCodeBench (Artificial Analysis run)60.4%Not measuredOnly GLM 4.5V measured
Terminal-Bench Hard (Artificial Analysis run)6.8%43.9%Qwen3.6 Plus
ALE-BenchNot measured670.1Only Qwen3.6 Plus measured
LMArena Text: coding13471467Qwen3.6 Plus
LMArena Text: software and IT13531462Qwen3.6 Plus
LMArena WebDev: ReactNot measured1454Only Qwen3.6 Plus measured
LMArena WebDev: HTMLNot measured1463Only Qwen3.6 Plus measured
LMArena WebDev: from an imageNot measured1472Only Qwen3.6 Plus measured
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.5V and Qwen3.6 Plus
BenchmarkGLM 4.5VQwen3.6 PlusLeader
AA Intelligence Index7.627.0Qwen3.6 Plus
Epoch Capabilities IndexNot measured147.3Only Qwen3.6 Plus measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.5V and Qwen3.6 Plus
BenchmarkGLM 4.5VQwen3.6 PlusLeader
Finance Agent (Vals AI)Not measured40.9%Only Qwen3.6 Plus measured
Tax Agent Bench (Vals AI)Not measured7.7%Only Qwen3.6 Plus measured
Legal Research Bench (Vals AI)Not measured14.9%Only Qwen3.6 Plus measured
Excel Modeling Benchmark (Vals AI)Not measured32.9%Only Qwen3.6 Plus measured
τ²-Bench Telecom22.5%97.7%Qwen3.6 Plus
Vending-Bench 2Not measured$5,115Only Qwen3.6 Plus 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 4.5V and Qwen3.6 Plus
BenchmarkGLM 4.5VQwen3.6 PlusLeader
GPQA DiamondNot measured87.4%Only Qwen3.6 Plus measured
MMMU Pro (Vals AI)Not measured84.2%Only Qwen3.6 Plus measured
GPQA Diamond (Artificial Analysis run)68.4%88.2%Qwen3.6 Plus
Humanity's Last Exam (Artificial Analysis run)6.3%27.8%Qwen3.6 Plus
GPQA Diamond (Epoch AI run)Not measured88.4%Only Qwen3.6 Plus measured
Chess puzzlesNot measured14.0%Only Qwen3.6 Plus measured
Mystery game puzzlesNot measured12.0%Only Qwen3.6 Plus measured
LMArena Text: hard prompts13351448Qwen3.6 Plus
LMArena Text: expert13531454Qwen3.6 Plus
LMArena Text: hard prompts in English13571455Qwen3.6 Plus
LMArena Vision: diagrams1151Not measuredOnly GLM 4.5V measured
LMArena Vision: homework1163Not measuredOnly GLM 4.5V measured
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 GLM 4.5V and Qwen3.6 Plus
BenchmarkGLM 4.5VQwen3.6 PlusLeader
AIMENot measured94.6%Only Qwen3.6 Plus measured
AA Math Index73.0Not measuredOnly GLM 4.5V measured
AIME 2025 (Artificial Analysis run)73.0%Not measuredOnly GLM 4.5V measured
OTIS Mock AIMENot measured93.3%Only Qwen3.6 Plus measured
FrontierMath, tiers 1 to 3Not measured38.2%Only Qwen3.6 Plus measured
LMArena Text: math13541449Qwen3.6 Plus
LMArena Text: mathematical work13361448Qwen3.6 Plus
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 GLM 4.5V and Qwen3.6 Plus
BenchmarkGLM 4.5VQwen3.6 PlusLeader
MMLU ProNot measured87.7%Only Qwen3.6 Plus measured
LegalBench (Vals AI)Not measured84.2%Only Qwen3.6 Plus measured
CaseLaw (Vals AI)Not measured51.5%Only Qwen3.6 Plus measured
CorpFin (Vals AI)Not measured61.9%Only Qwen3.6 Plus measured
TaxEval (Vals AI)Not measured74.7%Only Qwen3.6 Plus measured
MortgageTax (Vals AI)Not measured68.0%Only Qwen3.6 Plus measured
MedCode (Vals AI)Not measured36.9%Only Qwen3.6 Plus measured
MMLU Pro (Artificial Analysis run)78.8%Not measuredOnly GLM 4.5V measured
SimpleQA VerifiedNot measured44.1%Only Qwen3.6 Plus measured
LMArena Text: science13301439Qwen3.6 Plus
LMArena Text: medicine and healthcare13491442Qwen3.6 Plus
LMArena Text: legal and government13381437Qwen3.6 Plus
LMArena Text: business and finance13091430Qwen3.6 Plus
Neither model has a result on the other 4 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 4.5V and Qwen3.6 Plus
BenchmarkGLM 4.5VQwen3.6 PlusLeader
Design Arena: websitesNot measured1243Only Qwen3.6 Plus measured
Design Arena: UI componentsNot measured1233Only Qwen3.6 Plus measured
Design Arena: game devNot measured1211Only Qwen3.6 Plus measured
Design Arena: data vizNot measured1228Only Qwen3.6 Plus measured
Design Arena: 3DNot measured1202Only Qwen3.6 Plus measured
Design Arena: all code categoriesNot measured1236Only Qwen3.6 Plus measured
Design Arena: SVGNot measured1161Only Qwen3.6 Plus measured
Design Arena: ASCII artNot measured1113Only Qwen3.6 Plus measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index015
General benchmarks for GLM 4.5V and Qwen3.6 Plus
BenchmarkGLM 4.5VQwen3.6 PlusLeader
SAGE (Vals AI)Not measured44.9%Only Qwen3.6 Plus measured
MedScribe (Vals AI)Not measured77.0%Only Qwen3.6 Plus measured
Vals Multimodal IndexNot measured51.5%Only Qwen3.6 Plus measured
IFBench34.2%75.2%Qwen3.6 Plus
Long context reasoning (AA-LCR)0.0%78.3%Qwen3.6 Plus
DTBenchNot measured81.9%Only Qwen3.6 Plus measured
LMCANot measured33.1%Only Qwen3.6 Plus measured
CL-benchNot measured20.3%Only Qwen3.6 Plus measured
LMArena Text13331436Qwen3.6 Plus
LMArena Text: instruction following13111424Qwen3.6 Plus
LMArena Text: creative writing12941404Qwen3.6 Plus
LMArena Vision1154Not measuredOnly GLM 4.5V measured
LMArena Text: English13621445Qwen3.6 Plus
LMArena Text: non-English13031424Qwen3.6 Plus
LMArena Text: Chinese13371477Qwen3.6 Plus
LMArena Text: GermanNot measured1451Only Qwen3.6 Plus measured
LMArena Text: JapaneseNot measured1390Only Qwen3.6 Plus measured
LMArena Text: KoreanNot measured1379Only Qwen3.6 Plus measured
LMArena Text: FrenchNot measured1455Only Qwen3.6 Plus measured
LMArena Text: Spanish13361432Qwen3.6 Plus
LMArena Text: Russian12981434Qwen3.6 Plus
LMArena Text: Polish13021433Qwen3.6 Plus
LMArena Text: multi-turn13331438Qwen3.6 Plus
LMArena Text: longer queries13051439Qwen3.6 Plus
LMArena Text: writing and language13021418Qwen3.6 Plus
LMArena Text: entertainment and media13031402Qwen3.6 Plus
LMArena Vision: English1183Not measuredOnly GLM 4.5V measured
LMArena Vision: reading text1151Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 18 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.5V and Qwen3.6 Plus
BenchmarkGLM 4.5VQwen3.6 PlusLeader
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 4.5V$0.0015Fits in one request
Qwen3.6 Plus$0.0013Fits in one request

Qwen3.6 Plus costs less

Repository review

50K input + 3K output tokens

GLM 4.5V$0.035Fits in one request
Qwen3.6 Plus$0.022Fits in one request

Qwen3.6 Plus costs less

Agent loop

200K input + 10K output tokens

GLM 4.5V$0.138Exceeds the context window
Qwen3.6 Plus$0.085Fits in one request

Qwen3.6 Plus 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 4.5V and Qwen3.6 Plus
SpecificationZ.ai GLM 4.5VQwen3.6 Plus
MakerZ.aiQwen
WeightsOpen weightsNo public weights listed
ReleasedAugust 11, 2025April 2, 2026
Context window66K1M
Input $/Mtok$0.60$0.325
Output $/Mtok$1.80$1.95
Coding indexNot benchmarked44.1
Independent measurements behind it03
Cost per solved taskNot computable$1.19
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5V better than Qwen3.6 Plus for coding?

Only Qwen3.6 Plus has a coding score, 44.1 on the Vibe Coding Index; GLM 4.5V has none, so the two cannot be ranked on coding. Pick GLM 4.5V for output tokens 1.1x cheaper ($1.80 vs $1.95 per million).

Which is cheaper, GLM 4.5V or Qwen3.6 Plus?

GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; Qwen3.6 Plus costs $0.325 and $1.95. Cost per solved task is known for Qwen3.6 Plus only.

Which has the bigger context window, GLM 4.5V or Qwen3.6 Plus?

Qwen3.6 Plus, with 1M tokens against 66K.

How many benchmarks is the GLM 4.5V vs Qwen3.6 Plus comparison based on?

31 benchmarks have a result for both models as of October 21, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and Qwen3.6 Plus's on 3. Where only one model has a result, the page says so instead of naming a winner.

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