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GLM 4.5V vs GLM 4.6

GLM 4.6 leads GLM 4.5V on the Artificial Analysis Intelligence Index, 18.5 to 7.6, and GLM 4.5V costs 1.2x less per output token ($1.80 vs $2.20 per million). Updated October 21, 2026; 35 benchmarks have a result for both.

Replace GLM 4.5V Replace GLM 4.6
GLM 4.5VZ.ai
Coding
No data
Overall
7.6
Output $/M
$1.80
0
GLM 4.6Z.ai
Coding
8.8
Overall
18.5
Output $/M
$2.20
35
vs boards won of 35 shared

GLM 4.5V or GLM 4.6: which to pick

Only GLM 4.6 has a coding score, 8.8 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 1.2x cheaper ($1.80 vs $2.20 per million).

Model AGLM 4.5VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved task
Shared results
35
GLM 4.5V only
5
GLM 4.6 only
31
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, GLM 4.6 8.8 from 2 benchmarks.
Widest category
Agentic work, 54.4 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.5V 22.5% against GLM 4.6 76.9%, 54.4 points apart on a 0 to 100 scale.
Output price
GLM 4.5V $1.80 against GLM 4.6 $2.20 per million output tokens, 1.2x apart.
Context window
GLM 4.5V takes 66K tokens, GLM 4.6 205K.

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 4.5VBarsGLM 4.6Ties
Coding04
Intelligence01
Agentic work01
Reasoning05
Math04
Knowledge05
General015
All shared boards035
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for GLM 4.5V and GLM 4.6
BenchmarkGLM 4.5VGLM 4.6Leader
SWE-bench VerifiedNot measured68.2%Only GLM 4.6 measured
LMArena WebDevNot measured1341Only GLM 4.6 measured
AA Coding IndexNot measured45.8Only GLM 4.6 measured
LiveCodeBench (Vals AI)Not measured81.0%Only GLM 4.6 measured
Vibe Code Bench (Vals AI)Not measured3.1%Only GLM 4.6 measured
LiveCodeBench (Artificial Analysis run)60.4%69.5%GLM 4.6
Terminal-Bench Hard (Artificial Analysis run)6.8%28.8%GLM 4.6
Terminal-BenchNot measured24.5%Only GLM 4.6 measured
ALE-BenchNot measured340.8Only GLM 4.6 measured
LMArena Text: coding13471449GLM 4.6
LMArena Text: software and IT13531455GLM 4.6
LMArena WebDev: HTMLNot measured1350Only GLM 4.6 measured
Neither model has a result on the other 26 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.5V and GLM 4.6
BenchmarkGLM 4.5VGLM 4.6Leader
AA Intelligence Index7.618.5GLM 4.6
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.5V and GLM 4.6
BenchmarkGLM 4.5VGLM 4.6Leader
Design Arena: agents building full-stack appsNot measured1029Only GLM 4.6 measured
Design Arena: agents building mobile appsNot measured1096Only GLM 4.6 measured
Design Arena: agents building Android appsNot measured1084Only GLM 4.6 measured
Design Arena: agents building Godot gamesNot measured1180Only GLM 4.6 measured
τ²-Bench Telecom22.5%76.9%GLM 4.6
Neither model has a result on the other 34 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
Reasoning benchmarks for GLM 4.5V and GLM 4.6
BenchmarkGLM 4.5VGLM 4.6Leader
GPQA DiamondNot measured74.5%Only GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)68.4%78.0%GLM 4.6
Humanity's Last Exam (Artificial Analysis run)6.3%14.5%GLM 4.6
LMArena Text: hard prompts13351440GLM 4.6
LMArena Text: expert13531432GLM 4.6
LMArena Text: hard prompts in English13571445GLM 4.6
LMArena Vision: diagrams1151Not measuredOnly GLM 4.5V measured
LMArena Vision: homework1163Not measuredOnly GLM 4.5V measured
Neither model has a result on the other 13 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for GLM 4.5V and GLM 4.6
BenchmarkGLM 4.5VGLM 4.6Leader
AIMENot measured92.7%Only GLM 4.6 measured
MGSM (Vals AI)Not measured89.8%Only GLM 4.6 measured
AA Math Index73.086.0GLM 4.6
AIME 2025 (Artificial Analysis run)73.0%86.0%GLM 4.6
LMArena Text: math13541432GLM 4.6
LMArena Text: mathematical work13361447GLM 4.6
Neither model has a result on the other 7 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for GLM 4.5V and GLM 4.6
BenchmarkGLM 4.5VGLM 4.6Leader
MMLU ProNot measured82.2%Only GLM 4.6 measured
LegalBench (Vals AI)Not measured79.6%Only GLM 4.6 measured
CorpFin (Vals AI)Not measured56.8%Only GLM 4.6 measured
TaxEval (Vals AI)Not measured66.2%Only GLM 4.6 measured
MedQA (Vals AI)Not measured92.2%Only GLM 4.6 measured
MMLU Pro (Artificial Analysis run)78.8%82.9%GLM 4.6
LMArena Text: science13301456GLM 4.6
LMArena Text: medicine and healthcare13491455GLM 4.6
LMArena Text: legal and government13381448GLM 4.6
LMArena Text: business and finance13091416GLM 4.6
Neither model has a result on the other 7 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
Web design benchmarks for GLM 4.5V and GLM 4.6
BenchmarkGLM 4.5VGLM 4.6Leader
Design Arena: websitesNot measured1174Only GLM 4.6 measured
Design Arena: UI componentsNot measured1148Only GLM 4.6 measured
Design Arena: game devNot measured1151Only GLM 4.6 measured
Design Arena: data vizNot measured1161Only GLM 4.6 measured
Design Arena: 3DNot measured1129Only GLM 4.6 measured
Design Arena: all code categoriesNot measured1166Only GLM 4.6 measured
Design Arena: SVGNot measured1109Only GLM 4.6 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index015
General benchmarks for GLM 4.5V and GLM 4.6
BenchmarkGLM 4.5VGLM 4.6Leader
IFBench34.2%43.4%GLM 4.6
Long context reasoning (AA-LCR)0.0%54.0%GLM 4.6
LMArena Text13331440GLM 4.6
LMArena Text: instruction following13111410GLM 4.6
LMArena Text: creative writing12941412GLM 4.6
LMArena Vision1154Not measuredOnly GLM 4.5V measured
LMArena Text: English13621449GLM 4.6
LMArena Text: non-English13031426GLM 4.6
LMArena Text: Chinese13371499GLM 4.6
LMArena Text: GermanNot measured1447Only GLM 4.6 measured
LMArena Text: JapaneseNot measured1393Only GLM 4.6 measured
LMArena Text: KoreanNot measured1400Only GLM 4.6 measured
LMArena Text: FrenchNot measured1459Only GLM 4.6 measured
LMArena Text: Spanish13361436GLM 4.6
LMArena Text: Russian12981418GLM 4.6
LMArena Text: Polish13021447GLM 4.6
LMArena Text: multi-turn13331427GLM 4.6
LMArena Text: longer queries13051422GLM 4.6
LMArena Text: writing and language13021415GLM 4.6
LMArena Text: entertainment and media13031409GLM 4.6
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 24 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 GLM 4.6
BenchmarkGLM 4.5VGLM 4.6Leader
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
GLM 4.6$0.0017Fits in one request

GLM 4.5V costs less

Repository review

50K input + 3K output tokens

GLM 4.5V$0.035Fits in one request
GLM 4.6$0.037Fits in one request

GLM 4.5V costs less

Agent loop

200K input + 10K output tokens

GLM 4.5V$0.138Exceeds the context window
GLM 4.6$0.142Exceeds the context window

GLM 4.5V 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 GLM 4.6
SpecificationZ.ai GLM 4.5VZ.ai GLM 4.6
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedAugust 11, 2025September 30, 2025
Context window66K205K
Input $/Mtok$0.60$0.60
Output $/Mtok$1.80$2.20
Coding indexNot benchmarked8.8
Independent measurements behind it02
Cost per solved taskNot computableNot computable
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5V better than GLM 4.6 for coding?

Only GLM 4.6 has a coding score, 8.8 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 1.2x cheaper ($1.80 vs $2.20 per million).

Which is cheaper, GLM 4.5V or GLM 4.6?

GLM 4.5V costs $0.60 per million input tokens and $1.80 per million output tokens; GLM 4.6 costs $0.60 and $2.20. Cost per solved task is known for neither of them.

Which has the bigger context window, GLM 4.5V or GLM 4.6?

GLM 4.6, with 205K tokens against 66K.

How many benchmarks is the GLM 4.5V vs GLM 4.6 comparison based on?

35 benchmarks have a result for both models as of October 21, 2026. GLM 4.5V's coding index rests on 0 independent benchmarks and GLM 4.6's on 2. Where only one model has a result, the page says so instead of naming a winner.

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