Enter opens the highlighted result. Esc closes.

GLM 4.6V vs GLM 4.7

GLM 4.7 leads GLM 4.6V on the Artificial Analysis Intelligence Index, 22.2 to 11.2, and GLM 4.6V costs 2.4x less per output token ($0.90 vs $2.20 per million). Updated October 21, 2026; 31 benchmarks have a result for both.

Replace GLM 4.6V Replace GLM 4.7
GLM 4.6VZ.ai
Coding
No data
Overall
No data
Output $/M
$0.90
0
GLM 4.7Z.ai
Coding
55.1
Overall
56.9
Output $/M
$2.20
31
vs boards won of 31 shared

GLM 4.6V or GLM 4.7: which to pick

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; GLM 4.6V has none, so the two cannot be ranked on coding.

Pick GLM 4.6V for output tokens 2.4x cheaper ($0.90 vs $2.20 per million).

Model AGLM 4.6VZ.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGLM 4.7Z.ai55.1/1002 independent benchmarks$0.29 per solved task
Shared results
31
GLM 4.6V only
7
GLM 4.7 only
50
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 4.6V not benchmarked, GLM 4.7 55.1 from 2 benchmarks.
Widest category
Agentic work, 64.3 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.6V 31.6% against GLM 4.7 95.9%, 64.3 points apart on a 0 to 100 scale.
Output price
GLM 4.6V $0.90 against GLM 4.7 $2.20 per million output tokens, 2.4x apart.
Context window
GLM 4.6V takes 131K tokens, GLM 4.7 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 4.6VBarsGLM 4.7Ties
Coding04
Intelligence01
Agentic work01
Reasoning05
Math02
Knowledge05
General013
All shared boards031
CodingLike-for-likeSix of these boards feed the Vibe Coding Index04
Coding benchmarks for GLM 4.6V and GLM 4.7
BenchmarkGLM 4.6VGLM 4.7Leader
LMArena WebDevNot measured1435Only GLM 4.7 measured
AA Coding IndexNot measured45.3Only GLM 4.7 measured
LiveCodeBench (Vals AI)Not measured82.2%Only GLM 4.7 measured
SWE-bench Verified (Vals AI)Not measured69.4%Only GLM 4.7 measured
LiveCodeBench (Artificial Analysis run)41.1%89.4%GLM 4.7
Terminal-Bench Hard (Artificial Analysis run)14.4%31.8%GLM 4.7
Terminal-BenchNot measured33.4%Only GLM 4.7 measured
ALE-BenchNot measured399.5Only GLM 4.7 measured
LMArena Text: coding13891454GLM 4.7
LMArena Text: software and IT13911453GLM 4.7
LMArena WebDev: ReactNot measured1420Only GLM 4.7 measured
LMArena WebDev: HTMLNot measured1447Only GLM 4.7 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 4.6V and GLM 4.7
BenchmarkGLM 4.6VGLM 4.7Leader
AA Intelligence Index11.222.2GLM 4.7
Epoch Capabilities IndexNot measured143.6Only GLM 4.7 measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.6V and GLM 4.7
BenchmarkGLM 4.6VGLM 4.7Leader
Design Arena: agents building full-stack appsNot measured1050Only GLM 4.7 measured
Design Arena: agents building mobile appsNot measured1105Only GLM 4.7 measured
Design Arena: agents building Android appsNot measured1112Only GLM 4.7 measured
Design Arena: agents building Godot gamesNot measured1058Only GLM 4.7 measured
τ²-Bench Telecom31.6%95.9%GLM 4.7
Vending-Bench 2Not measured$2,377Only GLM 4.7 measured
BrowseComp-PlusNot measured64.7%Only GLM 4.7 measured
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 4.6V and GLM 4.7
BenchmarkGLM 4.6VGLM 4.7Leader
GPQA DiamondNot measured80.0%Only GLM 4.7 measured
GPQA Diamond (Artificial Analysis run)71.9%85.9%GLM 4.7
Humanity's Last Exam (Artificial Analysis run)9.6%27.4%GLM 4.7
GPQA Diamond (Epoch AI run)Not measured83.3%Only GLM 4.7 measured
SimpleBenchNot measured47.7%Only GLM 4.7 measured
Chess puzzlesNot measured21.0%Only GLM 4.7 measured
LMArena Text: hard prompts13671443GLM 4.7
LMArena Text: expert13711424GLM 4.7
LMArena Text: hard prompts in English13911452GLM 4.7
LMArena Vision: diagrams1182Not measuredOnly GLM 4.6V measured
LMArena Vision: homework1155Not measuredOnly GLM 4.6V 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 4.6V and GLM 4.7
BenchmarkGLM 4.6VGLM 4.7Leader
AIMENot measured93.3%Only GLM 4.7 measured
MGSM (Vals AI)Not measured88.2%Only GLM 4.7 measured
AA Math Index85.395.0GLM 4.7
AIME 2025 (Artificial Analysis run)85.3%95.0%GLM 4.7
OTIS Mock AIMENot measured83.3%Only GLM 4.7 measured
ProofBenchNot measured6.0%Only GLM 4.7 measured
LMArena Text: mathNot measured1424Only GLM 4.7 measured
LMArena Text: mathematical workNot measured1411Only GLM 4.7 measured
Neither model has a result on the other 5 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index05
Knowledge benchmarks for GLM 4.6V and GLM 4.7
BenchmarkGLM 4.6VGLM 4.7Leader
MMLU ProNot measured82.7%Only GLM 4.7 measured
LegalBench (Vals AI)Not measured83.4%Only GLM 4.7 measured
CaseLaw (Vals AI)Not measured54.9%Only GLM 4.7 measured
CorpFin (Vals AI)Not measured46.4%Only GLM 4.7 measured
TaxEval (Vals AI)Not measured68.8%Only GLM 4.7 measured
MedQA (Vals AI)Not measured93.7%Only GLM 4.7 measured
MedCode (Vals AI)Not measured32.8%Only GLM 4.7 measured
MMLU Pro (Artificial Analysis run)79.9%85.6%GLM 4.7
SimpleQA VerifiedNot measured32.2%Only GLM 4.7 measured
LMArena Text: science13891457GLM 4.7
LMArena Text: medicine and healthcare13891469GLM 4.7
LMArena Text: legal and government13931444GLM 4.7
LMArena Text: business and finance13591429GLM 4.7
Neither model has a result on the other 5 boards here
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
Web design benchmarks for GLM 4.6V and GLM 4.7
BenchmarkGLM 4.6VGLM 4.7Leader
Design Arena: websitesNot measured1226Only GLM 4.7 measured
Design Arena: UI componentsNot measured1199Only GLM 4.7 measured
Design Arena: game devNot measured1189Only GLM 4.7 measured
Design Arena: data vizNot measured1195Only GLM 4.7 measured
Design Arena: 3DNot measured1191Only GLM 4.7 measured
Design Arena: all code categoriesNot measured1217Only GLM 4.7 measured
Design Arena: SVGNot measured1143Only GLM 4.7 measured
Design Arena: ASCII artNot measured1165Only GLM 4.7 measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index013
General benchmarks for GLM 4.6V and GLM 4.7
BenchmarkGLM 4.6VGLM 4.7Leader
MedScribe (Vals AI)Not measured68.6%Only GLM 4.7 measured
IFBench30.1%67.9%GLM 4.7
Long context reasoning (AA-LCR)48.7%71.0%GLM 4.7
CL-benchNot measured15.9%Only GLM 4.7 measured
CL-bench LifeNot measured10.9%Only GLM 4.7 measured
LMArena Text13761435GLM 4.7
LMArena Text: instruction following13501412GLM 4.7
LMArena Text: creative writing13471401GLM 4.7
LMArena Vision1164Not measuredOnly GLM 4.6V measured
LMArena Text: English13951453GLM 4.7
LMArena Text: non-English13581417GLM 4.7
LMArena Text: Chinese14251495GLM 4.7
LMArena Text: GermanNot measured1426Only GLM 4.7 measured
LMArena Text: JapaneseNot measured1439Only GLM 4.7 measured
LMArena Text: KoreanNot measured1399Only GLM 4.7 measured
LMArena Text: FrenchNot measured1432Only GLM 4.7 measured
LMArena Text: SpanishNot measured1434Only GLM 4.7 measured
LMArena Text: Russian13391423GLM 4.7
LMArena Text: PolishNot measured1435Only GLM 4.7 measured
LMArena Text: multi-turn13591446GLM 4.7
LMArena Text: longer queries13571432GLM 4.7
LMArena Text: writing and language13471411GLM 4.7
LMArena Text: entertainment and media13431400GLM 4.7
LMArena Vision: English1179Not measuredOnly GLM 4.6V measured
LMArena Vision: Chinese1217Not measuredOnly GLM 4.6V measured
LMArena Vision: reading text1171Not measuredOnly GLM 4.6V measured
LMArena Vision: creative writing1113Not measuredOnly GLM 4.6V measured
Neither model has a result on the other 19 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6V and GLM 4.7
BenchmarkGLM 4.6VGLM 4.7Leader
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 4.6V$0.0008Fits in one request
GLM 4.7$0.0017Fits in one request

GLM 4.6V costs less

Repository review

50K input + 3K output tokens

GLM 4.6V$0.018Fits in one request
GLM 4.7$0.037Fits in one request

GLM 4.6V costs less

Agent loop

200K input + 10K output tokens

GLM 4.6V$0.069Exceeds the context window
GLM 4.7$0.142Exceeds the context window

GLM 4.6V 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 4.6V and GLM 4.7
SpecificationZ.ai GLM 4.6VZ.ai GLM 4.7
MakerZ.aiZ.ai
WeightsOpen weightsOpen weights
ReleasedDecember 8, 2025December 22, 2025
Context window131K205K
Input $/Mtok$0.30$0.60
Output $/Mtok$0.90$2.20
Coding indexNot benchmarked55.1
Independent measurements behind it02
Cost per solved taskNot computable$0.29
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6V better than GLM 4.7 for coding?

Only GLM 4.7 has a coding score, 55.1 on the Vibe Coding Index; GLM 4.6V has none, so the two cannot be ranked on coding. Pick GLM 4.6V for output tokens 2.4x cheaper ($0.90 vs $2.20 per million).

Which is cheaper, GLM 4.6V or GLM 4.7?

GLM 4.6V costs $0.30 per million input tokens and $0.90 per million output tokens; GLM 4.7 costs $0.60 and $2.20. Cost per solved task is known for GLM 4.7 only.

Which has the bigger context window, GLM 4.6V or GLM 4.7?

GLM 4.7, with 205K tokens against 131K.

How many benchmarks is the GLM 4.6V vs GLM 4.7 comparison based on?

31 benchmarks have a result for both models as of October 21, 2026. GLM 4.6V's coding index rests on 0 independent benchmarks and GLM 4.7's on 2. Where only one model has a result, the page says so instead of naming a winner.

Compare next