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GLM 4.7 vs Grok 4.5

Updated October 21, 2026. 66 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.

GLM 4.7Z.ai
Coding
33.6
Overall
22.2
Output $/M
$1.98
4
Grok 4.5xAI
Coding
75.5
Overall
38.8
Output $/M
$6.00
62
vs boards won of 66 shared

Decision reading

Pick Grok 4.5 for coding: it scores 75.5 on the Vibe Coding Index against 33.6 for GLM 4.7.

Pick GLM 4.7 when output tokens cost 3.0x less, or when a solved task costs $0.29 against $0.62.

Shared results
66
GLM 4.7 only
14
Grok 4.5 only
54
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 4.7 33.6 from 1 benchmark, Grok 4.5 75.5 from 2 benchmarks.
Widest category
Agentic work, 17.1 points apart on average over 5 shared benchmarks.
Widest single gap
ALE-Bench: GLM 4.7 399.5 against Grok 4.5 1309.3, 30.3 points apart on a 0 to 100 scale.
Output price
GLM 4.7 $1.98 against Grok 4.5 $6.00 per million output tokens, 3.0x apart.
Context window
GLM 4.7 takes 205K tokens, Grok 4.5 500K.

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 4.7BarsGrok 4.5Ties
Coding09
Intelligence02
Agentic work05
Reasoning09
Math04
Knowledge19
Web design08
General316
All shared boards462
CodingLike-for-likeFour of these boards feed the Vibe Coding Index09
Coding benchmarks for GLM 4.7 and Grok 4.5
BenchmarkGLM 4.7Grok 4.5Leader
LiveBench codingNot measured94.3%Only Grok 4.5 measured
LMArena WebDev14351553Grok 4.5
AA Coding Index45.372.4Grok 4.5
LiveCodeBench (Vals AI)82.2%87.3%Grok 4.5
SWE-bench Verified (Vals AI)69.4%86.6%Grok 4.5
Terminal-Bench 4 (Vals AI)Not measured8.6%Only Grok 4.5 measured
Vibe Code Bench (Vals AI)Not measured69.0%Only Grok 4.5 measured
Code migration (Vals AI)Not measured36.6%Only Grok 4.5 measured
ProgramBench (Vals AI)Not measured0.0%Only Grok 4.5 measured
IOI (Vals AI)Not measured40.6%Only Grok 4.5 measured
Terminal-Bench 2.1 (Vals AI)Not measured67.8%Only Grok 4.5 measured
SkillsBench (Vals AI)Not measured66.0%Only Grok 4.5 measured
CyberBench (Vals AI)Not measured71.0%Only Grok 4.5 measured
LiveCodeBench (Artificial Analysis run)89.4%Not measuredOnly GLM 4.7 measured
SciCodeNot measured55.0%Only Grok 4.5 measured
Terminal-Bench Hard (Artificial Analysis run)31.8%Not measuredOnly GLM 4.7 measured
Terminal-Bench33.4%Not measuredOnly GLM 4.7 measured
FrontierCodeNot measured42.4%Only Grok 4.5 measured
WeirdMLNot measured46.4%Only Grok 4.5 measured
ALE-Bench399.51309.3Grok 4.5
LMArena Text: coding14541474Grok 4.5
LMArena Text: software and IT14531471Grok 4.5
LMArena WebDev: React14201556Grok 4.5
LMArena WebDev: HTML14471567Grok 4.5
LMArena WebDev: from an imageNot measured1562Only Grok 4.5 measured
Neither model has a result on the other 13 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 4.7 and Grok 4.5
BenchmarkGLM 4.7Grok 4.5Leader
AA Intelligence Index22.238.8Grok 4.5
Epoch Capabilities Index143.6154.1Grok 4.5
Agentic workLike-for-likePublic boards, shown as published, kept out of the index05
Agentic work benchmarks for GLM 4.7 and Grok 4.5
BenchmarkGLM 4.7Grok 4.5Leader
AA Agentic IndexNot measured41.2Only Grok 4.5 measured
Design Arena: agents building web appsNot measured1217Only Grok 4.5 measured
Design Arena: agents building full-stack apps10501249Grok 4.5
Design Arena: agents building mobile apps11051221Grok 4.5
Design Arena: agents building Android apps11181242Grok 4.5
Design Arena: agents building Godot games10581229Grok 4.5
Design Arena: agents building gamesNot measured1235Only Grok 4.5 measured
Design Arena: HTML slidesNot measured1204Only Grok 4.5 measured
Design Arena: PowerPoint slidesNot measured1232Only Grok 4.5 measured
LiveBench researchNot measured85.9%Only Grok 4.5 measured
LiveBench browserNot measured44.0%Only Grok 4.5 measured
LiveBench long agenticNot measured27.9%Only Grok 4.5 measured
SREBench (Vals AI)Not measured0.8%Only Grok 4.5 measured
Finance Agent (Vals AI)Not measured48.4%Only Grok 4.5 measured
Tax Agent Bench (Vals AI)Not measured24.9%Only Grok 4.5 measured
Legal Research Bench (Vals AI)Not measured38.0%Only Grok 4.5 measured
Excel Modeling Benchmark (Vals AI)Not measured52.9%Only Grok 4.5 measured
Time Horizon Index (Vals AI)Not measured6.3%Only Grok 4.5 measured
τ²-Bench Telecom95.9%Not measuredOnly GLM 4.7 measured
APEX-AgentsNot measured56.2%Only Grok 4.5 measured
PostTrainBenchNot measured23.4%Only Grok 4.5 measured
Vending-Bench 2$2,377$3,887Grok 4.5
LMArena SearchNot measured1216Only Grok 4.5 measured
Neither model has a result on the other 16 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index09
Reasoning benchmarks for GLM 4.7 and Grok 4.5
BenchmarkGLM 4.7Grok 4.5Leader
GPQA Diamond80.0%92.9%Grok 4.5
LiveBench reasoningNot measured87.2%Only Grok 4.5 measured
MMMU Pro (Vals AI)Not measured61.8%Only Grok 4.5 measured
GPQA Diamond (Artificial Analysis run)85.9%93.1%Grok 4.5
Humanity's Last Exam (Artificial Analysis run)27.4%42.7%Grok 4.5
GPQA Diamond (Epoch AI run)83.3%93.4%Grok 4.5
ARC-AGINot measured87.2%Only Grok 4.5 measured
ARC-AGI-2Not measured52.6%Only Grok 4.5 measured
SimpleBench47.7%70.0%Grok 4.5
Chess puzzles21.0%35.0%Grok 4.5
LMArena Text: hard prompts14431462Grok 4.5
LMArena Text: expert14241465Grok 4.5
LMArena Text: hard prompts in English14521464Grok 4.5
LMArena Vision: diagramsNot measured1304Only Grok 4.5 measured
LMArena Vision: homeworkNot measured1295Only Grok 4.5 measured
Neither model has a result on the other 6 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index04
Math benchmarks for GLM 4.7 and Grok 4.5
BenchmarkGLM 4.7Grok 4.5Leader
AIME93.3%Not measuredOnly GLM 4.7 measured
MGSM (Vals AI)88.2%Not measuredOnly GLM 4.7 measured
AA Math Index95.0Not measuredOnly GLM 4.7 measured
AIME 2025 (Artificial Analysis run)95.0%Not measuredOnly GLM 4.7 measured
OTIS Mock AIME83.3%97.8%Grok 4.5
FrontierMath, tiers 1 to 3Not measured57.2%Only Grok 4.5 measured
FrontierMath, tier 4Not measured24.4%Only Grok 4.5 measured
ProofBench6.0%31.0%Grok 4.5
LMArena Text: math14241460Grok 4.5
LMArena Text: mathematical work14111482Grok 4.5
Neither model has a result on the other 3 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index19
Knowledge benchmarks for GLM 4.7 and Grok 4.5
BenchmarkGLM 4.7Grok 4.5Leader
MMLU Pro82.7%89.2%Grok 4.5
LegalBench (Vals AI)83.4%86.0%Grok 4.5
CaseLaw (Vals AI)54.9%Not measuredOnly GLM 4.7 measured
CorpFin (Vals AI)46.4%67.4%Grok 4.5
TaxEval (Vals AI)68.8%71.7%Grok 4.5
MortgageTax (Vals AI)Not measured61.8%Only Grok 4.5 measured
MedQA (Vals AI)93.7%Not measuredOnly GLM 4.7 measured
MedCode (Vals AI)32.8%43.3%Grok 4.5
MMLU Pro (Artificial Analysis run)85.6%Not measuredOnly GLM 4.7 measured
SimpleQA Verified32.2%48.3%Grok 4.5
LMArena Text: science14571464Grok 4.5
LMArena Text: medicine and healthcare14691440GLM 4.7
LMArena Text: legal and government14441463Grok 4.5
LMArena Text: business and finance14291441Grok 4.5
Neither model has a result on the other 3 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index08
Web design benchmarks for GLM 4.7 and Grok 4.5
BenchmarkGLM 4.7Grok 4.5Leader
Design Arena: websites12261281Grok 4.5
Design Arena: UI components11991279Grok 4.5
Design Arena: game dev11891263Grok 4.5
Design Arena: data viz11951267Grok 4.5
Design Arena: 3D11911257Grok 4.5
Design Arena: all code categories12171276Grok 4.5
Design Arena: SVG11421215Grok 4.5
Design Arena: ASCII art11651257Grok 4.5
LiveBench designNot measured78.1%Only Grok 4.5 measured
GeneralLike-for-likePublic boards, shown as published, kept out of the index316
General benchmarks for GLM 4.7 and Grok 4.5
BenchmarkGLM 4.7Grok 4.5Leader
LiveBench data analysisNot measured73.0%Only Grok 4.5 measured
SAGE (Vals AI)Not measured35.0%Only Grok 4.5 measured
MedScribe (Vals AI)68.6%86.9%Grok 4.5
Vals Multimodal IndexNot measured63.4%Only Grok 4.5 measured
IFBench67.9%Not measuredOnly GLM 4.7 measured
Long context reasoning (AA-LCR)71.0%79.3%Grok 4.5
DTBenchNot measured96.5%Only Grok 4.5 measured
LMCANot measured45.2%Only Grok 4.5 measured
CL-bench15.9%Not measuredOnly GLM 4.7 measured
Surface Evolver BenchNot measured74.4%Only Grok 4.5 measured
Furniture assemblyNot measured22.5%Only Grok 4.5 measured
CL-bench Life10.9%Not measuredOnly GLM 4.7 measured
LMArena Text14351448Grok 4.5
LMArena Text: instruction following14121445Grok 4.5
LMArena Text: creative writing14011442Grok 4.5
LMArena VisionNot measured1287Only Grok 4.5 measured
LMArena Text: English14531453GLM 4.7
LMArena Text: non-English14171440Grok 4.5
LMArena Text: Chinese14951495Grok 4.5
LMArena Text: German14261448Grok 4.5
LMArena Text: Japanese14391430GLM 4.7
LMArena Text: Korean13991406Grok 4.5
LMArena Text: French14321457Grok 4.5
LMArena Text: Spanish14341451Grok 4.5
LMArena Text: Russian14231449Grok 4.5
LMArena Text: Polish14351432GLM 4.7
LMArena Text: multi-turn14461456Grok 4.5
LMArena Text: longer queries14321462Grok 4.5
LMArena Text: writing and language14111445Grok 4.5
LMArena Text: entertainment and media14001434Grok 4.5
LMArena Vision: EnglishNot measured1285Only Grok 4.5 measured
LMArena Vision: ChineseNot measured1345Only Grok 4.5 measured
LMArena Vision: reading textNot measured1293Only Grok 4.5 measured
LMArena Vision: humorNot measured1276Only Grok 4.5 measured
LMArena Vision: creative writingNot measured1293Only Grok 4.5 measured
LMArena DocumentNot measured1452Only Grok 4.5 measured
Neither model has a result on the other 10 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.7 and Grok 4.5
BenchmarkGLM 4.7Grok 4.5Leader
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 4.7$0.0015Fits in one request
Grok 4.5$0.0050Fits in one request

GLM 4.7 costs less

Repository review

50K input + 3K output tokens

GLM 4.7$0.033Fits in one request
Grok 4.5$0.118Fits in one request

GLM 4.7 costs less

Agent loop

200K input + 10K output tokens

GLM 4.7$0.128Exceeds the context window
Grok 4.5$0.460Fits in one request

GLM 4.7 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 4.7 and Grok 4.5
SpecificationZ.ai GLM 4.7SpaceXAI Grok 4.5
MakerZ.aixAI
WeightsOpen weightsNo public weights listed
ReleasedDecember 22, 2025July 8, 2026
Context window205K500K
Input $/Mtok$0.54$2.00
Output $/Mtok$1.98$6.00
Coding index33.675.5
Independent measurements behind it12
Cost per solved task$0.29$0.62
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.7 better than Grok 4.5 for coding?

Pick Grok 4.5 for coding: it scores 75.5 on the Vibe Coding Index against 33.6 for GLM 4.7. Pick GLM 4.7 when output tokens cost 3.0x less, or when a solved task costs $0.29 against $0.62.

Which is cheaper, GLM 4.7 or Grok 4.5?

GLM 4.7 costs $0.54 per million input tokens and $1.98 per million output tokens; Grok 4.5 costs $2.00 and $6.00. Per solved SWE-bench Verified task, GLM 4.7 costs $0.29 and Grok 4.5 $0.62.

Which has the bigger context window, GLM 4.7 or Grok 4.5?

Grok 4.5, with 500K tokens against 205K.

How many benchmarks is the GLM 4.7 vs Grok 4.5 comparison based on?

66 benchmarks have a result for both models as of October 21, 2026. GLM 4.7's coding index rests on 1 independent benchmark and Grok 4.5's on 2. Where only one model has a result, the page says so instead of naming a winner.

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