Noometry

Model comparison

GLM-4.5V vs Inkling

Inkling is the stronger model overall, scoring 44.1 to 39.8 on the Noometry Index. GLM-4.5V costs 2.9× less per token, which makes it the better buy when Inkling's lead doesn't matter for your workload.

Last verified . 13 shared benchmarks.

GLM-4.5V Z.ai (Zhipu)

39.8

Rank #158 Confirmed

Inkling Thinking Machines Lab

44.1

Rank #80 Confirmed

Summary

  • They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 2 categories and Inkling in 6 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where Inkling leads 55.1 to 37.5.
  • GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $1.87 / $4.68 for Inkling.
  • Inkling accepts more context: 66K tokens versus 64K.

Side by side

GLM-4.5VInkling
ProviderZ.ai (Zhipu)Thinking Machines Lab
Noometry Index39.844.1
Released2025-08-112026-07-15
WeightsOpenOpen
Context window64K66K
Max output16K66K
Input $ / M tokens$0.60$1.87
Output $ / M tokens$1.80$4.68
Results tracked1541

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Category by category

Coding GLM-4.5V leads

GLM-4.5V: 39.5 (#155), Inkling: 34.5 (#234)

BenchmarkGLM-4.5VInkling
LMArena Coding13471464
FrontierCode—14%
LMArena WebDev—1413
FrontierSWE—4.1%
SciCode—47%
WeirdML—32.3%
ALE-Bench—946

Agentic & Tool Use Not comparable

GLM-4.5V: —, Inkling: 29.6 (#85)

BenchmarkGLM-4.5VInkling
APEX-Agents—33.8%
τ²-bench Banking—25%

Reasoning Inkling leads

GLM-4.5V: 27.4 (#119), Inkling: 40.4 (#56)

BenchmarkGLM-4.5VInkling
LMArena Hard Prompts13341451
ARC-AGI-2—36.5%
SimpleBench—50%
Kagi LLM Benchmark59.8%—
ARC-AGI-1—79.5%
CritPt—5.4%
Chess Puzzles—21%
DTBench—87.5%
LMCA—37.6%
Epoch Capabilities Index—148.54

Math GLM-4.5V leads

GLM-4.5V: 37.4 (#159), Inkling: 31.3 (#225)

BenchmarkGLM-4.5VInkling
LMArena Math13541479
FrontierMath (Tiers 1-3)—33.3%
FrontierMath Tier 4—4.9%
OTIS Mock AIME 2024-2025—88.9%
ProofBench—0%

Knowledge Inkling leads

GLM-4.5V: 37.5 (#156), Inkling: 55.1 (#49)

BenchmarkGLM-4.5VInkling
LMArena Expert13531465
GPQA Diamond—88.3%
SimpleQA Verified—40.3%

Multimodal Not comparable

GLM-4.5V: 34.3 (#92), Inkling: —

BenchmarkGLM-4.5VInkling
LMArena Vision1154—

Multilingual Inkling leads

GLM-4.5V: 44.6 (#177), Inkling: 54.0 (#52)

BenchmarkGLM-4.5VInkling
LMArena Non-English13031434
LMArena Chinese13371490
LMArena Russian12981429
LMArena Spanish13361448
LMArena French—1458
LMArena German—1446
LMArena Japanese—1429
LMArena Korean—1404

Instruction Following Inkling leads

GLM-4.5V: 69.2 (#175), Inkling: 75.1 (#71)

BenchmarkGLM-4.5VInkling
LMArena Instruction Following13111426

Long Context Inkling leads

GLM-4.5V: 39.6 (#171), Inkling: 43.8 (#86)

BenchmarkGLM-4.5VInkling
LMArena Longer Query13041434

Writing & Preference Inkling leads

GLM-4.5V: 52.5 (#170), Inkling: 65.2 (#51)

BenchmarkGLM-4.5VInkling
LMArena Text13331441
LMArena Creative Writing12951387
LMArena Multi-Turn13321436
EQ-Bench Creative Writing—1611
EQ-Bench 4—1226

Frequently asked questions

Is GLM-4.5V better than Inkling?

Inkling is the stronger model overall, scoring 44.1 to 39.8 on the Noometry Index. GLM-4.5V costs 2.9× less per token, which makes it the better buy when Inkling's lead doesn't matter for your workload.

Which is cheaper, GLM-4.5V or Inkling?

GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Inkling lists at $1.87 and $4.68.

Is GLM-4.5V or Inkling better for coding?

GLM-4.5V scores higher on coding benchmarks: 39.5 versus 34.5 in the Noometry coding category.

Which has the bigger context window?

Inkling does, with 66K tokens against 64K.

How many benchmarks do GLM-4.5V and Inkling share?

13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Inkling has 41.

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