Noometry

Model comparison

GLM-5.3-Flash vs Trinity Large Thinking

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 38.6 on the Noometry Index.

Last verified . 21 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

51.8

Rank #41 Confirmed

Trinity Large Thinking Arcee AI

38.6

Rank #185 Confirmed

Summary

  • They share 21 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and Trinity Large Thinking in 0 categories; 8 gaps are clear of the uncertainty.
  • The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 16.9.
  • The biggest single-benchmark swing is Surface Evolver Bench: 52.5% for GLM-5.3-Flash and 15.6% for Trinity Large Thinking.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.25 / $0.80 for Trinity Large Thinking.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 262K.

Side by side

GLM-5.3-FlashTrinity Large Thinking
ProviderZ.ai (Zhipu)Arcee AI
Noometry Index51.838.6
Released2026-08-202026-04-01
WeightsOpenOpen
Context window1M262K
Max output131K80K
Input $ / M tokens$0.15$0.25
Output $ / M tokens$0.50$0.80
Results tracked4024

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#31), Trinity Large Thinking: 34.1 (#244)

BenchmarkGLM-5.3-FlashTrinity Large Thinking
LMArena WebDev16091238
SciCode51.6%36.1%
LMArena Coding15081381
DeepSWE63.4%—
FrontierCode31.8%—
CursorBench36.8%—
FrontierSWE18.1%—
ALE-Bench303.55—

Agentic & Tool Use Not comparable

GLM-5.3-Flash: 34.2 (#47), Trinity Large Thinking: —

BenchmarkGLM-5.3-FlashTrinity Large Thinking
APEX-Agents52.8%—
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 48.0 (#42), Trinity Large Thinking: 16.9 (#298)

BenchmarkGLM-5.3-FlashTrinity Large Thinking
CritPt15.4%0.9%
LMArena Hard Prompts14911350
Surface Evolver Bench52.5%15.6%
ARC-AGI-265.8%—
NYT Connections (extended)—16.5%
ARC-AGI-191%—
Chess Puzzles14%—
Thematic Generalization—41.6%
Mystery Game Puzzles8%—
Bench to the Future 30.15—
Epoch Capabilities Index151.88—

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.3 (#47), Trinity Large Thinking: 37.6 (#149)

BenchmarkGLM-5.3-FlashTrinity Large Thinking
LMArena Math15001366
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
OTIS Mock AIME 2024-202593.9%—
ProofBench21%—

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#36), Trinity Large Thinking: 40.9 (#113)

BenchmarkGLM-5.3-FlashTrinity Large Thinking
LMArena Expert15131360
GPQA Diamond90.2%—
Vectara Hallucination Rate—6.9%

Multimodal Not comparable

GLM-5.3-Flash: 42.8 (#27), Trinity Large Thinking: —

BenchmarkGLM-5.3-FlashTrinity Large Thinking
LMArena Vision1296—

Multilingual GLM-5.3-Flash leads

GLM-5.3-Flash: 56.0 (#25), Trinity Large Thinking: 46.2 (#160)

BenchmarkGLM-5.3-FlashTrinity Large Thinking
LMArena Non-English14621325
LMArena Chinese15271373
LMArena French14961374
LMArena German14701356
LMArena Japanese14291311
LMArena Korean14461306
LMArena Russian14691337
LMArena Spanish14711357

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.5 (#20), Trinity Large Thinking: 70.5 (#162)

BenchmarkGLM-5.3-FlashTrinity Large Thinking
LMArena Instruction Following14781334

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.4 (#39), Trinity Large Thinking: 41.3 (#144)

BenchmarkGLM-5.3-FlashTrinity Large Thinking
LMArena Longer Query14821355

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.3 (#50), Trinity Large Thinking: 53.8 (#158)

BenchmarkGLM-5.3-FlashTrinity Large Thinking
LMArena Text14711340
LMArena Creative Writing14421320
LMArena Multi-Turn14671342

Frequently asked questions

Is GLM-5.3-Flash better than Trinity Large Thinking?

GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 38.6 on the Noometry Index.

Which is cheaper, GLM-5.3-Flash or Trinity Large Thinking?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Trinity Large Thinking lists at $0.25 and $0.80.

Is GLM-5.3-Flash or Trinity Large Thinking better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 34.1 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 262K.

How many benchmarks do GLM-5.3-Flash and Trinity Large Thinking share?

21 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Trinity Large Thinking has 24.

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