Noometry

Model comparison

GLM-5.3-Flash vs GPT-5.3 Codex

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 45.5 on the Noometry Index.

Last verified . 3 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

49.6

Rank #45 Confirmed

GPT-5.3 Codex OpenAI

45.5

Rank #61 Reported

Summary

  • They share 3 benchmarks with published results for both. GLM-5.3-Flash scores higher in 1 category and GPT-5.3 Codex in 1 category; 2 gaps are clear of the uncertainty.
  • The widest gap is in agentic & tool use, where GPT-5.3 Codex leads 47.8 to 34.3.
  • GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Codex.
  • GLM-5.3-Flash accepts more context: 1M tokens versus 400K.
  • GLM-5.3-Flash has downloadable open weights; the other is API-only.

Side by side

GLM-5.3-FlashGPT-5.3 Codex
ProviderZ.ai (Zhipu)OpenAI
Noometry Index49.645.5
Released2026-08-202026-02-05
WeightsOpenProprietary
Context window1M400K
Max output131K128K
Input $ / M tokens$0.15$1.75
Output $ / M tokens$0.50$14
Results tracked368

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.7 (#26), GPT-5.3 Codex: 48.5 (#51)

BenchmarkGLM-5.3-FlashGPT-5.3 Codex
LMArena WebDev16141408
ALE-Bench303.551,655
SWE-bench Verified—74.8%
DeepSWE63.4%—
FrontierCode31.8%—
SciCode51.6%—
WeirdML—79.3%
LMArena Coding1506—

Agentic & Tool Use GPT-5.3 Codex leads

GLM-5.3-Flash: 34.3 (#41), GPT-5.3 Codex: 47.8 (#9)

BenchmarkGLM-5.3-FlashGPT-5.3 Codex
Terminal-Bench—78.4%
APEX-Agents52.8%—
GDP.pdf14%—
METR Time Horizons—74.5%
Vending-Bench 2—5,940

Reasoning Not comparable

GLM-5.3-Flash: 31.7 (#69), GPT-5.3 Codex: —

BenchmarkGLM-5.3-FlashGPT-5.3 Codex
Epoch Capabilities Index151.9156.84
CritPt15.4%—
Chess Puzzles14%—
LMArena Hard Prompts1490—
Mystery Game Puzzles8%—
Surface Evolver Bench52.5%—
Bench to the Future 30.15—

Math Not comparable

GLM-5.3-Flash: 53.1 (#38), GPT-5.3 Codex: —

BenchmarkGLM-5.3-FlashGPT-5.3 Codex
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
OTIS Mock AIME 2024-202593.9%—
ProofBench21%—
LMArena Math1499—

Knowledge Not comparable

GLM-5.3-Flash: 58.4 (#33), GPT-5.3 Codex: —

BenchmarkGLM-5.3-FlashGPT-5.3 Codex
GPQA Diamond90.2%—
LMArena Expert1512—

Multimodal Not comparable

GLM-5.3-Flash: 42.6 (#25), GPT-5.3 Codex: —

BenchmarkGLM-5.3-FlashGPT-5.3 Codex
LMArena Vision1304—

Multilingual Not comparable

GLM-5.3-Flash: 56.2 (#24), GPT-5.3 Codex: —

BenchmarkGLM-5.3-FlashGPT-5.3 Codex
LMArena Non-English1462—
LMArena Chinese1529—
LMArena French1495—
LMArena German1457—
LMArena Japanese1420—
LMArena Korean1443—
LMArena Russian1472—
LMArena Spanish1471—

Instruction Following Not comparable

GLM-5.3-Flash: 77.8 (#17), GPT-5.3 Codex: —

BenchmarkGLM-5.3-FlashGPT-5.3 Codex
LMArena Instruction Following1480—

Long Context Not comparable

GLM-5.3-Flash: 45.3 (#41), GPT-5.3 Codex: —

BenchmarkGLM-5.3-FlashGPT-5.3 Codex
LMArena Longer Query1479—

Writing & Preference Not comparable

GLM-5.3-Flash: 65.4 (#49), GPT-5.3 Codex: —

BenchmarkGLM-5.3-FlashGPT-5.3 Codex
LMArena Text1471—
LMArena Creative Writing1440—
LMArena Multi-Turn1471—

Frequently asked questions

Is GLM-5.3-Flash better than GPT-5.3 Codex?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 45.5 on the Noometry Index.

Which is cheaper, GLM-5.3-Flash or GPT-5.3 Codex?

GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; GPT-5.3 Codex lists at $1.75 and $14.

Is GLM-5.3-Flash or GPT-5.3 Codex better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 48.5 in the Noometry coding category.

Which has the bigger context window?

GLM-5.3-Flash does, with 1M tokens against 400K.

How many benchmarks do GLM-5.3-Flash and GPT-5.3 Codex share?

3 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and GPT-5.3 Codex has 8.

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