Noometry

Model comparison

GLM-5.3-Flash vs GPT-4.1 nano

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 27.7 on the Noometry Index.

Last verified . 20 shared benchmarks.

GLM-5.3-Flash Z.ai (Zhipu)

49.6

Rank #45 Confirmed

GPT-4.1 nano OpenAI

27.7

Rank #322 Confirmed

Summary

  • They share 20 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and GPT-4.1 nano in 0 categories; 10 gaps are clear of the uncertainty.
  • The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 21.8.
  • The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 28.9% for GPT-4.1 nano.
  • GPT-4.1 nano is cheaper at $0.10 / $0.40 per million input/output tokens, against $0.15 / $0.50 for GLM-5.3-Flash.
  • GPT-4.1 nano accepts more context: 1.05M tokens versus 1M.
  • GLM-5.3-Flash has downloadable open weights; the other is API-only.

Side by side

GLM-5.3-FlashGPT-4.1 nano
ProviderZ.ai (Zhipu)OpenAI
Noometry Index49.627.7
Released2026-08-202025-04-14
WeightsOpenProprietary
Context window1M1.05M
Max output131K33K
Input $ / M tokens$0.15$0.10
Output $ / M tokens$0.50$0.40
Results tracked3638

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Category by category

Coding GLM-5.3-Flash leads

GLM-5.3-Flash: 53.7 (#26), GPT-4.1 nano: 23.8 (#325)

BenchmarkGLM-5.3-FlashGPT-4.1 nano
SciCode51.6%25.9%
LMArena Coding15061306
DeepSWE63.4%—
FrontierCode31.8%—
Aider Polyglot—8.9%
LMArena WebDev1614—
WeirdML—19%
ALE-Bench303.55—

Agentic & Tool Use GLM-5.3-Flash leads

GLM-5.3-Flash: 34.3 (#41), GPT-4.1 nano: 26.3 (#98)

BenchmarkGLM-5.3-FlashGPT-4.1 nano
APEX-Agents52.8%—
Berkeley Function Calling Leaderboard—33%
GDP.pdf14%—

Reasoning GLM-5.3-Flash leads

GLM-5.3-Flash: 31.7 (#69), GPT-4.1 nano: 8.6 (#344)

BenchmarkGLM-5.3-FlashGPT-4.1 nano
CritPt15.4%0%
LMArena Hard Prompts14901286
Epoch Capabilities Index151.9129.62
ARC-AGI-2—0%
Kagi LLM Benchmark—33.3%
ARC-AGI-1—0%
Chess Puzzles14%—
Mystery Game Puzzles8%—
DTBench—52.5%
LMCA—5.5%
Surface Evolver Bench52.5%—
Bench to the Future 30.15—

Math GLM-5.3-Flash leads

GLM-5.3-Flash: 53.1 (#38), GPT-4.1 nano: 25.5 (#246)

BenchmarkGLM-5.3-FlashGPT-4.1 nano
OTIS Mock AIME 2024-202593.9%28.9%
LMArena Math14991274
FrontierMath (Tiers 1-3)55.8%—
FrontierMath Tier 417.1%—
ProofBench21%—
Omni-MATH—36.7%
MATH Level 5—70%
FrontierMath (Feb 2025 set)—1%

Knowledge GLM-5.3-Flash leads

GLM-5.3-Flash: 58.4 (#33), GPT-4.1 nano: 21.8 (#268)

BenchmarkGLM-5.3-FlashGPT-4.1 nano
GPQA Diamond90.2%48.9%
LMArena Expert15121272
SimpleQA Verified—6%
MMLU-Pro—55%
GPQA (HELM)—50.7%

Multimodal GLM-5.3-Flash leads

GLM-5.3-Flash: 42.6 (#25), GPT-4.1 nano: 28.6 (#108)

BenchmarkGLM-5.3-FlashGPT-4.1 nano
LMArena Vision13041063

Multilingual GLM-5.3-Flash leads

GLM-5.3-Flash: 56.2 (#24), GPT-4.1 nano: 41.8 (#200)

BenchmarkGLM-5.3-FlashGPT-4.1 nano
LMArena Non-English14621260
LMArena Chinese15291270
LMArena German14571288
LMArena Japanese14201199
LMArena Russian14721261
LMArena French1495—
LMArena Korean1443—
LMArena Spanish1471—

Instruction Following GLM-5.3-Flash leads

GLM-5.3-Flash: 77.8 (#17), GPT-4.1 nano: 68.0 (#188)

BenchmarkGLM-5.3-FlashGPT-4.1 nano
LMArena Instruction Following14801267
IFEval—84.3%

Long Context GLM-5.3-Flash leads

GLM-5.3-Flash: 45.3 (#41), GPT-4.1 nano: 23.7 (#291)

BenchmarkGLM-5.3-FlashGPT-4.1 nano
LMArena Longer Query14791283
Fiction.LiveBench—25%

Writing & Preference GLM-5.3-Flash leads

GLM-5.3-Flash: 65.4 (#49), GPT-4.1 nano: 40.7 (#238)

BenchmarkGLM-5.3-FlashGPT-4.1 nano
LMArena Text14711285
LMArena Creative Writing14401260
LMArena Multi-Turn14711277
EQ-Bench Creative Writing—946
WildBench—81.2%

Frequently asked questions

Is GLM-5.3-Flash better than GPT-4.1 nano?

GLM-5.3-Flash is the stronger model overall, scoring 49.6 to 27.7 on the Noometry Index.

Which is cheaper, GLM-5.3-Flash or GPT-4.1 nano?

GPT-4.1 nano is cheaper. It lists at $0.10 per million input tokens and $0.40 per million output tokens; GLM-5.3-Flash lists at $0.15 and $0.50.

Is GLM-5.3-Flash or GPT-4.1 nano better for coding?

GLM-5.3-Flash scores higher on coding benchmarks: 53.7 versus 23.8 in the Noometry coding category.

Which has the bigger context window?

GPT-4.1 nano does, with 1.05M tokens against 1M.

How many benchmarks do GLM-5.3-Flash and GPT-4.1 nano share?

20 benchmarks have published results for both models. GLM-5.3-Flash has 36 scored results on Noometry and GPT-4.1 nano has 38.

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