Model comparison

GLM-4.7-Flash vs GPT-5.3 Chat

GPT-5.3 Chat is the stronger model overall, scoring 42.8 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 33× less per token, which makes it the better buy when GPT-5.3 Chat's lead doesn't matter for your workload.

Last verified October 10, 2026. 17 shared benchmarks.

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

GPT-5.3 Chat OpenAI

42.8

Rank #109 Confirmed

Summary

They share 17 benchmarks with published results for both. GLM-4.7-Flash scores higher in 0 categories and GPT-5.3 Chat in 8 categories; 7 gaps are clear of the uncertainty.
The widest gap is in writing & preference, where GPT-5.3 Chat leads 63.1 to 47.4.
GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $1.75 / $14 for GPT-5.3 Chat.
GLM-4.7-Flash accepts more context: 200K tokens versus 128K.
GLM-4.7-Flash has downloadable open weights; the other is API-only.

Side by side

GLM-4.7-Flash and GPT-5.3 Chat specifications
	GLM-4.7-Flash	GPT-5.3 Chat
Provider	Z.ai (Zhipu)	OpenAI
Noometry Index	38.8	42.8
Released	2026-01-19	2026-03-03
Weights	Open	Proprietary
Context window	200K	128K
Max output	131K	16K
Input $ / M tokens	$0.06	$1.75
Output $ / M tokens	$0.40	$14
Results tracked	21	18

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Category by category

Coding Too close to call

GLM-4.7-Flash: 40.6 (#135), GPT-5.3 Chat: 41.4 (#124)

Coding benchmarks
Benchmark	GLM-4.7-Flash	GPT-5.3 Chat
LMArena Coding	1383	1408

Reasoning GPT-5.3 Chat leads

GLM-4.7-Flash: 20.9 (#229), GPT-5.3 Chat: 28.5 (#102)

Reasoning benchmarks
Benchmark	GLM-4.7-Flash	GPT-5.3 Chat
LMArena Hard Prompts	1356	1399
Chess Puzzles	0%	—

Math GPT-5.3 Chat leads

GLM-4.7-Flash: 36.1 (#173), GPT-5.3 Chat: 38.2 (#142)

Math benchmarks
Benchmark	GLM-4.7-Flash	GPT-5.3 Chat
LMArena Math	1355	1389
OTIS Mock AIME 2024-2025	58.3%	—

Knowledge GPT-5.3 Chat leads

GLM-4.7-Flash: 35.5 (#184), GPT-5.3 Chat: 38.8 (#140)

Knowledge benchmarks
Benchmark	GLM-4.7-Flash	GPT-5.3 Chat
LMArena Expert	1357	1397
GPQA Diamond	60.5%	—
Vectara Hallucination Rate	9.3%	—

Multilingual GPT-5.3 Chat leads

GLM-4.7-Flash: 46.5 (#158), GPT-5.3 Chat: 50.3 (#124)

Multilingual benchmarks
Benchmark	GLM-4.7-Flash	GPT-5.3 Chat
LMArena Non-English	1330	1382
LMArena Chinese	1403	1432
LMArena French	1332	1397
LMArena German	1337	1384
LMArena Korean	1283	1346
LMArena Russian	1332	1400
LMArena Spanish	1350	1371
LMArena Japanese	—	1352

Instruction Following GPT-5.3 Chat leads

GLM-4.7-Flash: 70.1 (#167), GPT-5.3 Chat: 72.8 (#129)

Instruction Following benchmarks
Benchmark	GLM-4.7-Flash	GPT-5.3 Chat
LMArena Instruction Following	1327	1378

Long Context GPT-5.3 Chat leads

GLM-4.7-Flash: 40.9 (#148), GPT-5.3 Chat: 42.6 (#120)

Long Context benchmarks
Benchmark	GLM-4.7-Flash	GPT-5.3 Chat
LMArena Longer Query	1345	1396

Writing & Preference GPT-5.3 Chat leads

GLM-4.7-Flash: 47.4 (#210), GPT-5.3 Chat: 63.1 (#68)

Writing & Preference benchmarks
Benchmark	GLM-4.7-Flash	GPT-5.3 Chat
LMArena Text	1351	1389
LMArena Creative Writing	1297	1355
EQ-Bench Creative Writing	1125	1690
LMArena Multi-Turn	1342	1412

Frequently asked questions

Is GLM-4.7-Flash better than GPT-5.3 Chat?

Which is cheaper, GLM-4.7-Flash or GPT-5.3 Chat?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; GPT-5.3 Chat lists at $1.75 and $14.

Is GLM-4.7-Flash or GPT-5.3 Chat better for coding?

They score almost the same on coding (40.6 vs 41.4); test both on your own repository before choosing.

Which has the bigger context window?

GLM-4.7-Flash does, with 200K tokens against 128K.

How many benchmarks do GLM-4.7-Flash and GPT-5.3 Chat share?

17 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GPT-5.3 Chat has 18.