Noometry

Model comparison

GLM-4.7-Flash vs GPT-5.2 Codex

GPT-5.2 Codex is the stronger model overall, scoring 42.6 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 33× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.

Last verified . 0 shared benchmarks.

GLM-4.7-Flash Z.ai (Zhipu)

38.8

Rank #180 Confirmed

GPT-5.2 Codex OpenAI

42.6

Rank #111 Reported

Summary

  • The widest gap is in coding, where GPT-5.2 Codex leads 45.5 to 40.6.
  • GLM-4.7-Flash is cheaper at $0.06 / $0.40 per million input/output tokens, against $1.75 / $14 for GPT-5.2 Codex.
  • GPT-5.2 Codex accepts more context: 400K tokens versus 200K.
  • GLM-4.7-Flash has downloadable open weights; the other is API-only.

Side by side

GLM-4.7-FlashGPT-5.2 Codex
ProviderZ.ai (Zhipu)OpenAI
Noometry Index38.842.6
Released2026-01-192025-12-18
WeightsOpenProprietary
Context window200K400K
Max output131K128K
Input $ / M tokens$0.06$1.75
Output $ / M tokens$0.40$14
Results tracked215

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Category by category

Coding GPT-5.2 Codex leads

GLM-4.7-Flash: 40.6 (#135), GPT-5.2 Codex: 45.5 (#71)

BenchmarkGLM-4.7-FlashGPT-5.2 Codex
SWE-bench Verified (bash only)—72.8%
LMArena WebDev—1339
SWE-bench Multilingual—66.3%
LMArena Coding1383—
ALE-Bench—1,300

Agentic & Tool Use Not comparable

GLM-4.7-Flash: —, GPT-5.2 Codex: 41.0 (#22)

BenchmarkGLM-4.7-FlashGPT-5.2 Codex
Terminal-Bench—66.5%

Reasoning Not comparable

GLM-4.7-Flash: 20.9 (#229), GPT-5.2 Codex: —

BenchmarkGLM-4.7-FlashGPT-5.2 Codex
Chess Puzzles0%—
LMArena Hard Prompts1356—

Math Not comparable

GLM-4.7-Flash: 36.1 (#173), GPT-5.2 Codex: —

BenchmarkGLM-4.7-FlashGPT-5.2 Codex
OTIS Mock AIME 2024-202558.3%—
LMArena Math1355—

Knowledge Not comparable

GLM-4.7-Flash: 35.5 (#184), GPT-5.2 Codex: —

BenchmarkGLM-4.7-FlashGPT-5.2 Codex
GPQA Diamond60.5%—
Vectara Hallucination Rate9.3%—
LMArena Expert1357—

Multilingual Not comparable

GLM-4.7-Flash: 46.5 (#158), GPT-5.2 Codex: —

BenchmarkGLM-4.7-FlashGPT-5.2 Codex
LMArena Non-English1330—
LMArena Chinese1403—
LMArena French1332—
LMArena German1337—
LMArena Korean1283—
LMArena Russian1332—
LMArena Spanish1350—

Instruction Following Not comparable

GLM-4.7-Flash: 70.1 (#167), GPT-5.2 Codex: —

BenchmarkGLM-4.7-FlashGPT-5.2 Codex
LMArena Instruction Following1327—

Long Context Not comparable

GLM-4.7-Flash: 40.9 (#148), GPT-5.2 Codex: —

BenchmarkGLM-4.7-FlashGPT-5.2 Codex
LMArena Longer Query1345—

Writing & Preference Not comparable

GLM-4.7-Flash: 47.4 (#210), GPT-5.2 Codex: —

BenchmarkGLM-4.7-FlashGPT-5.2 Codex
LMArena Text1351—
LMArena Creative Writing1297—
EQ-Bench Creative Writing1125—
LMArena Multi-Turn1342—

Frequently asked questions

Is GLM-4.7-Flash better than GPT-5.2 Codex?

GPT-5.2 Codex is the stronger model overall, scoring 42.6 to 38.8 on the Noometry Index. GLM-4.7-Flash costs 33× less per token, which makes it the better buy when GPT-5.2 Codex's lead doesn't matter for your workload.

Which is cheaper, GLM-4.7-Flash or GPT-5.2 Codex?

GLM-4.7-Flash is cheaper. It lists at $0.06 per million input tokens and $0.40 per million output tokens; GPT-5.2 Codex lists at $1.75 and $14.

Is GLM-4.7-Flash or GPT-5.2 Codex better for coding?

GPT-5.2 Codex scores higher on coding benchmarks: 45.5 versus 40.6 in the Noometry coding category.

Which has the bigger context window?

GPT-5.2 Codex does, with 400K tokens against 200K.

How many benchmarks do GLM-4.7-Flash and GPT-5.2 Codex share?

0 benchmarks have published results for both models. GLM-4.7-Flash has 21 scored results on Noometry and GPT-5.2 Codex has 5.

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