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GLM 4.6 vs GPT-5.3-Codex

GPT-5.3-Codex leads GLM 4.6 on coding, 60.0 to 8.8 on the Vibe Coding Index, and GLM 4.6 costs 6.4x less per output token ($2.20 vs $14.00 per million). Updated October 22, 2026; 24 benchmarks have a result for both.

Replace GLM 4.6 Replace GPT-5.3-Codex
GLM 4.6Z.ai
Coding
8.8
Overall
33.4
Output $/M
$2.20
7
GPT-5.3-CodexOpenAI
Coding
60.0
Overall
No data
Output $/M
$14.00
16
vs boards won of 24 shared, 1 tied

GLM 4.6 or GPT-5.3-Codex: which to pick

Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 8.8 for GLM 4.6.

Pick GLM 4.6 for output tokens 6.4x cheaper ($2.20 vs $14.00 per million) and work measured by Design Arena: agents building full-stack apps or Design Arena: agents building mobile apps, where it leads.

Model AGLM 4.6Z.ai8.8/1002 independent benchmarksNo cost per solved taskModel BGPT-5.3-CodexOpenAI60.0/1003 independent benchmarks$0.59 per solved task
Shared results
24
GLM 4.6 only
45
GPT-5.3-Codex only
10
Like-for-like categories
6 of 9

Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.

Which one for your work

Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.

At a glance

Coding index
GLM 4.6 8.8 from 2 benchmarks, GPT-5.3-Codex 60.0 from 3 benchmarks.
Widest category
General, 30.7 points apart on average over 2 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): GLM 4.6 3.1% against GPT-5.3-Codex 61.8%, 58.7 points apart on a 0 to 100 scale.
Output price
GLM 4.6 $2.20 against GPT-5.3-Codex $14.00 per million output tokens, 6.4x apart.
Context window
GLM 4.6 takes 205K tokens, GPT-5.3-Codex 400K.

Benchmark by benchmark

First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.

Boards won, where both have a result
CategoryGLM 4.6BarsGPT-5.3-CodexTies
Coding07
Intelligence01
Agentic work311 tie
Reasoning02
Web design43
General02
All shared boards7161 tie
CodingLike-for-likeSix of these boards feed the Vibe Coding Index07
Coding benchmarks for GLM 4.6 and GPT-5.3-Codex
BenchmarkGLM 4.6GPT-5.3-CodexLeader
SWE-bench Verified68.2%Not measuredOnly GLM 4.6 measured
LMArena WebDev13411407GPT-5.3-Codex
AA Coding Index45.8Not measuredOnly GLM 4.6 measured
LiveCodeBench (Vals AI)81.0%87.3%GPT-5.3-Codex
SWE-bench Verified (Vals AI)Not measured78.0%Only GPT-5.3-Codex measured
Vibe Code Bench (Vals AI)3.1%61.8%GPT-5.3-Codex
IOI (Vals AI)Not measured53.8%Only GPT-5.3-Codex measured
LiveCodeBench (Artificial Analysis run)69.5%Not measuredOnly GLM 4.6 measured
Terminal-Bench Hard (Artificial Analysis run)28.8%53.0%GPT-5.3-Codex
Terminal-Bench24.5%78.4%GPT-5.3-Codex
WeirdMLNot measured79.3%Only GPT-5.3-Codex measured
ALE-Bench340.81655.2GPT-5.3-Codex
LMArena Text: coding1449Not measuredOnly GLM 4.6 measured
LMArena Text: software and IT1455Not measuredOnly GLM 4.6 measured
LMArena WebDev: ReactNot measured1395Only GPT-5.3-Codex measured
LMArena WebDev: HTML13501422GPT-5.3-Codex
LMArena WebDev: from an imageNot measured1443Only GPT-5.3-Codex measured
Neither model has a result on the other 22 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.6 and GPT-5.3-Codex
BenchmarkGLM 4.6GPT-5.3-CodexLeader
AA Intelligence Index18.532.5GPT-5.3-Codex
Epoch Capabilities IndexNot measured157.1Only GPT-5.3-Codex measured
Agentic workLike-for-likePublic boards, shown as published, kept out of the index31
Agentic work benchmarks for GLM 4.6 and GPT-5.3-Codex
BenchmarkGLM 4.6GPT-5.3-CodexLeader
Design Arena: agents building web appsNot measured1032Only GPT-5.3-Codex measured
Design Arena: agents building full-stack apps1029983GLM 4.6
Design Arena: agents building mobile apps10961053GLM 4.6
Design Arena: agents building Android apps10841084Tie
Design Arena: agents building Godot games11801124GLM 4.6
τ²-Bench Telecom76.9%86.0%GPT-5.3-Codex
METR time horizonsNot measured74.5%Only GPT-5.3-Codex measured
Vending-Bench 2Not measured$5,940Only GPT-5.3-Codex measured
Berkeley Function Calling Leaderboard (BFCL)72.4%Not measuredOnly GLM 4.6 measured
BrowseComp-Plus48.2%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 31 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for GLM 4.6 and GPT-5.3-Codex
BenchmarkGLM 4.6GPT-5.3-CodexLeader
GPQA Diamond74.5%Not measuredOnly GLM 4.6 measured
GPQA Diamond (Artificial Analysis run)78.0%91.5%GPT-5.3-Codex
Humanity's Last Exam (Artificial Analysis run)14.5%42.5%GPT-5.3-Codex
LMArena Text: hard prompts1440Not measuredOnly GLM 4.6 measured
LMArena Text: expert1432Not measuredOnly GLM 4.6 measured
LMArena Text: hard prompts in English1445Not measuredOnly GLM 4.6 measured
Kagi LLM Benchmark45.7%Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 15 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
Math benchmarks for GLM 4.6 and GPT-5.3-Codex
BenchmarkGLM 4.6GPT-5.3-CodexLeader
AIME92.7%Not measuredOnly GLM 4.6 measured
MGSM (Vals AI)89.8%Not measuredOnly GLM 4.6 measured
AA Math Index86.0Not measuredOnly GLM 4.6 measured
AIME 2025 (Artificial Analysis run)86.0%Not measuredOnly GLM 4.6 measured
LMArena Text: math1432Not measuredOnly GLM 4.6 measured
LMArena Text: mathematical work1447Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 7 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Knowledge benchmarks for GLM 4.6 and GPT-5.3-Codex
BenchmarkGLM 4.6GPT-5.3-CodexLeader
MMLU Pro82.2%Not measuredOnly GLM 4.6 measured
LegalBench (Vals AI)79.6%Not measuredOnly GLM 4.6 measured
CorpFin (Vals AI)56.8%Not measuredOnly GLM 4.6 measured
TaxEval (Vals AI)66.2%Not measuredOnly GLM 4.6 measured
MedQA (Vals AI)92.2%Not measuredOnly GLM 4.6 measured
MMLU Pro (Artificial Analysis run)82.9%Not measuredOnly GLM 4.6 measured
LMArena Text: science1456Not measuredOnly GLM 4.6 measured
LMArena Text: medicine and healthcare1455Not measuredOnly GLM 4.6 measured
LMArena Text: legal and government1448Not measuredOnly GLM 4.6 measured
LMArena Text: business and finance1416Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index43
Web design benchmarks for GLM 4.6 and GPT-5.3-Codex
BenchmarkGLM 4.6GPT-5.3-CodexLeader
Design Arena: websites11741162GLM 4.6
Design Arena: UI components11451134GLM 4.6
Design Arena: game dev11501161GPT-5.3-Codex
Design Arena: data viz11611164GPT-5.3-Codex
Design Arena: 3D11281007GLM 4.6
Design Arena: all code categories11651144GLM 4.6
Design Arena: SVG11081129GPT-5.3-Codex
Design Arena: ASCII artNot measured1152Only GPT-5.3-Codex measured
Neither model has a result on the other 1 board here
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
General benchmarks for GLM 4.6 and GPT-5.3-Codex
BenchmarkGLM 4.6GPT-5.3-CodexLeader
IFBench43.4%75.4%GPT-5.3-Codex
Long context reasoning (AA-LCR)54.0%83.3%GPT-5.3-Codex
LMArena Text1440Not measuredOnly GLM 4.6 measured
LMArena Text: instruction following1410Not measuredOnly GLM 4.6 measured
LMArena Text: creative writing1412Not measuredOnly GLM 4.6 measured
LMArena Text: English1449Not measuredOnly GLM 4.6 measured
LMArena Text: non-English1426Not measuredOnly GLM 4.6 measured
LMArena Text: Chinese1499Not measuredOnly GLM 4.6 measured
LMArena Text: German1447Not measuredOnly GLM 4.6 measured
LMArena Text: Japanese1393Not measuredOnly GLM 4.6 measured
LMArena Text: Korean1400Not measuredOnly GLM 4.6 measured
LMArena Text: French1459Not measuredOnly GLM 4.6 measured
LMArena Text: Spanish1436Not measuredOnly GLM 4.6 measured
LMArena Text: Russian1418Not measuredOnly GLM 4.6 measured
LMArena Text: Polish1447Not measuredOnly GLM 4.6 measured
LMArena Text: multi-turn1427Not measuredOnly GLM 4.6 measured
LMArena Text: longer queries1422Not measuredOnly GLM 4.6 measured
LMArena Text: writing and language1415Not measuredOnly GLM 4.6 measured
LMArena Text: entertainment and media1409Not measuredOnly GLM 4.6 measured
Neither model has a result on the other 27 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 4.6 and GPT-5.3-Codex
BenchmarkGLM 4.6GPT-5.3-CodexLeader
Neither model has a result on 5 boards here

What each workload costs

Three fixed token mixes at each model's listed price, so per-token rates become a bill.

Chat turn

1K input + 500 output tokens

GLM 4.6$0.0017Fits in one request
GPT-5.3-Codex$0.0088Fits in one request

GLM 4.6 costs less

Repository review

50K input + 3K output tokens

GLM 4.6$0.037Fits in one request
GPT-5.3-Codex$0.130Fits in one request

GLM 4.6 costs less

Agent loop

200K input + 10K output tokens

GLM 4.6$0.142Exceeds the context window
GPT-5.3-Codex$0.490Fits in one request

GLM 4.6 costs less

Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.

Specification differences

The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.

Specifications of GLM 4.6 and GPT-5.3-Codex
SpecificationZ.ai GLM 4.6OpenAI GPT-5.3-Codex
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedSeptember 30, 2025February 24, 2026
Context window205K400K
Input $/Mtok$0.60$1.75
Output $/Mtok$2.20$14.00
Coding index8.860.0
Independent measurements behind it23
Cost per solved taskNot computable$0.59
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.6 better than GPT-5.3-Codex for coding?

Pick GPT-5.3-Codex for coding: it scores 60.0 on the Vibe Coding Index against 8.8 for GLM 4.6. Pick GLM 4.6 for output tokens 6.4x cheaper ($2.20 vs $14.00 per million) and work measured by Design Arena: agents building full-stack apps or Design Arena: agents building mobile apps, where it leads.

Which is cheaper, GLM 4.6 or GPT-5.3-Codex?

GLM 4.6 costs $0.60 per million input tokens and $2.20 per million output tokens; GPT-5.3-Codex costs $1.75 and $14.00. Cost per solved task is known for GPT-5.3-Codex only.

Which has the bigger context window, GLM 4.6 or GPT-5.3-Codex?

GPT-5.3-Codex, with 400K tokens against 205K.

How many benchmarks is the GLM 4.6 vs GPT-5.3-Codex comparison based on?

24 benchmarks have a result for both models as of October 22, 2026. GLM 4.6's coding index rests on 2 independent benchmarks and GPT-5.3-Codex's on 3. Where only one model has a result, the page says so instead of naming a winner.

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