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GLM 5 vs GPT-5.3-Codex

GPT-5.3-Codex leads GLM 5 on coding, 60.0 to 41.7 on the Vibe Coding Index, and GLM 5 costs 4.4x less per output token ($3.20 vs $14.00 per million). Updated October 21, 2026; 30 benchmarks have a result for both.

Replace GLM 5 Replace GPT-5.3-Codex
GLM 5Z.ai
Coding
41.7
Overall
54.3
Output $/M
$3.20
15
GPT-5.3-CodexOpenAI
Coding
60.0
Overall
No data
Output $/M
$14.00
15
vs boards won of 30 shared

GLM 5 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 41.7 for GLM 5.

Pick GLM 5 for output tokens 4.4x cheaper ($3.20 vs $14.00 per million) and work measured by LMArena WebDev or LMArena WebDev: React, where it leads.

Model AGLM 5Z.ai41.7/1003 independent benchmarks$0.63 per solved taskModel BGPT-5.3-CodexOpenAI60.0/1003 independent benchmarks$0.59 per solved task
Shared results
30
GLM 5 only
46
GPT-5.3-Codex only
4
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 5 41.7 from 3 benchmarks, GPT-5.3-Codex 60.0 from 3 benchmarks.
Widest category
Coding, 16.1 points apart on average over 10 shared benchmarks.
Widest single gap
Vibe Code Bench (Vals AI): GLM 5 23.4% against GPT-5.3-Codex 61.8%, 38.4 points apart on a 0 to 100 scale.
Output price
GLM 5 $3.20 against GPT-5.3-Codex $14.00 per million output tokens, 4.4x apart.
Context window
GLM 5 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 5BarsGPT-5.3-CodexTies
Coding37
Intelligence02
Agentic work51
Reasoning02
Web design71
General02
All shared boards1515
CodingLike-for-likeSix of these boards feed the Vibe Coding Index37
Coding benchmarks for GLM 5 and GPT-5.3-Codex
BenchmarkGLM 5GPT-5.3-CodexLeader
SWE-bench Verified72.8%Not measuredOnly GLM 5 measured
SWE-bench Multilingual69.7%Not measuredOnly GLM 5 measured
LMArena WebDev14351407GLM 5
LiveCodeBench (Vals AI)81.9%87.3%GPT-5.3-Codex
SWE-bench Verified (Vals AI)71.4%78.0%GPT-5.3-Codex
Vibe Code Bench (Vals AI)23.4%61.8%GPT-5.3-Codex
IOI (Vals AI)Not measured53.8%Only GPT-5.3-Codex measured
Terminal-Bench Hard (Artificial Analysis run)43.2%53.0%GPT-5.3-Codex
Terminal-Bench52.4%78.4%GPT-5.3-Codex
WeirdML48.2%79.3%GPT-5.3-Codex
ALE-Bench765.61655.2GPT-5.3-Codex
LMArena Text: coding1461Not measuredOnly GLM 5 measured
LMArena Text: software and IT1460Not measuredOnly GLM 5 measured
LMArena WebDev: React14241395GLM 5
LMArena WebDev: HTML14501422GLM 5
LMArena WebDev: from an imageNot measured1443Only GPT-5.3-Codex measured
Neither model has a result on the other 23 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
Intelligence benchmarks for GLM 5 and GPT-5.3-Codex
BenchmarkGLM 5GPT-5.3-CodexLeader
AA Intelligence Index27.932.5GPT-5.3-Codex
Epoch Capabilities Index145.8157.1GPT-5.3-Codex
Agentic workLike-for-likePublic boards, shown as published, kept out of the index51
Agentic work benchmarks for GLM 5 and GPT-5.3-Codex
BenchmarkGLM 5GPT-5.3-CodexLeader
Design Arena: agents building web appsNot measured1032Only GPT-5.3-Codex measured
Design Arena: agents building full-stack apps1123983GLM 5
Design Arena: agents building mobile apps11361054GLM 5
Design Arena: agents building Android apps11461084GLM 5
Design Arena: agents building Godot games11441124GLM 5
Design Arena: HTML slides1148Not measuredOnly GLM 5 measured
τ²-Bench Telecom98.2%86.0%GLM 5
METR time horizonsNot measured74.5%Only GPT-5.3-Codex measured
Vending-Bench 2$4,432$5,940GPT-5.3-Codex
Neither model has a result on the other 32 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for GLM 5 and GPT-5.3-Codex
BenchmarkGLM 5GPT-5.3-CodexLeader
GPQA Diamond83.3%Not measuredOnly GLM 5 measured
GPQA Diamond (Artificial Analysis run)82.0%91.5%GPT-5.3-Codex
Humanity's Last Exam (Artificial Analysis run)29.3%42.5%GPT-5.3-Codex
GPQA Diamond (Epoch AI run)87.8%Not measuredOnly GLM 5 measured
ARC-AGI44.7%Not measuredOnly GLM 5 measured
ARC-AGI-24.9%Not measuredOnly GLM 5 measured
SimpleBench53.2%Not measuredOnly GLM 5 measured
LMArena Text: hard prompts1452Not measuredOnly GLM 5 measured
LMArena Text: expert1454Not measuredOnly GLM 5 measured
LMArena Text: hard prompts in English1460Not measuredOnly GLM 5 measured
Kagi LLM Benchmark75.0%Not measuredOnly GLM 5 measured
Neither model has a result on the other 11 boards here
MathDirectional onlyPublic boards, shown as published, kept out of the index4 boards, none shared
Math benchmarks for GLM 5 and GPT-5.3-Codex
BenchmarkGLM 5GPT-5.3-CodexLeader
AIME91.7%Not measuredOnly GLM 5 measured
OTIS Mock AIME80.0%Not measuredOnly GLM 5 measured
LMArena Text: math1440Not measuredOnly GLM 5 measured
LMArena Text: mathematical work1451Not measuredOnly GLM 5 measured
Neither model has a result on the other 9 boards here
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
Knowledge benchmarks for GLM 5 and GPT-5.3-Codex
BenchmarkGLM 5GPT-5.3-CodexLeader
MMLU Pro86.0%Not measuredOnly GLM 5 measured
LegalBench (Vals AI)84.1%Not measuredOnly GLM 5 measured
CaseLaw (Vals AI)52.5%Not measuredOnly GLM 5 measured
CorpFin (Vals AI)62.9%Not measuredOnly GLM 5 measured
TaxEval (Vals AI)70.0%Not measuredOnly GLM 5 measured
MedQA (Vals AI)94.3%Not measuredOnly GLM 5 measured
LMArena Text: science1460Not measuredOnly GLM 5 measured
LMArena Text: medicine and healthcare1455Not measuredOnly GLM 5 measured
LMArena Text: legal and government1453Not measuredOnly GLM 5 measured
LMArena Text: business and finance1430Not measuredOnly GLM 5 measured
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index71
Web design benchmarks for GLM 5 and GPT-5.3-Codex
BenchmarkGLM 5GPT-5.3-CodexLeader
Design Arena: websites12471163GLM 5
Design Arena: UI components12301137GLM 5
Design Arena: game dev12341161GLM 5
Design Arena: data viz12281164GLM 5
Design Arena: 3D12291008GLM 5
Design Arena: all code categories12461145GLM 5
Design Arena: SVG11681129GLM 5
Design Arena: ASCII art11471152GPT-5.3-Codex
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 5 and GPT-5.3-Codex
BenchmarkGLM 5GPT-5.3-CodexLeader
IFBench72.3%75.4%GPT-5.3-Codex
Long context reasoning (AA-LCR)75.7%83.3%GPT-5.3-Codex
CL-bench18.7%Not measuredOnly GLM 5 measured
LMArena Text1446Not measuredOnly GLM 5 measured
LMArena Text: instruction following1428Not measuredOnly GLM 5 measured
LMArena Text: creative writing1438Not measuredOnly GLM 5 measured
LMArena Text: English1458Not measuredOnly GLM 5 measured
LMArena Text: non-English1430Not measuredOnly GLM 5 measured
LMArena Text: Chinese1511Not measuredOnly GLM 5 measured
LMArena Text: German1447Not measuredOnly GLM 5 measured
LMArena Text: Japanese1416Not measuredOnly GLM 5 measured
LMArena Text: Korean1423Not measuredOnly GLM 5 measured
LMArena Text: French1456Not measuredOnly GLM 5 measured
LMArena Text: Spanish1454Not measuredOnly GLM 5 measured
LMArena Text: Russian1436Not measuredOnly GLM 5 measured
LMArena Text: Polish1434Not measuredOnly GLM 5 measured
LMArena Text: multi-turn1456Not measuredOnly GLM 5 measured
LMArena Text: longer queries1446Not measuredOnly GLM 5 measured
LMArena Text: writing and language1437Not measuredOnly GLM 5 measured
LMArena Text: entertainment and media1426Not measuredOnly GLM 5 measured
Neither model has a result on the other 26 boards here
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
Image generation benchmarks for GLM 5 and GPT-5.3-Codex
BenchmarkGLM 5GPT-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 5$0.0026Fits in one request
GPT-5.3-Codex$0.0088Fits in one request

GLM 5 costs less

Repository review

50K input + 3K output tokens

GLM 5$0.060Fits in one request
GPT-5.3-Codex$0.130Fits in one request

GLM 5 costs less

Agent loop

200K input + 10K output tokens

GLM 5$0.232Exceeds the context window
GPT-5.3-Codex$0.490Fits in one request

GLM 5 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 5 and GPT-5.3-Codex
SpecificationZ.ai GLM 5OpenAI GPT-5.3-Codex
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedFebruary 11, 2026February 24, 2026
Context window205K400K
Input $/Mtok$1.00$1.75
Output $/Mtok$3.20$14.00
Coding index41.760.0
Independent measurements behind it33
Cost per solved task$0.63$0.59
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 5 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 41.7 for GLM 5. Pick GLM 5 for output tokens 4.4x cheaper ($3.20 vs $14.00 per million) and work measured by LMArena WebDev or LMArena WebDev: React, where it leads.

Which is cheaper, GLM 5 or GPT-5.3-Codex?

GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; GPT-5.3-Codex costs $1.75 and $14.00. Per solved SWE-bench Verified task, GLM 5 costs $0.63 and GPT-5.3-Codex $0.59.

Which has the bigger context window, GLM 5 or GPT-5.3-Codex?

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

How many benchmarks is the GLM 5 vs GPT-5.3-Codex comparison based on?

30 benchmarks have a result for both models as of October 21, 2026. GLM 5's coding index rests on 3 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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