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GLM 4.5 vs GPT-5.1-Codex

GPT-5.1-Codex leads GLM 4.5 on the Artificial Analysis Intelligence Index, 23.7 to 12.8, and GLM 4.5 costs 4.5x less per output token ($2.20 vs $10.00 per million). Updated October 22, 2026; 19 benchmarks have a result for both.

Replace GLM 4.5 Replace GPT-5.1-Codex
GLM 4.5Z.ai
Coding
No data
Overall
No data
Output $/M
$2.20
3
GPT-5.1-CodexOpenAI
Coding
15.3
Overall
No data
Output $/M
$10.00
16
vs boards won of 19 shared

GLM 4.5 or GPT-5.1-Codex: which to pick

Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding.

Pick GLM 4.5 for output tokens 4.5x cheaper ($2.20 vs $10.00 per million) and work measured by Design Arena: websites or Design Arena: game dev, where it leads.

Model AGLM 4.5Z.aiNot benchmarkedNo coding benchmarkNo cost per solved taskModel BGPT-5.1-CodexOpenAI15.3/1002 independent benchmarks$0.892 per solved task
Shared results
19
GLM 4.5 only
42
GPT-5.1-Codex only
7
Like-for-like categories
8 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 4.5 not benchmarked, GPT-5.1-Codex 15.3 from 2 benchmarks.
Widest category
Agentic work, 40.1 points apart on average over 1 shared benchmark.
Widest single gap
τ²-Bench Telecom: GLM 4.5 43.0% against GPT-5.1-Codex 83.0%, 40.1 points apart on a 0 to 100 scale.
Output price
GLM 4.5 $2.20 against GPT-5.1-Codex $10.00 per million output tokens, 4.5x apart.
Context window
GLM 4.5 takes 131K tokens, GPT-5.1-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 4.5BarsGPT-5.1-CodexTies
Coding05
Intelligence01
Agentic work01
Reasoning02
Math02
Knowledge01
Web design32
General02
All shared boards316
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
Coding benchmarks for GLM 4.5 and GPT-5.1-Codex
BenchmarkGLM 4.5GPT-5.1-CodexLeader
SWE-bench Verified64.2%66.0%GPT-5.1-Codex
LMArena WebDevNot measured1337Only GPT-5.1-Codex measured
LiveCodeBench (Vals AI)67.5%85.5%GPT-5.1-Codex
Vibe Code Bench (Vals AI)Not measured13.1%Only GPT-5.1-Codex measured
LiveCodeBench (Artificial Analysis run)73.8%84.9%GPT-5.1-Codex
Terminal-Bench Hard (Artificial Analysis run)22.0%34.8%GPT-5.1-Codex
Terminal-BenchNot measured57.8%Only GPT-5.1-Codex measured
WeirdML40.6%Not measuredOnly GLM 4.5 measured
ALE-Bench344.81244.9GPT-5.1-Codex
LMArena Text: coding1434Not measuredOnly GLM 4.5 measured
LMArena Text: software and IT1440Not measuredOnly GLM 4.5 measured
LMArena WebDev: HTMLNot measured1346Only GPT-5.1-Codex measured
Neither model has a result on the other 27 boards here
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
Intelligence benchmarks for GLM 4.5 and GPT-5.1-Codex
BenchmarkGLM 4.5GPT-5.1-CodexLeader
AA Intelligence Index12.823.7GPT-5.1-Codex
Neither model has a result on the other 1 board here
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
Agentic work benchmarks for GLM 4.5 and GPT-5.1-Codex
BenchmarkGLM 4.5GPT-5.1-CodexLeader
Design Arena: agents building web appsNot measured1018Only GPT-5.1-Codex measured
Design Arena: agents building full-stack appsNot measured1086Only GPT-5.1-Codex measured
Design Arena: agents building mobile appsNot measured1191Only GPT-5.1-Codex measured
τ²-Bench Telecom43.0%83.0%GPT-5.1-Codex
Neither model has a result on the other 37 boards here
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
Reasoning benchmarks for GLM 4.5 and GPT-5.1-Codex
BenchmarkGLM 4.5GPT-5.1-CodexLeader
GPQA Diamond72.2%Not measuredOnly GLM 4.5 measured
GPQA Diamond (Artificial Analysis run)78.2%86.0%GPT-5.1-Codex
Humanity's Last Exam (Artificial Analysis run)13.0%25.7%GPT-5.1-Codex
LMArena Text: hard prompts1429Not measuredOnly GLM 4.5 measured
LMArena Text: expert1433Not measuredOnly GLM 4.5 measured
LMArena Text: hard prompts in English1429Not measuredOnly GLM 4.5 measured
Kagi LLM Benchmark57.9%Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 15 boards here
MathLike-for-likePublic boards, shown as published, kept out of the index02
Math benchmarks for GLM 4.5 and GPT-5.1-Codex
BenchmarkGLM 4.5GPT-5.1-CodexLeader
AIME86.7%Not measuredOnly GLM 4.5 measured
MATH 500 (Vals AI)94.0%Not measuredOnly GLM 4.5 measured
MGSM (Vals AI)90.8%Not measuredOnly GLM 4.5 measured
AA Math Index73.795.7GPT-5.1-Codex
AIME 2025 (Artificial Analysis run)73.7%95.7%GPT-5.1-Codex
LMArena Text: math1427Not measuredOnly GLM 4.5 measured
LMArena Text: mathematical work1430Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 6 boards here
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index01
Knowledge benchmarks for GLM 4.5 and GPT-5.1-Codex
BenchmarkGLM 4.5GPT-5.1-CodexLeader
MMLU Pro81.2%Not measuredOnly GLM 4.5 measured
LegalBench (Vals AI)75.6%Not measuredOnly GLM 4.5 measured
CorpFin (Vals AI)61.0%Not measuredOnly GLM 4.5 measured
TaxEval (Vals AI)72.4%Not measuredOnly GLM 4.5 measured
MedQA (Vals AI)90.0%Not measuredOnly GLM 4.5 measured
MMLU Pro (Artificial Analysis run)83.5%86.0%GPT-5.1-Codex
LMArena Text: science1444Not measuredOnly GLM 4.5 measured
LMArena Text: medicine and healthcare1455Not measuredOnly GLM 4.5 measured
LMArena Text: legal and government1438Not measuredOnly GLM 4.5 measured
LMArena Text: business and finance1420Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 8 boards here
Web designLike-for-likePublic boards, shown as published, kept out of the index32
Web design benchmarks for GLM 4.5 and GPT-5.1-Codex
BenchmarkGLM 4.5GPT-5.1-CodexLeader
Design Arena: websites11691161GLM 4.5
Design Arena: UI components11411234GPT-5.1-Codex
Design Arena: game dev11441137GLM 4.5
Design Arena: data viz11591213GPT-5.1-Codex
Design Arena: 3D1170Not measuredOnly GLM 4.5 measured
Design Arena: all code categories11651152GLM 4.5
Design Arena: SVG1096Not measuredOnly GLM 4.5 measured
Neither model has a result on the other 2 boards here
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
General benchmarks for GLM 4.5 and GPT-5.1-Codex
BenchmarkGLM 4.5GPT-5.1-CodexLeader
IFBench44.1%70.0%GPT-5.1-Codex
Long context reasoning (AA-LCR)52.7%69.3%GPT-5.1-Codex
Creative writing (Lech Mazur)73.4%Not measuredOnly GLM 4.5 measured
LMArena Text1430Not measuredOnly GLM 4.5 measured
LMArena Text: instruction following1404Not measuredOnly GLM 4.5 measured
LMArena Text: creative writing1395Not measuredOnly GLM 4.5 measured
LMArena Text: English1437Not measuredOnly GLM 4.5 measured
LMArena Text: non-English1417Not measuredOnly GLM 4.5 measured
LMArena Text: Chinese1465Not measuredOnly GLM 4.5 measured
LMArena Text: German1407Not measuredOnly GLM 4.5 measured
LMArena Text: Japanese1415Not measuredOnly GLM 4.5 measured
LMArena Text: Korean1380Not measuredOnly GLM 4.5 measured
LMArena Text: French1418Not measuredOnly GLM 4.5 measured
LMArena Text: Spanish1454Not measuredOnly GLM 4.5 measured
LMArena Text: Russian1413Not measuredOnly GLM 4.5 measured
LMArena Text: Polish1422Not measuredOnly GLM 4.5 measured
LMArena Text: multi-turn1415Not measuredOnly GLM 4.5 measured
LMArena Text: longer queries1412Not measuredOnly GLM 4.5 measured
LMArena Text: writing and language1403Not measuredOnly GLM 4.5 measured
LMArena Text: entertainment and media1404Not measuredOnly GLM 4.5 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 4.5 and GPT-5.1-Codex
BenchmarkGLM 4.5GPT-5.1-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 4.5$0.0017Fits in one request
GPT-5.1-Codex$0.0063Fits in one request

GLM 4.5 costs less

Repository review

50K input + 3K output tokens

GLM 4.5$0.037Fits in one request
GPT-5.1-Codex$0.092Fits in one request

GLM 4.5 costs less

Agent loop

200K input + 10K output tokens

GLM 4.5$0.142Exceeds the context window
GPT-5.1-Codex$0.350Fits in one request

GLM 4.5 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 4.5 and GPT-5.1-Codex
SpecificationZ.ai GLM 4.5OpenAI GPT-5.1-Codex
MakerZ.aiOpenAI
WeightsOpen weightsNo public weights listed
ReleasedJuly 25, 2025November 13, 2025
Context window131K400K
Input $/Mtok$0.60$1.25
Output $/Mtok$2.20$10.00
Coding indexNot benchmarked15.3
Independent measurements behind it02
Cost per solved taskNot computable$0.892
SourceOpenRouter listingOpenRouter listing

Questions

Is GLM 4.5 better than GPT-5.1-Codex for coding?

Only GPT-5.1-Codex has a coding score, 15.3 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding. Pick GLM 4.5 for output tokens 4.5x cheaper ($2.20 vs $10.00 per million) and work measured by Design Arena: websites or Design Arena: game dev, where it leads.

Which is cheaper, GLM 4.5 or GPT-5.1-Codex?

GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; GPT-5.1-Codex costs $1.25 and $10.00. Cost per solved task is known for GPT-5.1-Codex only.

Which has the bigger context window, GLM 4.5 or GPT-5.1-Codex?

GPT-5.1-Codex, with 400K tokens against 131K.

How many benchmarks is the GLM 4.5 vs GPT-5.1-Codex comparison based on?

19 benchmarks have a result for both models as of October 22, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and GPT-5.1-Codex's on 2. Where only one model has a result, the page says so instead of naming a winner.

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