GLM 5 vs GPT-5.2-Codex
Updated October 21, 2026. 21 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 5 for coding: it scores 59.7 on the Vibe Coding Index against 55.1 for GPT-5.2-Codex.
Pick GPT-5.2-Codex when a solved task costs $0.43 against $0.63, or when the context window is larger, 400K against 205K tokens.
- Shared results
- 21
- GLM 5 only
- 54
- GPT-5.2-Codex only
- 1
- Like-for-like categories
- 5 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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.
Coding
Vibe Coding Index, four public coding benchmarks
GLM 5
GLM 5 scores 59.7 against 55.1.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 against 27.9.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 5 has a GPQA Diamond result, 83.3%.
Directional only
Math
AIME
No call
Only GLM 5 has a AIME result, 91.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5 has a MMLU Pro result, 86.0%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5 has a Design Arena: websites result, 1247.
Directional only
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5
GLM 5 $0.0026, GPT-5.2-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GPT-5.2-Codex
GLM 5 $0.63, GPT-5.2-Codex $0.43.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5 59.7 from 2 benchmarks, GPT-5.2-Codex 55.1 from 2 benchmarks.
- Widest category
- Coding, 10.0 points apart on average over 11 shared benchmarks.
- Widest single gap
- ALE-Bench: GLM 5 765.6 against GPT-5.2-Codex 1299.9, 17.8 points apart on a 0 to 100 scale.
- Output price
- GLM 5 $3.20 against GPT-5.2-Codex $14.00 per million output tokens, 4.4x apart.
- Context window
- GLM 5 takes 205K tokens, GPT-5.2-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.
| Category | GLM 5 | Bars | GPT-5.2-Codex | Ties |
|---|---|---|---|---|
| Coding | 5 | 5 | 1 tie | |
| Intelligence | 0 | 1 | ||
| Agentic work | 4 | 1 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 9 | 11 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index55
| Benchmark | GLM 5 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | 72.8% | Tie |
| SWE-bench Multilingual | 69.7% | 66.3% | GLM 5 |
| LMArena WebDev | 1435 | 1339 | GLM 5 |
| LiveCodeBench (Vals AI) | 81.9% | 88.0% | GPT-5.2-Codex |
| SWE-bench Verified (Vals AI) | 71.4% | 72.4% | GPT-5.2-Codex |
| Vibe Code Bench (Vals AI) | 23.4% | 37.9% | GPT-5.2-Codex |
| Terminal-Bench Hard (Artificial Analysis run) | 43.2% | 37.1% | GLM 5 |
| Terminal-Bench | 52.4% | 66.5% | GPT-5.2-Codex |
| WeirdML | 48.2% | Not measured | Only GLM 5 measured |
| ALE-Bench | 765.6 | 1299.9 | GPT-5.2-Codex |
| LMArena Text: coding | 1461 | Not measured | Only GLM 5 measured |
| LMArena Text: software and IT | 1460 | Not measured | Only GLM 5 measured |
| LMArena WebDev: React | 1424 | 1329 | GLM 5 |
| LMArena WebDev: HTML | 1450 | 1348 | GLM 5 |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 27.9 | 28.5 | GPT-5.2-Codex |
| Epoch Capabilities Index | 145.8 | Not measured | Only GLM 5 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GLM 5 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1051 | Only GPT-5.2-Codex measured |
| Design Arena: agents building full-stack apps | 1123 | 993 | GLM 5 |
| Design Arena: agents building mobile apps | 1136 | 1090 | GLM 5 |
| Design Arena: agents building Android apps | 1152 | 1176 | GPT-5.2-Codex |
| Design Arena: agents building Godot games | 1144 | 1142 | GLM 5 |
| Design Arena: HTML slides | 1148 | Not measured | Only GLM 5 measured |
| τ²-Bench Telecom | 98.2% | 92.1% | GLM 5 |
| Vending-Bench 2 | $4,432 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| GPQA Diamond | 83.3% | Not measured | Only GLM 5 measured |
| GPQA Diamond (Artificial Analysis run) | 82.0% | 89.9% | GPT-5.2-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 29.3% | 35.7% | GPT-5.2-Codex |
| GPQA Diamond (Epoch AI run) | 87.8% | Not measured | Only GLM 5 measured |
| ARC-AGI | 44.7% | Not measured | Only GLM 5 measured |
| ARC-AGI-2 | 4.9% | Not measured | Only GLM 5 measured |
| SimpleBench | 53.2% | Not measured | Only GLM 5 measured |
| LMArena Text: hard prompts | 1452 | Not measured | Only GLM 5 measured |
| LMArena Text: expert | 1454 | Not measured | Only GLM 5 measured |
| LMArena Text: hard prompts in English | 1460 | Not measured | Only 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
| Benchmark | GLM 5 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AIME | 91.7% | Not measured | Only GLM 5 measured |
| OTIS Mock AIME | 80.0% | Not measured | Only GLM 5 measured |
| LMArena Text: math | 1440 | Not measured | Only GLM 5 measured |
| LMArena Text: mathematical work | 1451 | Not measured | Only 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
| Benchmark | GLM 5 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | Not measured | Only GLM 5 measured |
| LegalBench (Vals AI) | 84.1% | Not measured | Only GLM 5 measured |
| CaseLaw (Vals AI) | 52.5% | Not measured | Only GLM 5 measured |
| CorpFin (Vals AI) | 62.9% | Not measured | Only GLM 5 measured |
| TaxEval (Vals AI) | 70.0% | Not measured | Only GLM 5 measured |
| MedQA (Vals AI) | 94.3% | Not measured | Only GLM 5 measured |
| LMArena Text: science | 1460 | Not measured | Only GLM 5 measured |
| LMArena Text: medicine and healthcare | 1455 | Not measured | Only GLM 5 measured |
| LMArena Text: legal and government | 1453 | Not measured | Only GLM 5 measured |
| LMArena Text: business and finance | 1430 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 7 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 5 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1247 | Not measured | Only GLM 5 measured |
| Design Arena: UI components | 1230 | Not measured | Only GLM 5 measured |
| Design Arena: game dev | 1234 | Not measured | Only GLM 5 measured |
| Design Arena: data viz | 1228 | Not measured | Only GLM 5 measured |
| Design Arena: 3D | 1229 | Not measured | Only GLM 5 measured |
| Design Arena: all code categories | 1246 | Not measured | Only GLM 5 measured |
| Design Arena: SVG | 1168 | Not measured | Only GLM 5 measured |
| Design Arena: ASCII art | 1146 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| IFBench | 72.3% | 77.6% | GPT-5.2-Codex |
| Long context reasoning (AA-LCR) | 75.7% | 82.3% | GPT-5.2-Codex |
| CL-bench | 18.7% | Not measured | Only GLM 5 measured |
| LMArena Text | 1446 | Not measured | Only GLM 5 measured |
| LMArena Text: instruction following | 1428 | Not measured | Only GLM 5 measured |
| LMArena Text: creative writing | 1438 | Not measured | Only GLM 5 measured |
| LMArena Text: English | 1458 | Not measured | Only GLM 5 measured |
| LMArena Text: non-English | 1430 | Not measured | Only GLM 5 measured |
| LMArena Text: Chinese | 1511 | Not measured | Only GLM 5 measured |
| LMArena Text: German | 1447 | Not measured | Only GLM 5 measured |
| LMArena Text: Japanese | 1416 | Not measured | Only GLM 5 measured |
| LMArena Text: Korean | 1423 | Not measured | Only GLM 5 measured |
| LMArena Text: French | 1456 | Not measured | Only GLM 5 measured |
| LMArena Text: Spanish | 1454 | Not measured | Only GLM 5 measured |
| LMArena Text: Russian | 1436 | Not measured | Only GLM 5 measured |
| LMArena Text: Polish | 1434 | Not measured | Only GLM 5 measured |
| LMArena Text: multi-turn | 1456 | Not measured | Only GLM 5 measured |
| LMArena Text: longer queries | 1446 | Not measured | Only GLM 5 measured |
| LMArena Text: writing and language | 1437 | Not measured | Only GLM 5 measured |
| LMArena Text: entertainment and media | 1426 | Not measured | Only 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
| Benchmark | GLM 5 | GPT-5.2-Codex | Leader |
|---|---|---|---|
| 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 costs less
Repository review
50K input + 3K output tokens
GLM 5 costs less
Agent loop
200K input + 10K output tokens
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.
| Specification | Z.ai GLM 5 | OpenAI GPT-5.2-Codex |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | Open weights | No public weights listed |
| Released | February 11, 2026 | January 14, 2026 |
| Context window | 205K | 400K |
| Input $/Mtok | $1.00 | $1.75 |
| Output $/Mtok | $3.20 | $14.00 |
| Coding index | 59.7 | 55.1 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | $0.63 | $0.43 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5 better than GPT-5.2-Codex for coding?
Pick GLM 5 for coding: it scores 59.7 on the Vibe Coding Index against 55.1 for GPT-5.2-Codex. Pick GPT-5.2-Codex when a solved task costs $0.43 against $0.63, or when the context window is larger, 400K against 205K tokens.
Which is cheaper, GLM 5 or GPT-5.2-Codex?
GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; GPT-5.2-Codex costs $1.75 and $14.00. Per solved SWE-bench Verified task, GLM 5 costs $0.63 and GPT-5.2-Codex $0.43.
Which has the bigger context window, GLM 5 or GPT-5.2-Codex?
GPT-5.2-Codex, with 400K tokens against 205K.
How many benchmarks is the GLM 5 vs GPT-5.2-Codex comparison based on?
21 benchmarks have a result for both models as of October 21, 2026. GLM 5's coding index rests on 2 independent benchmarks and GPT-5.2-Codex's on 2. Where only one model has a result, the page says so instead of naming a winner.