GLM 5 Turbo vs GPT-5.2-Codex
GPT-5.2-Codex leads GLM 5 Turbo on the Artificial Analysis Intelligence Index, 28.5 to 26.6, and GLM 5 Turbo costs 3.5x less per output token ($4.00 vs $14.00 per million). Updated October 21, 2026; 8 benchmarks have a result for both.
GLM 5 Turbo or GPT-5.2-Codex: which to pick
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; GLM 5 Turbo has none, so the two cannot be ranked on coding.
Pick GLM 5 Turbo for output tokens 3.5x cheaper ($4.00 vs $14.00 per million) and work measured by τ²-Bench Telecom, where it leads.
- Shared results
- 8
- GLM 5 Turbo only
- 8
- GPT-5.2-Codex only
- 14
- Like-for-like categories
- 5 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GPT-5.2-Codex has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GPT-5.2-Codex
GPT-5.2-Codex leads, 28.5 against 26.6.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5 Turbo has a Design Arena: websites result, 1262.
Directional only
Long documents
Listed context window
GPT-5.2-Codex
GPT-5.2-Codex takes 400K tokens against 203K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5 Turbo
GLM 5 Turbo $0.0032, GPT-5.2-Codex $0.0088 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5.2-Codex has the number, $0.43.
Directional only
At a glance
- Coding index
- GLM 5 Turbo not benchmarked, GPT-5.2-Codex 45.4 from 3 benchmarks.
- Widest category
- Coding, 13.0 points apart on average over 2 shared benchmarks.
- Widest single gap
- ALE-Bench: GLM 5 Turbo 634.0 against GPT-5.2-Codex 1299.9, 22.2 points apart on a 0 to 100 scale.
- Output price
- GLM 5 Turbo $4.00 against GPT-5.2-Codex $14.00 per million output tokens, 3.5x apart.
- Context window
- GLM 5 Turbo takes 203K 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 Turbo | Bars | GPT-5.2-Codex | Ties |
|---|---|---|---|---|
| Coding | 0 | 2 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 0 | ||
| Reasoning | 0 | 2 | ||
| General | 0 | 2 | ||
| All shared boards | 1 | 7 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index02
| Benchmark | GLM 5 Turbo | GPT-5.2-Codex | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 72.8% | Only GPT-5.2-Codex measured |
| SWE-bench Multilingual | Not measured | 66.3% | Only GPT-5.2-Codex measured |
| LMArena WebDev | Not measured | 1339 | Only GPT-5.2-Codex measured |
| LiveCodeBench (Vals AI) | Not measured | 88.0% | Only GPT-5.2-Codex measured |
| SWE-bench Verified (Vals AI) | Not measured | 72.4% | Only GPT-5.2-Codex measured |
| Vibe Code Bench (Vals AI) | Not measured | 37.9% | Only GPT-5.2-Codex measured |
| Terminal-Bench Hard (Artificial Analysis run) | 33.3% | 37.1% | GPT-5.2-Codex |
| Terminal-Bench | Not measured | 66.5% | Only GPT-5.2-Codex measured |
| ALE-Bench | 634.0 | 1299.9 | GPT-5.2-Codex |
| LMArena WebDev: React | Not measured | 1329 | Only GPT-5.2-Codex measured |
| LMArena WebDev: HTML | Not measured | 1348 | Only GPT-5.2-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
| Benchmark | GLM 5 Turbo | GPT-5.2-Codex | Leader |
|---|---|---|---|
| AA Intelligence Index | 26.6 | 28.5 | GPT-5.2-Codex |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5 Turbo | 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 | Not measured | 993 | Only GPT-5.2-Codex measured |
| Design Arena: agents building mobile apps | Not measured | 1090 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Android apps | Not measured | 1176 | Only GPT-5.2-Codex measured |
| Design Arena: agents building Godot games | Not measured | 1142 | Only GPT-5.2-Codex measured |
| τ²-Bench Telecom | 98.5% | 92.1% | GLM 5 Turbo |
| Neither model has a result on the other 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5 Turbo | GPT-5.2-Codex | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 84.7% | 89.9% | GPT-5.2-Codex |
| Humanity's Last Exam (Artificial Analysis run) | 27.8% | 35.7% | GPT-5.2-Codex |
| Neither model has a result on the other 19 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5 Turbo | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5 Turbo | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Neither model has a result on 17 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | GLM 5 Turbo | GPT-5.2-Codex | Leader |
|---|---|---|---|
| Design Arena: websites | 1262 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: UI components | 1263 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: game dev | 1248 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: data viz | 1264 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: 3D | 1248 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: all code categories | 1262 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: SVG | 1206 | Not measured | Only GLM 5 Turbo measured |
| Design Arena: ASCII art | 1160 | Not measured | Only GLM 5 Turbo 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 Turbo | GPT-5.2-Codex | Leader |
|---|---|---|---|
| IFBench | 73.2% | 77.6% | GPT-5.2-Codex |
| Long context reasoning (AA-LCR) | 71.7% | 82.3% | GPT-5.2-Codex |
| Neither model has a result on the other 44 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5 Turbo | 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 Turbo costs less
Repository review
50K input + 3K output tokens
GLM 5 Turbo costs less
Agent loop
200K input + 10K output tokens
GLM 5 Turbo 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 Turbo | OpenAI GPT-5.2-Codex |
|---|---|---|
| Maker | Z.ai | OpenAI |
| Weights | No public weights listed | No public weights listed |
| Released | March 15, 2026 | January 14, 2026 |
| Context window | 203K | 400K |
| Input $/Mtok | $1.20 | $1.75 |
| Output $/Mtok | $4.00 | $14.00 |
| Coding index | Not benchmarked | 45.4 |
| Independent measurements behind it | 0 | 3 |
| Cost per solved task | Not computable | $0.43 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5 Turbo better than GPT-5.2-Codex for coding?
Only GPT-5.2-Codex has a coding score, 45.4 on the Vibe Coding Index; GLM 5 Turbo has none, so the two cannot be ranked on coding. Pick GLM 5 Turbo for output tokens 3.5x cheaper ($4.00 vs $14.00 per million) and work measured by τ²-Bench Telecom, where it leads.
Which is cheaper, GLM 5 Turbo or GPT-5.2-Codex?
GLM 5 Turbo costs $1.20 per million input tokens and $4.00 per million output tokens; GPT-5.2-Codex costs $1.75 and $14.00. Cost per solved task is known for GPT-5.2-Codex only.
Which has the bigger context window, GLM 5 Turbo or GPT-5.2-Codex?
GPT-5.2-Codex, with 400K tokens against 203K.
How many benchmarks is the GLM 5 Turbo vs GPT-5.2-Codex comparison based on?
8 benchmarks have a result for both models as of October 21, 2026. GLM 5 Turbo's coding index rests on 0 independent benchmarks and GPT-5.2-Codex's on 3. Where only one model has a result, the page says so instead of naming a winner.