GLM 5.2 vs GLM 5V Turbo
GLM 5.2 leads GLM 5V Turbo on the Artificial Analysis Intelligence Index, 33.7 to 23.5, at about the same output price ($4.40 vs $4.00 per million). Updated October 22, 2026; 53 benchmarks have a result for both.
GLM 5.2 or GLM 5V Turbo: which to pick
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding.
Pick GLM 5V Turbo for output tokens 1.1x cheaper ($4.00 vs $4.40 per million) and work measured by Design Arena: agents building Android apps, where it leads.
- Shared results
- 53
- GLM 5.2 only
- 45
- GLM 5V Turbo only
- 12
- 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.
Coding
Vibe Coding Index, six public coding benchmarks
No call
Only GLM 5.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.2
GLM 5.2 leads, 33.7 against 23.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.2 has a AA Agentic Index result, 38.4.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.2 has a GPQA Diamond result, 85.6%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.2 has a MMLU Pro result, 86.7%.
Directional only
Web design
Design Arena: websites
GLM 5.2
GLM 5.2 leads, 1285 against 1230.
Same board, both measured
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 203K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5V Turbo
GLM 5.2 $0.0036, GLM 5V Turbo $0.0032 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.2 has the number, $0.86.
Directional only
At a glance
- Coding index
- GLM 5.2 61.1 from 4 benchmarks, GLM 5V Turbo not benchmarked.
- Widest category
- Coding, 20.9 points apart on average over 6 shared benchmarks.
- Widest single gap
- LMArena WebDev: React: GLM 5.2 1617 against GLM 5V Turbo 1386, 35.6 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $4.40 against GLM 5V Turbo $4.00 per million output tokens, 1.1x apart.
- Context window
- GLM 5.2 takes 1.05M tokens, GLM 5V Turbo 203K.
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 5.2 | Bars | GLM 5V Turbo | Ties |
|---|---|---|---|---|
| Coding | 6 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 8 | 1 | ||
| Reasoning | 5 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| Web design | 8 | 0 | ||
| General | 18 | 0 | ||
| All shared boards | 52 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index60
| Benchmark | GLM 5.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| LMArena WebDev | 1604 | 1402 | GLM 5.2 |
| AA Coding Index | 68.8 | Not measured | Only GLM 5.2 measured |
| LiveCodeBench (Vals AI) | 69.5% | Not measured | Only GLM 5.2 measured |
| SWE-bench Verified (Vals AI) | 82.8% | Not measured | Only GLM 5.2 measured |
| Vibe Code Bench (Vals AI) | 64.0% | Not measured | Only GLM 5.2 measured |
| Code migration (Vals AI) | 37.9% | Not measured | Only GLM 5.2 measured |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | Not measured | Only GLM 5.2 measured |
| SkillsBench (Vals AI) | 45.1% | Not measured | Only GLM 5.2 measured |
| SciCode | 51.2% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | 32.6% | GLM 5.2 |
| FrontierCode | 24.5% | Not measured | Only GLM 5.2 measured |
| WeirdML | 70.1% | Not measured | Only GLM 5.2 measured |
| ALE-Bench | 1047.0 | Not measured | Only GLM 5.2 measured |
| LMArena Text: coding | 1487 | 1466 | GLM 5.2 |
| LMArena Text: software and IT | 1486 | 1460 | GLM 5.2 |
| LMArena WebDev: React | 1617 | 1386 | GLM 5.2 |
| LMArena WebDev: HTML | 1565 | 1356 | GLM 5.2 |
| LMArena WebDev: from an image | Not measured | 1425 | Only GLM 5V Turbo measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 23.5 | GLM 5.2 |
| Epoch Capabilities Index | 151.9 | Not measured | Only GLM 5.2 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index81
| Benchmark | GLM 5.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building web apps | 1222 | 1124 | GLM 5.2 |
| Design Arena: agents building full-stack apps | 1241 | 1149 | GLM 5.2 |
| Design Arena: agents building mobile apps | 1159 | 1134 | GLM 5.2 |
| Design Arena: agents building Android apps | 1178 | 1267 | GLM 5V Turbo |
| Design Arena: agents building Godot games | 1142 | 957 | GLM 5.2 |
| Design Arena: agents building games | 1206 | 1102 | GLM 5.2 |
| Design Arena: HTML slides | 1125 | 1070 | GLM 5.2 |
| Design Arena: PowerPoint slides | 1170 | 1127 | GLM 5.2 |
| SREBench (Vals AI) | 0.0% | Not measured | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 49.7% | Not measured | Only GLM 5.2 measured |
| Legal Research Bench (Vals AI) | 31.3% | Not measured | Only GLM 5.2 measured |
| Excel Modeling Benchmark (Vals AI) | 61.5% | Not measured | Only GLM 5.2 measured |
| τ²-Bench Telecom | 99.1% | 98.5% | GLM 5.2 |
| APEX-Agents | 45.2% | Not measured | Only GLM 5.2 measured |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| Vending-Bench 2 | $8,314 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | Not measured | Only GLM 5.2 measured |
| GPQA Diamond (Artificial Analysis run) | 89.5% | 80.9% | GLM 5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 17.1% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 91.9% | Not measured | Only GLM 5.2 measured |
| ARC-AGI | 77.0% | Not measured | Only GLM 5.2 measured |
| ARC-AGI-2 | 22.8% | Not measured | Only GLM 5.2 measured |
| SimpleBench | 58.8% | Not measured | Only GLM 5.2 measured |
| Chess puzzles | 22.0% | Not measured | Only GLM 5.2 measured |
| Mystery game puzzles | 19.0% | Not measured | Only GLM 5.2 measured |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1481 | 1443 | GLM 5.2 |
| LMArena Text: expert | 1487 | 1453 | GLM 5.2 |
| LMArena Text: hard prompts in English | 1481 | 1456 | GLM 5.2 |
| LMArena Vision: diagrams | Not measured | 1286 | Only GLM 5V Turbo measured |
| LMArena Vision: homework | Not measured | 1273 | Only GLM 5V Turbo measured |
| Kagi LLM Benchmark | 62.6% | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| OTIS Mock AIME | 86.4% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tiers 1 to 3 | 59.2% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.2 measured |
| ProofBench | 35.0% | Not measured | Only GLM 5.2 measured |
| LMArena Text: math | 1483 | 1442 | GLM 5.2 |
| LMArena Text: mathematical work | 1489 | 1447 | GLM 5.2 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | Not measured | Only GLM 5.2 measured |
| LegalBench (Vals AI) | 84.1% | Not measured | Only GLM 5.2 measured |
| CorpFin (Vals AI) | 66.1% | Not measured | Only GLM 5.2 measured |
| TaxEval (Vals AI) | 73.3% | Not measured | Only GLM 5.2 measured |
| MedCode (Vals AI) | 40.8% | Not measured | Only GLM 5.2 measured |
| SimpleQA Verified | 34.2% | Not measured | Only GLM 5.2 measured |
| LMArena Text: science | 1489 | 1448 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1475 | 1433 | GLM 5.2 |
| LMArena Text: legal and government | 1486 | 1436 | GLM 5.2 |
| LMArena Text: business and finance | 1451 | 1435 | GLM 5.2 |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | 1230 | GLM 5.2 |
| Design Arena: UI components | 1294 | 1210 | GLM 5.2 |
| Design Arena: game dev | 1268 | 1218 | GLM 5.2 |
| Design Arena: data viz | 1285 | 1202 | GLM 5.2 |
| Design Arena: 3D | 1287 | 1208 | GLM 5.2 |
| Design Arena: all code categories | 1287 | 1228 | GLM 5.2 |
| Design Arena: SVG | 1213 | 1151 | GLM 5.2 |
| Design Arena: ASCII art | 1191 | 1117 | GLM 5.2 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index180
| Benchmark | GLM 5.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 83.5% | Not measured | Only GLM 5.2 measured |
| IFBench | 73.3% | 61.1% | GLM 5.2 |
| Long context reasoning (AA-LCR) | 78.3% | 70.3% | GLM 5.2 |
| DTBench | 93.6% | Not measured | Only GLM 5.2 measured |
| LMCA | 45.8% | Not measured | Only GLM 5.2 measured |
| Surface Evolver Bench | 55.6% | Not measured | Only GLM 5.2 measured |
| LMArena Text | 1470 | 1437 | GLM 5.2 |
| LMArena Text: instruction following | 1466 | 1423 | GLM 5.2 |
| LMArena Text: creative writing | 1463 | 1417 | GLM 5.2 |
| LMArena Vision | Not measured | 1264 | Only GLM 5V Turbo measured |
| LMArena Text: English | 1475 | 1454 | GLM 5.2 |
| LMArena Text: non-English | 1459 | 1420 | GLM 5.2 |
| LMArena Text: Chinese | 1522 | 1491 | GLM 5.2 |
| LMArena Text: German | 1470 | 1422 | GLM 5.2 |
| LMArena Text: Japanese | 1450 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Korean | 1440 | 1397 | GLM 5.2 |
| LMArena Text: French | 1479 | 1443 | GLM 5.2 |
| LMArena Text: Spanish | 1478 | 1452 | GLM 5.2 |
| LMArena Text: Russian | 1468 | 1432 | GLM 5.2 |
| LMArena Text: Polish | 1456 | 1432 | GLM 5.2 |
| LMArena Text: multi-turn | 1469 | 1431 | GLM 5.2 |
| LMArena Text: longer queries | 1480 | 1438 | GLM 5.2 |
| LMArena Text: writing and language | 1463 | 1420 | GLM 5.2 |
| LMArena Text: entertainment and media | 1450 | 1407 | GLM 5.2 |
| LMArena Vision: English | Not measured | 1274 | Only GLM 5V Turbo measured |
| LMArena Vision: Chinese | Not measured | 1329 | Only GLM 5V Turbo measured |
| LMArena Vision: reading text | Not measured | 1270 | Only GLM 5V Turbo measured |
| LMArena Vision: humor | Not measured | 1268 | Only GLM 5V Turbo measured |
| LMArena Vision: captioning | Not measured | 1264 | Only GLM 5V Turbo measured |
| LMArena Vision: recognizing things | Not measured | 1241 | Only GLM 5V Turbo measured |
| LMArena Vision: creative writing | Not measured | 1252 | Only GLM 5V Turbo measured |
| LMArena Document | Not measured | 1416 | Only GLM 5V Turbo measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | GLM 5V Turbo | 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 5V Turbo costs less
Repository review
50K input + 3K output tokens
GLM 5V Turbo costs less
Agent loop
200K input + 10K output tokens
GLM 5V 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 5.2 | Z.ai GLM 5V Turbo |
|---|---|---|
| Maker | Z.ai | Z.ai |
| Weights | Open weights | No public weights listed |
| Released | June 16, 2026 | April 1, 2026 |
| Context window | 1.05M | 203K |
| Input $/Mtok | $1.40 | $1.20 |
| Output $/Mtok | $4.40 | $4.00 |
| Coding index | 61.1 | Not benchmarked |
| Independent measurements behind it | 4 | 1 |
| Cost per solved task | $0.86 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than GLM 5V Turbo for coding?
Only GLM 5.2 has a coding score, 61.1 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding. Pick GLM 5V Turbo for output tokens 1.1x cheaper ($4.00 vs $4.40 per million) and work measured by Design Arena: agents building Android apps, where it leads.
Which is cheaper, GLM 5.2 or GLM 5V Turbo?
GLM 5.2 costs $1.40 per million input tokens and $4.40 per million output tokens; GLM 5V Turbo costs $1.20 and $4.00. Cost per solved task is known for GLM 5.2 only.
Which has the bigger context window, GLM 5.2 or GLM 5V Turbo?
GLM 5.2, with 1.05M tokens against 203K.
How many benchmarks is the GLM 5.2 vs GLM 5V Turbo comparison based on?
53 benchmarks have a result for both models as of October 22, 2026. GLM 5.2's coding index rests on 4 independent benchmarks and GLM 5V Turbo's on 1. Where only one model has a result, the page says so instead of naming a winner.