DeepSeek V3.2 vs GLM 5V Turbo
GLM 5V Turbo leads DeepSeek V3.2 on the Artificial Analysis Intelligence Index, 23.5 to 21.5, and DeepSeek V3.2 costs 4.5x less per output token ($0.88 vs $4.00 per million). Updated October 22, 2026; 45 benchmarks have a result for both.
DeepSeek V3.2 or GLM 5V Turbo: which to pick
Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding.
Pick GLM 5V Turbo for a larger context window (203K against 164K tokens) and work measured by LMArena WebDev or LMArena Text: coding, where it leads.
- Shared results
- 45
- DeepSeek V3.2 only
- 30
- GLM 5V Turbo only
- 20
- 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 DeepSeek V3.2 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5V Turbo
GLM 5V Turbo leads, 23.5 against 21.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only DeepSeek V3.2 has a GPQA Diamond result, 80.3%.
Directional only
Math
AIME
No call
Only DeepSeek V3.2 has a AIME result, 84.6%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V3.2 has a MMLU Pro result, 84.9%.
Directional only
Web design
Design Arena: websites
GLM 5V Turbo
GLM 5V Turbo leads, 1230 against 1175.
Same board, both measured
Long documents
Listed context window
GLM 5V Turbo
GLM 5V Turbo takes 203K tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2
DeepSeek V3.2 $0.0007, GLM 5V Turbo $0.0032 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V3.2 has the number, $0.99.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 30.0 from 3 benchmarks, GLM 5V Turbo not benchmarked.
- Widest category
- Web design, 8.5 points apart on average over 8 shared benchmarks.
- Widest single gap
- Design Arena: SVG: DeepSeek V3.2 1032 against GLM 5V Turbo 1151, 14.9 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 $0.88 against GLM 5V Turbo $4.00 per million output tokens, 4.5x apart.
- Context window
- DeepSeek V3.2 takes 164K 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 | DeepSeek V3.2 | Bars | GLM 5V Turbo | Ties |
|---|---|---|---|---|
| Coding | 2 | 4 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 2 | 3 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 3 | ||
| Web design | 0 | 8 | ||
| General | 1 | 17 | ||
| All shared boards | 6 | 39 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index24
| Benchmark | DeepSeek V3.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| SWE-bench Verified | 70.0% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Multilingual | 59.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena WebDev | 1362 | 1402 | GLM 5V Turbo |
| AA Coding Index | 44.2 | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Vals AI) | 80.7% | Not measured | Only DeepSeek V3.2 measured |
| SWE-bench Verified (Vals AI) | 67.6% | Not measured | Only DeepSeek V3.2 measured |
| Vibe Code Bench (Vals AI) | 5.1% | Not measured | Only DeepSeek V3.2 measured |
| LiveCodeBench (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 35.6% | 32.6% | DeepSeek V3.2 |
| Terminal-Bench | 39.6% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: coding | 1453 | 1466 | GLM 5V Turbo |
| LMArena Text: software and IT | 1449 | 1460 | GLM 5V Turbo |
| LMArena WebDev: React | 1350 | 1386 | GLM 5V Turbo |
| LMArena WebDev: HTML | 1371 | 1356 | DeepSeek V3.2 |
| LMArena WebDev: from an image | Not measured | 1425 | Only GLM 5V Turbo measured |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | 21.5 | 23.5 | GLM 5V Turbo |
| Epoch Capabilities Index | 146.1 | Not measured | Only DeepSeek V3.2 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | Not measured | 1124 | Only GLM 5V Turbo measured |
| Design Arena: agents building full-stack apps | Not measured | 1149 | Only GLM 5V Turbo measured |
| Design Arena: agents building mobile apps | Not measured | 1134 | Only GLM 5V Turbo measured |
| Design Arena: agents building Android apps | Not measured | 1267 | Only GLM 5V Turbo measured |
| Design Arena: agents building Godot games | Not measured | 957 | Only GLM 5V Turbo measured |
| Design Arena: agents building games | Not measured | 1102 | Only GLM 5V Turbo measured |
| Design Arena: HTML slides | Not measured | 1070 | Only GLM 5V Turbo measured |
| Design Arena: PowerPoint slides | Not measured | 1127 | Only GLM 5V Turbo measured |
| τ²-Bench Telecom | 90.6% | 98.5% | GLM 5V Turbo |
| APEX-Agents | 21.3% | Not measured | Only DeepSeek V3.2 measured |
| Vending-Bench 2 | $1,034 | Not measured | Only DeepSeek V3.2 measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | DeepSeek V3.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| GPQA Diamond | 80.3% | Not measured | Only DeepSeek V3.2 measured |
| GPQA Diamond (Artificial Analysis run) | 84.0% | 80.9% | DeepSeek V3.2 |
| Humanity's Last Exam (Artificial Analysis run) | 24.6% | 17.1% | DeepSeek V3.2 |
| ARC-AGI | 57.0% | Not measured | Only DeepSeek V3.2 measured |
| ARC-AGI-2 | 4.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: hard prompts | 1434 | 1443 | GLM 5V Turbo |
| LMArena Text: expert | 1436 | 1453 | GLM 5V Turbo |
| LMArena Text: hard prompts in English | 1446 | 1456 | GLM 5V Turbo |
| LMArena Vision: diagrams | Not measured | 1286 | Only GLM 5V Turbo measured |
| LMArena Vision: homework | Not measured | 1273 | Only GLM 5V Turbo measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| AIME | 84.6% | Not measured | Only DeepSeek V3.2 measured |
| MGSM (Vals AI) | 86.0% | Not measured | Only DeepSeek V3.2 measured |
| AA Math Index | 92.0 | Not measured | Only DeepSeek V3.2 measured |
| AIME 2025 (Artificial Analysis run) | 92.0% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: math | 1435 | 1442 | GLM 5V Turbo |
| LMArena Text: mathematical work | 1440 | 1447 | GLM 5V Turbo |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | DeepSeek V3.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| MMLU Pro | 84.9% | Not measured | Only DeepSeek V3.2 measured |
| LegalBench (Vals AI) | 76.1% | Not measured | Only DeepSeek V3.2 measured |
| CaseLaw (Vals AI) | 55.4% | Not measured | Only DeepSeek V3.2 measured |
| CorpFin (Vals AI) | 51.0% | Not measured | Only DeepSeek V3.2 measured |
| TaxEval (Vals AI) | 68.2% | Not measured | Only DeepSeek V3.2 measured |
| MedQA (Vals AI) | 93.9% | Not measured | Only DeepSeek V3.2 measured |
| MMLU Pro (Artificial Analysis run) | 86.2% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: science | 1446 | 1448 | GLM 5V Turbo |
| LMArena Text: medicine and healthcare | 1440 | 1433 | DeepSeek V3.2 |
| LMArena Text: legal and government | 1425 | 1436 | GLM 5V Turbo |
| LMArena Text: business and finance | 1410 | 1435 | GLM 5V Turbo |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | DeepSeek V3.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| Design Arena: websites | 1175 | 1230 | GLM 5V Turbo |
| Design Arena: UI components | 1146 | 1210 | GLM 5V Turbo |
| Design Arena: game dev | 1131 | 1218 | GLM 5V Turbo |
| Design Arena: data viz | 1158 | 1202 | GLM 5V Turbo |
| Design Arena: 3D | 1132 | 1208 | GLM 5V Turbo |
| Design Arena: all code categories | 1163 | 1228 | GLM 5V Turbo |
| Design Arena: SVG | 1032 | 1151 | GLM 5V Turbo |
| Design Arena: ASCII art | 1083 | 1117 | GLM 5V Turbo |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index117
| Benchmark | DeepSeek V3.2 | GLM 5V Turbo | Leader |
|---|---|---|---|
| IFBench | 60.7% | 61.1% | GLM 5V Turbo |
| Long context reasoning (AA-LCR) | 73.3% | 70.3% | DeepSeek V3.2 |
| DTBench | 85.6% | Not measured | Only DeepSeek V3.2 measured |
| LMCA | 28.8% | Not measured | Only DeepSeek V3.2 measured |
| CL-bench | 12.4% | Not measured | Only DeepSeek V3.2 measured |
| CL-bench Life | 7.4% | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text | 1425 | 1437 | GLM 5V Turbo |
| LMArena Text: instruction following | 1413 | 1423 | GLM 5V Turbo |
| LMArena Text: creative writing | 1400 | 1417 | GLM 5V Turbo |
| LMArena Vision | Not measured | 1264 | Only GLM 5V Turbo measured |
| LMArena Text: English | 1439 | 1454 | GLM 5V Turbo |
| LMArena Text: non-English | 1409 | 1420 | GLM 5V Turbo |
| LMArena Text: Chinese | 1455 | 1491 | GLM 5V Turbo |
| LMArena Text: German | 1413 | 1422 | GLM 5V Turbo |
| LMArena Text: Japanese | 1374 | Not measured | Only DeepSeek V3.2 measured |
| LMArena Text: Korean | 1370 | 1397 | GLM 5V Turbo |
| LMArena Text: French | 1429 | 1443 | GLM 5V Turbo |
| LMArena Text: Spanish | 1424 | 1452 | GLM 5V Turbo |
| LMArena Text: Russian | 1424 | 1432 | GLM 5V Turbo |
| LMArena Text: Polish | 1419 | 1432 | GLM 5V Turbo |
| LMArena Text: multi-turn | 1427 | 1431 | GLM 5V Turbo |
| LMArena Text: longer queries | 1428 | 1438 | GLM 5V Turbo |
| LMArena Text: writing and language | 1407 | 1420 | GLM 5V Turbo |
| LMArena Text: entertainment and media | 1395 | 1407 | GLM 5V Turbo |
| 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 | DeepSeek V3.2 | 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
DeepSeek V3.2 costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 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 | DeepSeek V3.2 | Z.ai GLM 5V Turbo |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | No public weights listed |
| Released | December 1, 2025 | April 1, 2026 |
| Context window | 164K | 203K |
| Input $/Mtok | $0.29 | $1.20 |
| Output $/Mtok | $0.88 | $4.00 |
| Coding index | 30.0 | Not benchmarked |
| Independent measurements behind it | 3 | 1 |
| Cost per solved task | $0.99 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 better than GLM 5V Turbo for coding?
Only DeepSeek V3.2 has a coding score, 30.0 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding. Pick GLM 5V Turbo for a larger context window (203K against 164K tokens) and work measured by LMArena WebDev or LMArena Text: coding, where it leads.
Which is cheaper, DeepSeek V3.2 or GLM 5V Turbo?
DeepSeek V3.2 costs $0.29 per million input tokens and $0.88 per million output tokens; GLM 5V Turbo costs $1.20 and $4.00. Cost per solved task is known for DeepSeek V3.2 only.
Which has the bigger context window, DeepSeek V3.2 or GLM 5V Turbo?
GLM 5V Turbo, with 203K tokens against 164K.
How many benchmarks is the DeepSeek V3.2 vs GLM 5V Turbo comparison based on?
45 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2's coding index rests on 3 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.