DeepSeek V3.2 Exp vs GLM 5V Turbo
GLM 5V Turbo leads DeepSeek V3.2 Exp on the Artificial Analysis Intelligence Index, 23.5 to 16.6, and DeepSeek V3.2 Exp costs 9.8x less per output token ($0.41 vs $4.00 per million). Updated October 22, 2026; 43 benchmarks have a result for both.
DeepSeek V3.2 Exp or GLM 5V Turbo: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.2 Exp for output tokens 9.8x cheaper ($0.41 vs $4.00 per million) and work measured by LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.
- Shared results
- 43
- DeepSeek V3.2 Exp only
- 17
- GLM 5V Turbo only
- 22
- 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
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
GLM 5V Turbo
GLM 5V Turbo leads, 23.5 against 16.6.
Same board, both measured
Web design
Design Arena: websites
GLM 5V Turbo
GLM 5V Turbo leads, 1230 against 1179.
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 Exp
DeepSeek V3.2 Exp $0.0005, GLM 5V Turbo $0.0032 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, GLM 5V Turbo not benchmarked.
- Widest category
- Agentic work, 64.6 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: DeepSeek V3.2 Exp 33.9% against GLM 5V Turbo 98.5%, 64.6 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against GLM 5V Turbo $4.00 per million output tokens, 9.8x apart.
- Context window
- DeepSeek V3.2 Exp 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 Exp | Bars | GLM 5V Turbo | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 1 | 3 | ||
| Web design | 0 | 7 | ||
| General | 4 | 14 | ||
| All shared boards | 5 | 38 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | DeepSeek V3.2 Exp | GLM 5V Turbo | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena WebDev | 1272 | 1402 | GLM 5V Turbo |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | 32.6% | GLM 5V Turbo |
| WeirdML | 39.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: coding | 1438 | 1466 | GLM 5V Turbo |
| LMArena Text: software and IT | 1438 | 1460 | GLM 5V Turbo |
| LMArena WebDev: React | Not measured | 1386 | Only GLM 5V Turbo measured |
| LMArena WebDev: HTML | 1281 | 1356 | GLM 5V Turbo |
| LMArena WebDev: from an image | Not measured | 1425 | Only GLM 5V Turbo measured |
| Neither model has a result on the other 33 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | GLM 5V Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 23.5 | GLM 5V Turbo |
| Epoch Capabilities Index | 144.9 | Not measured | Only DeepSeek V3.2 Exp measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.2 Exp | 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 | 1060 | Only GLM 5V Turbo measured |
| Design Arena: PowerPoint slides | Not measured | 1126 | Only GLM 5V Turbo measured |
| τ²-Bench Telecom | 33.9% | 98.5% | GLM 5V Turbo |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Berkeley Function Calling Leaderboard (BFCL) | 56.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V3.2 Exp | GLM 5V Turbo | Leader |
|---|---|---|---|
| GPQA Diamond (Artificial Analysis run) | 79.7% | 80.9% | GLM 5V Turbo |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 17.1% | GLM 5V Turbo |
| LMArena Text: hard prompts | 1429 | 1443 | GLM 5V Turbo |
| LMArena Text: expert | 1422 | 1453 | GLM 5V Turbo |
| LMArena Text: hard prompts in English | 1444 | 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 |
| Kagi LLM Benchmark | 52.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 15 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | GLM 5V Turbo | Leader |
|---|---|---|---|
| AA Math Index | 87.7 | Not measured | Only DeepSeek V3.2 Exp measured |
| AIME 2025 (Artificial Analysis run) | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: math | 1422 | 1442 | GLM 5V Turbo |
| LMArena Text: mathematical work | 1433 | 1447 | GLM 5V Turbo |
| MathArena | 14.4% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | DeepSeek V3.2 Exp | GLM 5V Turbo | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: science | 1443 | 1448 | GLM 5V Turbo |
| LMArena Text: medicine and healthcare | 1468 | 1433 | DeepSeek V3.2 Exp |
| LMArena Text: legal and government | 1419 | 1436 | GLM 5V Turbo |
| LMArena Text: business and finance | 1412 | 1435 | GLM 5V Turbo |
| Neither model has a result on the other 15 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | DeepSeek V3.2 Exp | GLM 5V Turbo | Leader |
|---|---|---|---|
| Design Arena: websites | 1179 | 1230 | GLM 5V Turbo |
| Design Arena: UI components | 1164 | 1210 | GLM 5V Turbo |
| Design Arena: game dev | 1143 | 1218 | GLM 5V Turbo |
| Design Arena: data viz | 1152 | 1202 | GLM 5V Turbo |
| Design Arena: 3D | 1148 | 1208 | GLM 5V Turbo |
| Design Arena: all code categories | 1170 | 1227 | GLM 5V Turbo |
| Design Arena: SVG | 1031 | 1150 | GLM 5V Turbo |
| Design Arena: ASCII art | Not measured | 1116 | Only GLM 5V Turbo measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index414
| Benchmark | DeepSeek V3.2 Exp | GLM 5V Turbo | Leader |
|---|---|---|---|
| IFBench | 54.1% | 61.1% | GLM 5V Turbo |
| Long context reasoning (AA-LCR) | 72.3% | 70.3% | DeepSeek V3.2 Exp |
| DTBench | 87.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMCA | 29.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Fiction.LiveBench | 83.3% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench | 13.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | 1437 | GLM 5V Turbo |
| LMArena Text: instruction following | 1401 | 1423 | GLM 5V Turbo |
| LMArena Text: creative writing | 1404 | 1417 | GLM 5V Turbo |
| LMArena Vision | Not measured | 1264 | Only GLM 5V Turbo measured |
| LMArena Text: English | 1441 | 1454 | GLM 5V Turbo |
| LMArena Text: non-English | 1404 | 1420 | GLM 5V Turbo |
| LMArena Text: Chinese | 1461 | 1491 | GLM 5V Turbo |
| LMArena Text: German | 1440 | 1422 | DeepSeek V3.2 Exp |
| LMArena Text: Japanese | 1331 | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: Korean | 1372 | 1397 | GLM 5V Turbo |
| LMArena Text: French | 1434 | 1443 | GLM 5V Turbo |
| LMArena Text: Spanish | 1441 | 1452 | GLM 5V Turbo |
| LMArena Text: Russian | 1412 | 1432 | GLM 5V Turbo |
| LMArena Text: Polish | 1435 | 1432 | DeepSeek V3.2 Exp |
| LMArena Text: multi-turn | 1422 | 1431 | GLM 5V Turbo |
| LMArena Text: longer queries | 1418 | 1438 | GLM 5V Turbo |
| LMArena Text: writing and language | 1407 | 1420 | GLM 5V Turbo |
| LMArena Text: entertainment and media | 1407 | 1407 | DeepSeek V3.2 Exp |
| 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 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | 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 Exp costs less
Repository review
50K input + 3K output tokens
DeepSeek V3.2 Exp costs less
Agent loop
200K input + 10K output tokens
DeepSeek V3.2 Exp 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 Exp | Z.ai GLM 5V Turbo |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | No public weights listed |
| Released | September 29, 2025 | April 1, 2026 |
| Context window | 164K | 203K |
| Input $/Mtok | $0.27 | $1.20 |
| Output $/Mtok | $0.41 | $4.00 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than GLM 5V Turbo for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 9.8x cheaper ($0.41 vs $4.00 per million) and work measured by LMArena WebDev or Terminal-Bench Hard (Artificial Analysis run), where it leads.
Which is cheaper, DeepSeek V3.2 Exp or GLM 5V Turbo?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GLM 5V Turbo costs $1.20 and $4.00. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V3.2 Exp or GLM 5V Turbo?
GLM 5V Turbo, with 203K tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs GLM 5V Turbo comparison based on?
43 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.2 Exp's coding index rests on 1 independent benchmark and GLM 5V Turbo's on 1. Where only one model has a result, the page says so instead of naming a winner.