DeepSeek V4 Pro 0423 vs GLM 5V Turbo
DeepSeek V4 Pro 0423 leads GLM 5V Turbo on the Artificial Analysis Intelligence Index, 30.4 to 23.5, and DeepSeek V4 Pro 0423 costs 1.3x less per output token ($2.976 vs $4.00 per million). Updated October 22, 2026; 42 benchmarks have a result for both.
DeepSeek V4 Pro 0423 or GLM 5V Turbo: which to pick
Only DeepSeek V4 Pro 0423 has a coding score, 38.7 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding.
Pick GLM 5V Turbo for work measured by Design Arena: agents building web apps or Design Arena: agents building full-stack apps, where it leads.
- Shared results
- 42
- DeepSeek V4 Pro 0423 only
- 42
- GLM 5V Turbo only
- 23
- 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 V4 Pro 0423 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 30.4 against 23.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0423 has a AA Agentic Index result, 26.3.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Pro 0423 has a GPQA Diamond result, 89.4%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Pro 0423 has a MMLU Pro result, 87.3%.
Directional only
Web design
Design Arena: websites
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 1234 against 1230.
Same board, both measured
Long documents
Listed context window
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 takes 1.05M tokens against 203K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 $0.0028, GLM 5V Turbo $0.0032 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V4 Pro 0423 has the number, $0.57.
Directional only
At a glance
- Coding index
- DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, GLM 5V Turbo not benchmarked.
- Widest category
- Agentic work, 17.8 points apart on average over 3 shared benchmarks.
- Widest single gap
- Design Arena: agents building full-stack apps: DeepSeek V4 Pro 0423 948 against GLM 5V Turbo 1149, 25.1 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $2.976 against GLM 5V Turbo $4.00 per million output tokens, 1.3x apart.
- Context window
- DeepSeek V4 Pro 0423 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 | DeepSeek V4 Pro 0423 | Bars | GLM 5V Turbo | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 1 | 2 | ||
| Reasoning | 3 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 4 | 0 | ||
| Web design | 7 | 1 | ||
| General | 15 | 1 | ||
| All shared boards | 38 | 4 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index50
| Benchmark | DeepSeek V4 Pro 0423 | GLM 5V Turbo | Leader |
|---|---|---|---|
| LMArena WebDev | 1447 | 1402 | DeepSeek V4 Pro 0423 |
| AA Coding Index | 59.4 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveCodeBench (Vals AI) | 87.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SWE-bench Verified (Vals AI) | 77.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 4 (Vals AI) | 11.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Vibe Code Bench (Vals AI) | 49.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Code migration (Vals AI) | 26.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SkillsBench (Vals AI) | 51.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 32.6% | Only GLM 5V Turbo measured |
| FrontierCode | 17.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| WeirdML | 48.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ALE-Bench | 1006.1 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: coding | 1470 | 1466 | DeepSeek V4 Pro 0423 |
| LMArena Text: software and IT | 1469 | 1460 | DeepSeek V4 Pro 0423 |
| LMArena WebDev: React | 1441 | 1386 | DeepSeek V4 Pro 0423 |
| LMArena WebDev: HTML | 1435 | 1356 | DeepSeek V4 Pro 0423 |
| 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 | DeepSeek V4 Pro 0423 | GLM 5V Turbo | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 23.5 | DeepSeek V4 Pro 0423 |
| Epoch Capabilities Index | 149.1 | Not measured | Only DeepSeek V4 Pro 0423 measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index12
| Benchmark | DeepSeek V4 Pro 0423 | GLM 5V Turbo | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building web apps | 1000 | 1124 | GLM 5V Turbo |
| Design Arena: agents building full-stack apps | 948 | 1149 | GLM 5V Turbo |
| 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 | 1059 | 957 | DeepSeek V4 Pro 0423 |
| 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 |
| Finance Agent (Vals AI) | 44.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Tax Agent Bench (Vals AI) | 25.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Legal Research Bench (Vals AI) | 23.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Excel Modeling Benchmark (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| τ²-Bench Telecom | Not measured | 98.5% | Only GLM 5V Turbo measured |
| Vending-Bench 2 | $3,285 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 26 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4 Pro 0423 | GLM 5V Turbo | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 80.9% | Only GLM 5V Turbo measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 17.1% | Only GLM 5V Turbo measured |
| GPQA Diamond (Epoch AI run) | 90.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Chess puzzles | 19.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Mystery game puzzles | 17.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: hard prompts | 1461 | 1443 | DeepSeek V4 Pro 0423 |
| LMArena Text: expert | 1463 | 1453 | DeepSeek V4 Pro 0423 |
| LMArena Text: hard prompts in English | 1466 | 1456 | DeepSeek V4 Pro 0423 |
| 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 | 53.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Pro 0423 | GLM 5V Turbo | Leader |
|---|---|---|---|
| OTIS Mock AIME | 96.7% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| FrontierMath, tiers 1 to 3 | 45.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| FrontierMath, tier 4 | 2.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ProofBench | 16.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: math | 1443 | 1442 | DeepSeek V4 Pro 0423 |
| LMArena Text: mathematical work | 1455 | 1447 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V4 Pro 0423 | GLM 5V Turbo | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LegalBench (Vals AI) | 80.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CaseLaw (Vals AI) | 59.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CorpFin (Vals AI) | 61.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| TaxEval (Vals AI) | 72.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MedCode (Vals AI) | 40.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SimpleQA Verified | 47.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: science | 1470 | 1448 | DeepSeek V4 Pro 0423 |
| LMArena Text: medicine and healthcare | 1466 | 1433 | DeepSeek V4 Pro 0423 |
| LMArena Text: legal and government | 1464 | 1436 | DeepSeek V4 Pro 0423 |
| LMArena Text: business and finance | 1443 | 1435 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index71
| Benchmark | DeepSeek V4 Pro 0423 | GLM 5V Turbo | Leader |
|---|---|---|---|
| Design Arena: websites | 1234 | 1230 | DeepSeek V4 Pro 0423 |
| Design Arena: UI components | 1219 | 1210 | DeepSeek V4 Pro 0423 |
| Design Arena: game dev | 1228 | 1218 | DeepSeek V4 Pro 0423 |
| Design Arena: data viz | 1203 | 1202 | DeepSeek V4 Pro 0423 |
| Design Arena: 3D | 1253 | 1208 | DeepSeek V4 Pro 0423 |
| Design Arena: all code categories | 1240 | 1228 | DeepSeek V4 Pro 0423 |
| Design Arena: SVG | 1139 | 1151 | GLM 5V Turbo |
| Design Arena: ASCII art | 1149 | 1117 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index151
| Benchmark | DeepSeek V4 Pro 0423 | GLM 5V Turbo | Leader |
|---|---|---|---|
| Public Benefits Bench (Vals AI) | 62.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MedScribe (Vals AI) | 75.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| IFBench | Not measured | 61.1% | Only GLM 5V Turbo measured |
| Long context reasoning (AA-LCR) | Not measured | 70.3% | Only GLM 5V Turbo measured |
| DTBench | 90.7% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMCA | 41.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Surface Evolver Bench | 40.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CL-bench Life | 13.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text | 1451 | 1437 | DeepSeek V4 Pro 0423 |
| LMArena Text: instruction following | 1445 | 1423 | DeepSeek V4 Pro 0423 |
| LMArena Text: creative writing | 1446 | 1417 | DeepSeek V4 Pro 0423 |
| LMArena Vision | Not measured | 1264 | Only GLM 5V Turbo measured |
| LMArena Text: English | 1459 | 1454 | DeepSeek V4 Pro 0423 |
| LMArena Text: non-English | 1439 | 1420 | DeepSeek V4 Pro 0423 |
| LMArena Text: Chinese | 1481 | 1491 | GLM 5V Turbo |
| LMArena Text: German | 1458 | 1422 | DeepSeek V4 Pro 0423 |
| LMArena Text: Japanese | 1446 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Korean | 1409 | 1397 | DeepSeek V4 Pro 0423 |
| LMArena Text: French | 1473 | 1443 | DeepSeek V4 Pro 0423 |
| LMArena Text: Spanish | 1457 | 1452 | DeepSeek V4 Pro 0423 |
| LMArena Text: Russian | 1453 | 1432 | DeepSeek V4 Pro 0423 |
| LMArena Text: Polish | 1461 | 1432 | DeepSeek V4 Pro 0423 |
| LMArena Text: multi-turn | 1468 | 1431 | DeepSeek V4 Pro 0423 |
| LMArena Text: longer queries | 1458 | 1438 | DeepSeek V4 Pro 0423 |
| LMArena Text: writing and language | 1447 | 1420 | DeepSeek V4 Pro 0423 |
| LMArena Text: entertainment and media | 1431 | 1407 | DeepSeek V4 Pro 0423 |
| 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 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | 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 V4 Pro 0423 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 | DeepSeek V4 Pro 0423 | Z.ai GLM 5V Turbo |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | No public weights listed |
| Released | April 24, 2026 | April 1, 2026 |
| Context window | 1.05M | 203K |
| Input $/Mtok | $1.358 | $1.20 |
| Output $/Mtok | $2.976 | $4.00 |
| Coding index | 38.7 | Not benchmarked |
| Independent measurements behind it | 5 | 1 |
| Cost per solved task | $0.57 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than GLM 5V Turbo for coding?
Only DeepSeek V4 Pro 0423 has a coding score, 38.7 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding. Pick GLM 5V Turbo for work measured by Design Arena: agents building web apps or Design Arena: agents building full-stack apps, where it leads.
Which is cheaper, DeepSeek V4 Pro 0423 or GLM 5V Turbo?
DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; GLM 5V Turbo costs $1.20 and $4.00. Cost per solved task is known for DeepSeek V4 Pro 0423 only.
Which has the bigger context window, DeepSeek V4 Pro 0423 or GLM 5V Turbo?
DeepSeek V4 Pro 0423, with 1.05M tokens against 203K.
How many benchmarks is the DeepSeek V4 Pro 0423 vs GLM 5V Turbo comparison based on?
42 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0423's coding index rests on 5 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.