DeepSeek V4 Pro 0423 vs GLM 4.5
Updated October 21, 2026. 44 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 35.3 for DeepSeek V4 Pro 0423.
Pick DeepSeek V4 Pro 0423 when output tokens cost 1.2x less, or when the context window is larger, 1.05M against 131K tokens.
- Shared results
- 44
- DeepSeek V4 Pro 0423 only
- 39
- GLM 4.5 only
- 16
- Like-for-like categories
- 7 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
GLM 4.5
GLM 4.5 scores 64.2 against 35.3.
One benchmark behind each score
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 30.4 against 12.8.
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
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 89.4% against 72.2%.
Same board, both measured
Math
AIME
No call
Only GLM 4.5 has a AIME result, 86.7%.
Directional only
Knowledge
MMLU Pro
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 87.3% against 81.2%.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 1235 against 1170.
Same board, both measured
Long documents
Listed context window
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 takes 1.05M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 4.5
DeepSeek V4 Pro 0423 $0.0019, GLM 4.5 $0.0017 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 35.3 from 1 benchmark, GLM 4.5 64.2 from 1 benchmark.
- Widest category
- Intelligence, 17.6 points apart on average over 1 shared benchmark.
- Widest single gap
- ALE-Bench: DeepSeek V4 Pro 0423 1006.1 against GLM 4.5 344.8, 22.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $1.9105 against GLM 4.5 $2.20 per million output tokens, 1.2x apart.
- Context window
- DeepSeek V4 Pro 0423 takes 1.05M tokens, GLM 4.5 131K.
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 4.5 | Ties |
|---|---|---|---|---|
| Coding | 5 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 4 | 0 | ||
| Math | 2 | 0 | ||
| Knowledge | 7 | 1 | ||
| Web design | 7 | 0 | ||
| General | 17 | 0 | ||
| All shared boards | 43 | 1 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index50
| Benchmark | DeepSeek V4 Pro 0423 | GLM 4.5 | Leader |
|---|---|---|---|
| SWE-bench Verified | Not measured | 64.2% | Only GLM 4.5 measured |
| LMArena WebDev | 1447 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| AA Coding Index | 59.4 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveCodeBench (Vals AI) | 87.5% | 67.5% | DeepSeek V4 Pro 0423 |
| 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 |
| LiveCodeBench (Artificial Analysis run) | Not measured | 73.8% | Only GLM 4.5 measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 22.0% | Only GLM 4.5 measured |
| FrontierCode | 17.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| WeirdML | 48.9% | 40.6% | DeepSeek V4 Pro 0423 |
| ALE-Bench | 1006.1 | 344.8 | DeepSeek V4 Pro 0423 |
| LMArena Text: coding | 1470 | 1434 | DeepSeek V4 Pro 0423 |
| LMArena Text: software and IT | 1469 | 1440 | DeepSeek V4 Pro 0423 |
| LMArena WebDev: React | 1441 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena WebDev: HTML | 1435 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 18 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Pro 0423 | GLM 4.5 | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 12.8 | DeepSeek V4 Pro 0423 |
| Epoch Capabilities Index | 149.1 | Not measured | Only DeepSeek V4 Pro 0423 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | GLM 4.5 | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building web apps | 1000 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building full-stack apps | 948 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building Godot games | 1059 | Not measured | Only DeepSeek V4 Pro 0423 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 | 43.0% | Only GLM 4.5 measured |
| Vending-Bench 2 | $3,285 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V4 Pro 0423 | GLM 4.5 | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | 72.2% | DeepSeek V4 Pro 0423 |
| GPQA Diamond (Artificial Analysis run) | Not measured | 78.2% | Only GLM 4.5 measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 13.0% | Only GLM 4.5 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 | 1429 | DeepSeek V4 Pro 0423 |
| LMArena Text: expert | 1463 | 1433 | DeepSeek V4 Pro 0423 |
| LMArena Text: hard prompts in English | 1466 | 1429 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Pro 0423 | GLM 4.5 | Leader |
|---|---|---|---|
| AIME | Not measured | 86.7% | Only GLM 4.5 measured |
| MATH 500 (Vals AI) | Not measured | 94.0% | Only GLM 4.5 measured |
| MGSM (Vals AI) | Not measured | 90.8% | Only GLM 4.5 measured |
| AA Math Index | Not measured | 73.7 | Only GLM 4.5 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 73.7% | Only GLM 4.5 measured |
| 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 | 1427 | DeepSeek V4 Pro 0423 |
| LMArena Text: mathematical work | 1455 | 1430 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 2 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index71
| Benchmark | DeepSeek V4 Pro 0423 | GLM 4.5 | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | 81.2% | DeepSeek V4 Pro 0423 |
| LegalBench (Vals AI) | 80.3% | 75.6% | DeepSeek V4 Pro 0423 |
| CaseLaw (Vals AI) | 59.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CorpFin (Vals AI) | 61.4% | 61.0% | DeepSeek V4 Pro 0423 |
| TaxEval (Vals AI) | 72.1% | 72.4% | GLM 4.5 |
| MedQA (Vals AI) | Not measured | 90.0% | Only GLM 4.5 measured |
| MedCode (Vals AI) | 40.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MMLU Pro (Artificial Analysis run) | Not measured | 83.5% | Only GLM 4.5 measured |
| SimpleQA Verified | 47.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: science | 1470 | 1444 | DeepSeek V4 Pro 0423 |
| LMArena Text: medicine and healthcare | 1466 | 1455 | DeepSeek V4 Pro 0423 |
| LMArena Text: legal and government | 1464 | 1438 | DeepSeek V4 Pro 0423 |
| LMArena Text: business and finance | 1443 | 1420 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 4 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | DeepSeek V4 Pro 0423 | GLM 4.5 | Leader |
|---|---|---|---|
| Design Arena: websites | 1235 | 1170 | DeepSeek V4 Pro 0423 |
| Design Arena: UI components | 1221 | 1144 | DeepSeek V4 Pro 0423 |
| Design Arena: game dev | 1229 | 1145 | DeepSeek V4 Pro 0423 |
| Design Arena: data viz | 1203 | 1159 | DeepSeek V4 Pro 0423 |
| Design Arena: 3D | 1254 | 1170 | DeepSeek V4 Pro 0423 |
| Design Arena: all code categories | 1240 | 1165 | DeepSeek V4 Pro 0423 |
| Design Arena: SVG | 1139 | 1096 | DeepSeek V4 Pro 0423 |
| Design Arena: ASCII art | 1148 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index170
| Benchmark | DeepSeek V4 Pro 0423 | GLM 4.5 | 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 | 44.1% | Only GLM 4.5 measured |
| Long context reasoning (AA-LCR) | Not measured | 52.7% | Only GLM 4.5 measured |
| DTBench | 90.7% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMCA | 41.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Creative writing (Lech Mazur) | Not measured | 73.4% | Only GLM 4.5 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 | 1430 | DeepSeek V4 Pro 0423 |
| LMArena Text: instruction following | 1445 | 1404 | DeepSeek V4 Pro 0423 |
| LMArena Text: creative writing | 1446 | 1395 | DeepSeek V4 Pro 0423 |
| LMArena Text: English | 1459 | 1437 | DeepSeek V4 Pro 0423 |
| LMArena Text: non-English | 1439 | 1417 | DeepSeek V4 Pro 0423 |
| LMArena Text: Chinese | 1481 | 1465 | DeepSeek V4 Pro 0423 |
| LMArena Text: German | 1458 | 1407 | DeepSeek V4 Pro 0423 |
| LMArena Text: Japanese | 1446 | 1415 | DeepSeek V4 Pro 0423 |
| LMArena Text: Korean | 1409 | 1380 | DeepSeek V4 Pro 0423 |
| LMArena Text: French | 1473 | 1418 | DeepSeek V4 Pro 0423 |
| LMArena Text: Spanish | 1457 | 1454 | DeepSeek V4 Pro 0423 |
| LMArena Text: Russian | 1453 | 1413 | DeepSeek V4 Pro 0423 |
| LMArena Text: Polish | 1461 | 1422 | DeepSeek V4 Pro 0423 |
| LMArena Text: multi-turn | 1468 | 1415 | DeepSeek V4 Pro 0423 |
| LMArena Text: longer queries | 1458 | 1412 | DeepSeek V4 Pro 0423 |
| LMArena Text: writing and language | 1447 | 1403 | DeepSeek V4 Pro 0423 |
| LMArena Text: entertainment and media | 1431 | 1404 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | GLM 4.5 | 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 4.5 costs less
Repository review
50K input + 3K output tokens
GLM 4.5 costs less
Agent loop
200K input + 10K output tokens
GLM 4.5 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 4.5 |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | April 24, 2026 | July 25, 2025 |
| Context window | 1.05M | 131K |
| Input $/Mtok | $0.9553 | $0.60 |
| Output $/Mtok | $1.9105 | $2.20 |
| Coding index | 35.3 | 64.2 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.57 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than GLM 4.5 for coding?
Pick GLM 4.5 for coding: it scores 64.2 on the Vibe Coding Index against 35.3 for DeepSeek V4 Pro 0423. Pick DeepSeek V4 Pro 0423 when output tokens cost 1.2x less, or when the context window is larger, 1.05M against 131K tokens.
Which is cheaper, DeepSeek V4 Pro 0423 or GLM 4.5?
DeepSeek V4 Pro 0423 costs $0.9553 per million input tokens and $1.9105 per million output tokens; GLM 4.5 costs $0.60 and $2.20. Cost per solved task is known for DeepSeek V4 Pro 0423 only.
Which has the bigger context window, DeepSeek V4 Pro 0423 or GLM 4.5?
DeepSeek V4 Pro 0423, with 1.05M tokens against 131K.
How many benchmarks is the DeepSeek V4 Pro 0423 vs GLM 4.5 comparison based on?
44 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4 Pro 0423's coding index rests on 1 independent benchmark and GLM 4.5's on 1. Where only one model has a result, the page says so instead of naming a winner.