DeepSeek V3.2 Exp vs GLM 5.3 Flash
Updated October 21, 2026. 42 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 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 55.0 for DeepSeek V3.2 Exp.
Pick DeepSeek V3.2 Exp when output tokens cost 1.2x less.
- Shared results
- 42
- DeepSeek V3.2 Exp only
- 15
- GLM 5.3 Flash only
- 60
- 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 5.3 Flash
GLM 5.3 Flash scores 71.0 against 55.0.
2 benchmarks behind each score
Intelligence
AA Intelligence Index
GLM 5.3 Flash
GLM 5.3 Flash leads, 41.8 against 16.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 Flash has a AA Agentic Index result, 50.9.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 Flash has a GPQA Diamond result, 86.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 Flash has a MMLU Pro result, 86.1%.
Directional only
Web design
Design Arena: websites
GLM 5.3 Flash
GLM 5.3 Flash leads, 1272 against 1179.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 Flash has a Vals Index result, 50.9%.
Directional only
Long documents
Listed context window
GLM 5.3 Flash
GLM 5.3 Flash takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3 Flash
DeepSeek V3.2 Exp $0.0005, GLM 5.3 Flash $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 Flash has the number, $0.02.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 Exp 55.0 from 2 benchmarks, GLM 5.3 Flash 71.0 from 2 benchmarks.
- Widest category
- Coding, 29.7 points apart on average over 4 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: DeepSeek V3.2 Exp 1281 against GLM 5.3 Flash 1577, 49.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against GLM 5.3 Flash $0.50 per million output tokens, 1.2x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, GLM 5.3 Flash 1.05M.
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 5.3 Flash | Ties |
|---|---|---|---|---|
| Coding | 0 | 4 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 7 | ||
| General | 0 | 18 | ||
| All shared boards | 0 | 42 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index04
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LiveBench coding | Not measured | 80.6% | Only GLM 5.3 Flash measured |
| LMArena WebDev | 1272 | 1607 | GLM 5.3 Flash |
| AA Coding Index | Not measured | 71.5 | Only GLM 5.3 Flash measured |
| LiveCodeBench (Vals AI) | Not measured | 80.5% | Only GLM 5.3 Flash measured |
| SWE-bench Verified (Vals AI) | Not measured | 92.0% | Only GLM 5.3 Flash measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 37.4% | Only GLM 5.3 Flash measured |
| Vibe Code Bench (Vals AI) | Not measured | 30.8% | Only GLM 5.3 Flash measured |
| Code migration (Vals AI) | Not measured | 20.5% | Only GLM 5.3 Flash measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only GLM 5.3 Flash measured |
| IOI (Vals AI) | Not measured | 52.5% | Only GLM 5.3 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 62.9% | Only GLM 5.3 Flash measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 16.0% | Only GLM 5.3 Flash measured |
| SkillsBench (Vals AI) | Not measured | 40.2% | Only GLM 5.3 Flash measured |
| CyberBench (Vals AI) | Not measured | 67.9% | Only GLM 5.3 Flash measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| SciCode | Not measured | 51.6% | Only GLM 5.3 Flash measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| FrontierCode | Not measured | 31.8% | Only GLM 5.3 Flash measured |
| FrontierSWE | Not measured | 18.1% | Only GLM 5.3 Flash measured |
| WeirdML | 39.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| ALE-Bench | Not measured | 303.6 | Only GLM 5.3 Flash measured |
| LMArena Text: coding | 1438 | 1507 | GLM 5.3 Flash |
| LMArena Text: software and IT | 1438 | 1500 | GLM 5.3 Flash |
| LMArena WebDev: React | Not measured | 1624 | Only GLM 5.3 Flash measured |
| LMArena WebDev: HTML | 1281 | 1577 | GLM 5.3 Flash |
| LMArena WebDev: from an image | Not measured | 1578 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 11 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 Flash | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 41.8 | GLM 5.3 Flash |
| Epoch Capabilities Index | 144.9 | 153.1 | GLM 5.3 Flash |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index14 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 Flash | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 50.9 | Only GLM 5.3 Flash measured |
| LiveBench research | Not measured | 70.1% | Only GLM 5.3 Flash measured |
| LiveBench browser | Not measured | 84.0% | Only GLM 5.3 Flash measured |
| LiveBench long agentic | Not measured | 28.4% | Only GLM 5.3 Flash measured |
| SREBench (Vals AI) | Not measured | 0.8% | Only GLM 5.3 Flash measured |
| Finance Agent (Vals AI) | Not measured | 57.9% | Only GLM 5.3 Flash measured |
| Tax Agent Bench (Vals AI) | Not measured | 31.6% | Only GLM 5.3 Flash measured |
| Legal Research Bench (Vals AI) | Not measured | 45.2% | Only GLM 5.3 Flash measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 10.4% | Only GLM 5.3 Flash measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 55.9% | Only GLM 5.3 Flash measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 2.9% | Only GLM 5.3 Flash measured |
| τ²-Bench Telecom | 33.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| APEX-Agents | Not measured | 52.8% | Only GLM 5.3 Flash measured |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 25 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 Flash | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 86.4% | Only GLM 5.3 Flash measured |
| LiveBench reasoning | Not measured | 77.6% | Only GLM 5.3 Flash measured |
| MMMU Pro (Vals AI) | Not measured | 86.0% | Only GLM 5.3 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 79.7% | 91.2% | GLM 5.3 Flash |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 39.9% | GLM 5.3 Flash |
| GPQA Diamond (Epoch AI run) | Not measured | 90.2% | Only GLM 5.3 Flash measured |
| ARC-AGI | Not measured | 91.0% | Only GLM 5.3 Flash measured |
| ARC-AGI-2 | Not measured | 65.8% | Only GLM 5.3 Flash measured |
| Chess puzzles | Not measured | 21.0% | Only GLM 5.3 Flash measured |
| Mystery game puzzles | Not measured | 8.0% | Only GLM 5.3 Flash measured |
| LMArena Text: hard prompts | 1429 | 1490 | GLM 5.3 Flash |
| LMArena Text: expert | 1422 | 1511 | GLM 5.3 Flash |
| LMArena Text: hard prompts in English | 1444 | 1488 | GLM 5.3 Flash |
| LMArena Vision: diagrams | Not measured | 1317 | Only GLM 5.3 Flash measured |
| LMArena Vision: homework | Not measured | 1318 | Only GLM 5.3 Flash measured |
| Neither model has a result on the other 6 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 Flash | 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 |
| OTIS Mock AIME | Not measured | 93.9% | Only GLM 5.3 Flash measured |
| FrontierMath, tiers 1 to 3 | Not measured | 55.8% | Only GLM 5.3 Flash measured |
| FrontierMath, tier 4 | Not measured | 17.1% | Only GLM 5.3 Flash measured |
| ProofBench | Not measured | 21.0% | Only GLM 5.3 Flash measured |
| LMArena Text: math | 1422 | 1503 | GLM 5.3 Flash |
| LMArena Text: mathematical work | 1433 | 1497 | GLM 5.3 Flash |
| Neither model has a result on the other 5 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 Flash | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.1% | Only GLM 5.3 Flash measured |
| LegalBench (Vals AI) | Not measured | 83.9% | Only GLM 5.3 Flash measured |
| TaxEval (Vals AI) | Not measured | 75.6% | Only GLM 5.3 Flash measured |
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text: science | 1443 | 1484 | GLM 5.3 Flash |
| LMArena Text: medicine and healthcare | 1468 | 1475 | GLM 5.3 Flash |
| LMArena Text: legal and government | 1419 | 1473 | GLM 5.3 Flash |
| LMArena Text: business and finance | 1412 | 1460 | GLM 5.3 Flash |
| Neither model has a result on the other 9 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Design Arena: websites | 1179 | 1272 | GLM 5.3 Flash |
| Design Arena: UI components | 1166 | 1316 | GLM 5.3 Flash |
| Design Arena: game dev | 1145 | 1279 | GLM 5.3 Flash |
| Design Arena: data viz | 1152 | 1262 | GLM 5.3 Flash |
| Design Arena: 3D | 1148 | 1316 | GLM 5.3 Flash |
| Design Arena: all code categories | 1171 | 1282 | GLM 5.3 Flash |
| Design Arena: SVG | 1031 | 1284 | GLM 5.3 Flash |
| Design Arena: ASCII art | Not measured | 1269 | Only GLM 5.3 Flash measured |
| LiveBench design | Not measured | 77.2% | Only GLM 5.3 Flash measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index018
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 Flash | Leader |
|---|---|---|---|
| Vals Index | Not measured | 50.9% | Only GLM 5.3 Flash measured |
| LiveBench data analysis | Not measured | 76.4% | Only GLM 5.3 Flash measured |
| MedScribe (Vals AI) | Not measured | 88.9% | Only GLM 5.3 Flash measured |
| IFBench | 54.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Long context reasoning (AA-LCR) | 72.3% | 80.0% | GLM 5.3 Flash |
| 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 |
| Surface Evolver Bench | Not measured | 52.5% | Only GLM 5.3 Flash measured |
| CL-bench Life | 9.5% | Not measured | Only DeepSeek V3.2 Exp measured |
| LMArena Text | 1425 | 1471 | GLM 5.3 Flash |
| LMArena Text: instruction following | 1401 | 1478 | GLM 5.3 Flash |
| LMArena Text: creative writing | 1404 | 1441 | GLM 5.3 Flash |
| LMArena Vision | Not measured | 1294 | Only GLM 5.3 Flash measured |
| LMArena Text: English | 1441 | 1474 | GLM 5.3 Flash |
| LMArena Text: non-English | 1404 | 1462 | GLM 5.3 Flash |
| LMArena Text: Chinese | 1461 | 1531 | GLM 5.3 Flash |
| LMArena Text: German | 1440 | 1462 | GLM 5.3 Flash |
| LMArena Text: Japanese | 1331 | 1444 | GLM 5.3 Flash |
| LMArena Text: Korean | 1372 | 1436 | GLM 5.3 Flash |
| LMArena Text: French | 1434 | 1491 | GLM 5.3 Flash |
| LMArena Text: Spanish | 1441 | 1475 | GLM 5.3 Flash |
| LMArena Text: Russian | 1412 | 1465 | GLM 5.3 Flash |
| LMArena Text: Polish | 1435 | 1450 | GLM 5.3 Flash |
| LMArena Text: multi-turn | 1422 | 1469 | GLM 5.3 Flash |
| LMArena Text: longer queries | 1418 | 1481 | GLM 5.3 Flash |
| LMArena Text: writing and language | 1407 | 1455 | GLM 5.3 Flash |
| LMArena Text: entertainment and media | 1407 | 1440 | GLM 5.3 Flash |
| LMArena Vision: English | Not measured | 1295 | Only GLM 5.3 Flash measured |
| LMArena Vision: Chinese | Not measured | 1363 | Only GLM 5.3 Flash measured |
| LMArena Vision: reading text | Not measured | 1299 | Only GLM 5.3 Flash measured |
| LMArena Vision: humor | Not measured | 1279 | Only GLM 5.3 Flash measured |
| LMArena Vision: creative writing | Not measured | 1270 | Only GLM 5.3 Flash 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 V3.2 Exp | GLM 5.3 Flash | 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 5.3 Flash costs less
Repository review
50K input + 3K output tokens
GLM 5.3 Flash costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 Flash 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 5.3 Flash |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | September 29, 2025 | August 26, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.27 | $0.15 |
| Output $/Mtok | $0.41 | $0.50 |
| Coding index | 55.0 | 71.0 |
| Independent measurements behind it | 2 | 2 |
| Cost per solved task | Not computable | $0.02 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than GLM 5.3 Flash for coding?
Pick GLM 5.3 Flash for coding: it scores 71.0 on the Vibe Coding Index against 55.0 for DeepSeek V3.2 Exp. Pick DeepSeek V3.2 Exp when output tokens cost 1.2x less.
Which is cheaper, DeepSeek V3.2 Exp or GLM 5.3 Flash?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GLM 5.3 Flash costs $0.15 and $0.50. Cost per solved task is known for GLM 5.3 Flash only.
Which has the bigger context window, DeepSeek V3.2 Exp or GLM 5.3 Flash?
GLM 5.3 Flash, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs GLM 5.3 Flash comparison based on?
42 benchmarks have a result for both models as of October 21, 2026. DeepSeek V3.2 Exp's coding index rests on 2 independent benchmarks and GLM 5.3 Flash's on 2. Where only one model has a result, the page says so instead of naming a winner.