DeepSeek V3.2 Exp vs GLM 5.3
GLM 5.3 leads DeepSeek V3.2 Exp on the Artificial Analysis Intelligence Index, 44.8 to 16.6, and DeepSeek V3.2 Exp costs 10.7x less per output token ($0.41 vs $4.40 per million). Updated October 22, 2026; 45 benchmarks have a result for both.
DeepSeek V3.2 Exp or GLM 5.3: which to pick
Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; DeepSeek V3.2 Exp has none, so the two cannot be ranked on coding.
Pick DeepSeek V3.2 Exp for output tokens 10.7x cheaper ($0.41 vs $4.40 per million) and work measured by DTBench, where it leads.
- Shared results
- 45
- DeepSeek V3.2 Exp only
- 14
- GLM 5.3 only
- 55
- Like-for-like categories
- 7 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 GLM 5.3 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 16.6.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.3 has a AA Agentic Index result, 53.1.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 5.3 has a GPQA Diamond result, 88.1%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 has a MMLU Pro result, 86.8%.
Directional only
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1178.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
GLM 5.3
GLM 5.3 takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V3.2 Exp
DeepSeek V3.2 Exp $0.0005, GLM 5.3 $0.0036 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 has the number, $0.36.
Directional only
At a glance
- Coding index
- DeepSeek V3.2 Exp not benchmarked, GLM 5.3 70.6 from 6 benchmarks.
- Widest category
- Coding, 31.4 points apart on average over 5 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: DeepSeek V3.2 Exp 1281 against GLM 5.3 1595, 52.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.2 Exp $0.41 against GLM 5.3 $4.40 per million output tokens, 10.7x apart.
- Context window
- DeepSeek V3.2 Exp takes 164K tokens, GLM 5.3 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 | Ties |
|---|---|---|---|---|
| Coding | 0 | 5 | ||
| Intelligence | 0 | 2 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 7 | ||
| General | 1 | 19 | ||
| All shared boards | 1 | 44 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index05
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 | Leader |
|---|---|---|---|
| Aider polyglot | 74.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| LiveBench coding | Not measured | 82.8% | Only GLM 5.3 measured |
| LMArena WebDev | 1272 | 1621 | GLM 5.3 |
| AA Coding Index | Not measured | 74.8 | Only GLM 5.3 measured |
| LiveCodeBench (Vals AI) | Not measured | 80.5% | Only GLM 5.3 measured |
| SWE-bench Verified (Vals AI) | Not measured | 95.4% | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 38.9% | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | Not measured | 78.1% | Only GLM 5.3 measured |
| Code migration (Vals AI) | Not measured | 44.2% | Only GLM 5.3 measured |
| ProgramBench (Vals AI) | Not measured | 1.5% | Only GLM 5.3 measured |
| IOI (Vals AI) | Not measured | 68.4% | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 71.5% | Only GLM 5.3 measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 20.0% | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | Not measured | 47.5% | Only GLM 5.3 measured |
| CyberBench (Vals AI) | Not measured | 72.1% | Only GLM 5.3 measured |
| LiveCodeBench (Artificial Analysis run) | 78.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| SciCode | Not measured | 59.0% | Only GLM 5.3 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 31.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| FrontierCode | Not measured | 40.1% | Only GLM 5.3 measured |
| FrontierSWE | Not measured | 30.2% | Only GLM 5.3 measured |
| WeirdML | 39.5% | 75.4% | GLM 5.3 |
| ALE-Bench | Not measured | 1317.4 | Only GLM 5.3 measured |
| LMArena Text: coding | 1438 | 1497 | GLM 5.3 |
| LMArena Text: software and IT | 1438 | 1493 | GLM 5.3 |
| LMArena WebDev: React | Not measured | 1641 | Only GLM 5.3 measured |
| LMArena WebDev: HTML | 1281 | 1595 | GLM 5.3 |
| Neither model has a result on the other 13 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 | Leader |
|---|---|---|---|
| AA Intelligence Index | 16.6 | 44.8 | GLM 5.3 |
| Epoch Capabilities Index | 144.9 | 156.0 | GLM 5.3 |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 53.1 | Only GLM 5.3 measured |
| Design Arena: agents building mobile apps | Not measured | 1194 | Only GLM 5.3 measured |
| Design Arena: HTML slides | Not measured | 1189 | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | Not measured | 1230 | Only GLM 5.3 measured |
| LiveBench research | Not measured | 73.2% | Only GLM 5.3 measured |
| LiveBench browser | Not measured | 0.0% | Only GLM 5.3 measured |
| LiveBench long agentic | Not measured | 35.9% | Only GLM 5.3 measured |
| SREBench (Vals AI) | Not measured | 9.2% | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | Not measured | 55.8% | Only GLM 5.3 measured |
| Tax Agent Bench (Vals AI) | Not measured | 39.7% | Only GLM 5.3 measured |
| Legal Research Bench (Vals AI) | Not measured | 49.0% | Only GLM 5.3 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | Not measured | 14.6% | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 56.3% | Only GLM 5.3 measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 5.7% | Only GLM 5.3 measured |
| τ²-Bench Telecom | 33.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| APEX-Agents | Not measured | 56.6% | Only GLM 5.3 measured |
| TheAgentCompany | 42.9% | Not measured | Only DeepSeek V3.2 Exp measured |
| Vending-Bench 2 | Not measured | $8,164 | Only GLM 5.3 measured |
| Berkeley Function Calling Leaderboard (BFCL) | 56.7% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 88.1% | Only GLM 5.3 measured |
| LiveBench reasoning | Not measured | 85.8% | Only GLM 5.3 measured |
| MysteryMechanism (Vals AI) | Not measured | 23.0% | Only GLM 5.3 measured |
| GPQA Diamond (Artificial Analysis run) | 79.7% | 91.7% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 14.9% | 42.3% | GLM 5.3 |
| GPQA Diamond (Epoch AI run) | Not measured | 90.9% | Only GLM 5.3 measured |
| Chess puzzles | Not measured | 37.0% | Only GLM 5.3 measured |
| Mystery game puzzles | Not measured | 33.0% | Only GLM 5.3 measured |
| LMArena Text: hard prompts | 1429 | 1490 | GLM 5.3 |
| LMArena Text: expert | 1422 | 1514 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1444 | 1496 | GLM 5.3 |
| Kagi LLM Benchmark | 52.2% | Not measured | Only DeepSeek V3.2 Exp measured |
| Neither model has a result on the other 10 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 | 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 | 91.1% | Only GLM 5.3 measured |
| FrontierMath, tiers 1 to 3 | Not measured | 68.8% | Only GLM 5.3 measured |
| FrontierMath, tier 4 | Not measured | 29.3% | Only GLM 5.3 measured |
| ProofBench | Not measured | 49.0% | Only GLM 5.3 measured |
| LMArena Text: math | 1422 | 1495 | GLM 5.3 |
| LMArena Text: mathematical work | 1433 | 1497 | GLM 5.3 |
| 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 | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 86.8% | Only GLM 5.3 measured |
| LegalBench (Vals AI) | Not measured | 84.8% | Only GLM 5.3 measured |
| TaxEval (Vals AI) | Not measured | 72.4% | Only GLM 5.3 measured |
| MedCode (Vals AI) | Not measured | 42.9% | Only GLM 5.3 measured |
| MMLU Pro (Artificial Analysis run) | 85.0% | Not measured | Only DeepSeek V3.2 Exp measured |
| SimpleQA Verified | Not measured | 41.0% | Only GLM 5.3 measured |
| LMArena Text: science | 1443 | 1498 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1468 | 1481 | GLM 5.3 |
| LMArena Text: legal and government | 1419 | 1473 | GLM 5.3 |
| LMArena Text: business and finance | 1412 | 1459 | GLM 5.3 |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 | Leader |
|---|---|---|---|
| Design Arena: websites | 1178 | 1295 | GLM 5.3 |
| Design Arena: UI components | 1164 | 1314 | GLM 5.3 |
| Design Arena: game dev | 1144 | 1311 | GLM 5.3 |
| Design Arena: data viz | 1152 | 1260 | GLM 5.3 |
| Design Arena: 3D | 1148 | 1332 | GLM 5.3 |
| Design Arena: all code categories | 1170 | 1304 | GLM 5.3 |
| Design Arena: SVG | 1031 | 1294 | GLM 5.3 |
| Design Arena: ASCII art | Not measured | 1208 | Only GLM 5.3 measured |
| LiveBench design | Not measured | 77.3% | Only GLM 5.3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index119
| Benchmark | DeepSeek V3.2 Exp | GLM 5.3 | Leader |
|---|---|---|---|
| Vals Index | Not measured | 59.7% | Only GLM 5.3 measured |
| LiveBench data analysis | Not measured | 70.2% | Only GLM 5.3 measured |
| Public Benefits Bench (Vals AI) | Not measured | 68.5% | Only GLM 5.3 measured |
| MedScribe (Vals AI) | Not measured | 88.8% | Only GLM 5.3 measured |
| IFBench | 54.1% | Not measured | Only DeepSeek V3.2 Exp measured |
| Long context reasoning (AA-LCR) | 72.3% | 79.7% | GLM 5.3 |
| DTBench | 87.7% | 87.7% | DeepSeek V3.2 Exp |
| LMCA | 29.1% | 55.5% | GLM 5.3 |
| 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 | 1474 | GLM 5.3 |
| LMArena Text: instruction following | 1401 | 1479 | GLM 5.3 |
| LMArena Text: creative writing | 1404 | 1458 | GLM 5.3 |
| LMArena Text: English | 1441 | 1482 | GLM 5.3 |
| LMArena Text: non-English | 1404 | 1459 | GLM 5.3 |
| LMArena Text: Chinese | 1461 | 1532 | GLM 5.3 |
| LMArena Text: German | 1440 | 1488 | GLM 5.3 |
| LMArena Text: Japanese | 1331 | 1460 | GLM 5.3 |
| LMArena Text: Korean | 1372 | 1460 | GLM 5.3 |
| LMArena Text: French | 1434 | 1503 | GLM 5.3 |
| LMArena Text: Spanish | 1441 | 1462 | GLM 5.3 |
| LMArena Text: Russian | 1412 | 1469 | GLM 5.3 |
| LMArena Text: Polish | 1435 | 1466 | GLM 5.3 |
| LMArena Text: multi-turn | 1422 | 1472 | GLM 5.3 |
| LMArena Text: longer queries | 1418 | 1483 | GLM 5.3 |
| LMArena Text: writing and language | 1407 | 1469 | GLM 5.3 |
| LMArena Text: entertainment and media | 1407 | 1447 | GLM 5.3 |
| Neither model has a result on the other 18 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 | 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 5.3 |
|---|---|---|
| Maker | DeepSeek | Z.ai |
| Weights | Open weights | Open weights |
| Released | September 29, 2025 | August 18, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.27 | $1.40 |
| Output $/Mtok | $0.41 | $4.40 |
| Coding index | Not benchmarked | 70.6 |
| Independent measurements behind it | 1 | 6 |
| Cost per solved task | Not computable | $0.36 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.2 Exp better than GLM 5.3 for coding?
Only GLM 5.3 has a coding score, 70.6 on the Vibe Coding Index; DeepSeek V3.2 Exp has none, so the two cannot be ranked on coding. Pick DeepSeek V3.2 Exp for output tokens 10.7x cheaper ($0.41 vs $4.40 per million) and work measured by DTBench, where it leads.
Which is cheaper, DeepSeek V3.2 Exp or GLM 5.3?
DeepSeek V3.2 Exp costs $0.27 per million input tokens and $0.41 per million output tokens; GLM 5.3 costs $1.40 and $4.40. Cost per solved task is known for GLM 5.3 only.
Which has the bigger context window, DeepSeek V3.2 Exp or GLM 5.3?
GLM 5.3, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.2 Exp vs GLM 5.3 comparison based on?
45 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 5.3's on 6. Where only one model has a result, the page says so instead of naming a winner.