GLM 5.3 vs Qwen3.8 Max (0902)
GLM 5.3 leads Qwen3.8 Max (0902) on coding, 70.6 to 62.4 on the Vibe Coding Index, and GLM 5.3 costs 1.4x less per output token ($4.40 vs $6.00 per million). Updated October 22, 2026; 91 benchmarks have a result for both.
GLM 5.3 or Qwen3.8 Max (0902): which to pick
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 62.4 for Qwen3.8 Max (0902).
Pick Qwen3.8 Max (0902) for work measured by LMArena WebDev or AA Coding Index, where it leads.
- Shared results
- 91
- GLM 5.3 only
- 9
- Qwen3.8 Max (0902) 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
GLM 5.3
GLM 5.3 scores 70.6 against 62.4.
6 and 5 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 44.8.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 56.0 against 53.1.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 93.7% against 88.1%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 88.6% against 86.8%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1275.
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 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5.3
GLM 5.3 $0.0036, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.3
GLM 5.3 $0.36, Qwen3.8 Max (0902) $1.32.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.3 70.6 from 6 benchmarks, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
- Widest category
- Coding, 8.6 points apart on average over 20 shared benchmarks.
- Widest single gap
- CyberBench (Vals AI): GLM 5.3 72.1% against Qwen3.8 Max (0902) 28.6%, 43.5 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $4.40 against Qwen3.8 Max (0902) $6.00 per million output tokens, 1.4x apart.
- Context window
- GLM 5.3 takes 1.05M tokens, Qwen3.8 Max (0902) 1M.
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 | GLM 5.3 | Bars | Qwen3.8 Max (0902) | Ties |
|---|---|---|---|---|
| Coding | 12 | 8 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 4 | 8 | ||
| Reasoning | 1 | 10 | ||
| Math | 1 | 5 | ||
| Knowledge | 2 | 7 | ||
| Web design | 3 | 5 | ||
| General | 5 | 18 | ||
| All shared boards | 28 | 63 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index128
| Benchmark | GLM 5.3 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench coding | 82.8% | 60.3% | GLM 5.3 |
| LMArena WebDev | 1621 | 1674 | Qwen3.8 Max (0902) |
| AA Coding Index | 74.8 | 76.2 | Qwen3.8 Max (0902) |
| LiveCodeBench (Vals AI) | 80.5% | 87.8% | Qwen3.8 Max (0902) |
| SWE-bench Verified (Vals AI) | 95.4% | 85.6% | GLM 5.3 |
| Terminal-Bench 4 (Vals AI) | 38.9% | 34.3% | GLM 5.3 |
| Vibe Code Bench (Vals AI) | 78.1% | 64.7% | GLM 5.3 |
| Code migration (Vals AI) | 44.2% | 24.0% | GLM 5.3 |
| ProgramBench (Vals AI) | 1.5% | 0.0% | GLM 5.3 |
| IOI (Vals AI) | 68.4% | 68.9% | Qwen3.8 Max (0902) |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | 67.4% | GLM 5.3 |
| SWE-bench Multimodal (Vals AI) | Not measured | 42.5% | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | 12.8% | GLM 5.3 |
| SkillsBench (Vals AI) | 47.5% | 42.0% | GLM 5.3 |
| CyberBench (Vals AI) | 72.1% | 28.6% | GLM 5.3 |
| SciCode | 59.0% | 53.2% | GLM 5.3 |
| FrontierCode | 40.1% | Not measured | Only GLM 5.3 measured |
| FrontierSWE | 30.2% | 17.8% | GLM 5.3 |
| WeirdML | 75.4% | Not measured | Only GLM 5.3 measured |
| ALE-Bench | 1317.4 | Not measured | Only GLM 5.3 measured |
| LMArena Text: coding | 1497 | 1502 | Qwen3.8 Max (0902) |
| LMArena Text: software and IT | 1493 | 1498 | Qwen3.8 Max (0902) |
| LMArena WebDev: React | 1641 | 1692 | Qwen3.8 Max (0902) |
| LMArena WebDev: HTML | 1595 | 1649 | Qwen3.8 Max (0902) |
| LMArena WebDev: from an image | Not measured | 1637 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 14 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5.3 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 45.4 | Qwen3.8 Max (0902) |
| Epoch Capabilities Index | 156.0 | 157.4 | Qwen3.8 Max (0902) |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index48
| Benchmark | GLM 5.3 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | 56.0 | Qwen3.8 Max (0902) |
| Design Arena: agents building web apps | Not measured | 1300 | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building full-stack apps | Not measured | 1305 | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building mobile apps | 1194 | 1224 | Qwen3.8 Max (0902) |
| Design Arena: agents building Android apps | Not measured | 1196 | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building Godot games | Not measured | 1234 | Only Qwen3.8 Max (0902) measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1230 | 1233 | Qwen3.8 Max (0902) |
| Design Arena: spreadsheets | Not measured | 967 | Only Qwen3.8 Max (0902) measured |
| LiveBench research | 73.2% | 69.9% | GLM 5.3 |
| LiveBench browser | 0.0% | 32.0% | Qwen3.8 Max (0902) |
| LiveBench long agentic | 35.9% | 42.3% | Qwen3.8 Max (0902) |
| SREBench (Vals AI) | 9.2% | Not measured | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | 55.8% | 50.6% | GLM 5.3 |
| Tax Agent Bench (Vals AI) | 39.7% | 32.1% | GLM 5.3 |
| Legal Research Bench (Vals AI) | 49.0% | 47.6% | GLM 5.3 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | 60.1% | Qwen3.8 Max (0902) |
| Terminal-Bench-Science (Vals AI) | 5.7% | 12.9% | Qwen3.8 Max (0902) |
| APEX-Agents | 56.6% | 63.3% | Qwen3.8 Max (0902) |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| LMArena Search | Not measured | 1261 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 19 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index110
| Benchmark | GLM 5.3 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | 93.7% | Qwen3.8 Max (0902) |
| LiveBench reasoning | 85.8% | 88.2% | Qwen3.8 Max (0902) |
| MysteryMechanism (Vals AI) | 23.0% | 23.9% | Qwen3.8 Max (0902) |
| MMMU Pro (Vals AI) | Not measured | 88.0% | Only Qwen3.8 Max (0902) measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 92.8% | Qwen3.8 Max (0902) |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 43.1% | Qwen3.8 Max (0902) |
| GPQA Diamond (Epoch AI run) | 90.9% | 92.7% | Qwen3.8 Max (0902) |
| Chess puzzles | 37.0% | 46.0% | Qwen3.8 Max (0902) |
| Mystery game puzzles | 33.0% | 38.0% | Qwen3.8 Max (0902) |
| LMArena Text: hard prompts | 1490 | 1496 | Qwen3.8 Max (0902) |
| LMArena Text: expert | 1514 | 1507 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1503 | Qwen3.8 Max (0902) |
| LMArena Vision: diagrams | Not measured | 1323 | Only Qwen3.8 Max (0902) measured |
| LMArena Vision: homework | Not measured | 1336 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | GLM 5.3 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| OTIS Mock AIME | 91.1% | 100.0% | Qwen3.8 Max (0902) |
| FrontierMath, tiers 1 to 3 | 68.8% | 74.7% | Qwen3.8 Max (0902) |
| FrontierMath, tier 4 | 29.3% | 46.3% | Qwen3.8 Max (0902) |
| ProofBench | 49.0% | 58.0% | Qwen3.8 Max (0902) |
| LMArena Text: math | 1495 | 1501 | Qwen3.8 Max (0902) |
| LMArena Text: mathematical work | 1497 | 1491 | GLM 5.3 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index27
| Benchmark | GLM 5.3 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | 88.6% | Qwen3.8 Max (0902) |
| LegalBench (Vals AI) | 84.8% | 83.6% | GLM 5.3 |
| CorpFin (Vals AI) | Not measured | 65.8% | Only Qwen3.8 Max (0902) measured |
| TaxEval (Vals AI) | 72.4% | 75.5% | Qwen3.8 Max (0902) |
| MortgageTax (Vals AI) | Not measured | 64.0% | Only Qwen3.8 Max (0902) measured |
| MedCode (Vals AI) | 42.9% | 40.7% | GLM 5.3 |
| SimpleQA Verified | 41.0% | 47.3% | Qwen3.8 Max (0902) |
| LMArena Text: science | 1498 | 1512 | Qwen3.8 Max (0902) |
| LMArena Text: medicine and healthcare | 1481 | 1503 | Qwen3.8 Max (0902) |
| LMArena Text: legal and government | 1473 | 1492 | Qwen3.8 Max (0902) |
| LMArena Text: business and finance | 1459 | 1480 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index35
| Benchmark | GLM 5.3 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1275 | GLM 5.3 |
| Design Arena: UI components | 1314 | 1338 | Qwen3.8 Max (0902) |
| Design Arena: game dev | 1311 | 1312 | Qwen3.8 Max (0902) |
| Design Arena: data viz | 1260 | 1281 | Qwen3.8 Max (0902) |
| Design Arena: 3D | 1332 | 1336 | Qwen3.8 Max (0902) |
| Design Arena: all code categories | 1304 | 1293 | GLM 5.3 |
| Design Arena: SVG | 1294 | Not measured | Only GLM 5.3 measured |
| Design Arena: ASCII art | 1208 | 1200 | GLM 5.3 |
| LiveBench design | 77.3% | 78.6% | Qwen3.8 Max (0902) |
GeneralLike-for-likePublic boards, shown as published, kept out of the index518
| Benchmark | GLM 5.3 | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| Vals Index | 59.7% | Not measured | Only GLM 5.3 measured |
| LiveBench data analysis | 70.2% | 78.4% | Qwen3.8 Max (0902) |
| Public Benefits Bench (Vals AI) | 68.5% | 67.1% | GLM 5.3 |
| SAGE (Vals AI) | Not measured | 51.2% | Only Qwen3.8 Max (0902) measured |
| MedScribe (Vals AI) | 88.8% | 85.0% | GLM 5.3 |
| Vals Multimodal Index | Not measured | 65.4% | Only Qwen3.8 Max (0902) measured |
| Long context reasoning (AA-LCR) | 79.7% | 80.3% | Qwen3.8 Max (0902) |
| DTBench | 87.7% | 92.0% | Qwen3.8 Max (0902) |
| LMCA | 55.5% | 46.2% | GLM 5.3 |
| Furniture assembly | Not measured | 20.0% | Only Qwen3.8 Max (0902) measured |
| LMArena Text | 1474 | 1483 | Qwen3.8 Max (0902) |
| LMArena Text: instruction following | 1479 | 1481 | Qwen3.8 Max (0902) |
| LMArena Text: creative writing | 1458 | 1477 | Qwen3.8 Max (0902) |
| LMArena Vision | Not measured | 1313 | Only Qwen3.8 Max (0902) measured |
| LMArena Text: English | 1482 | 1491 | Qwen3.8 Max (0902) |
| LMArena Text: non-English | 1459 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Chinese | 1532 | 1539 | Qwen3.8 Max (0902) |
| LMArena Text: German | 1488 | 1478 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Korean | 1460 | 1460 | Qwen3.8 Max (0902) |
| LMArena Text: French | 1503 | 1498 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: Russian | 1469 | 1482 | Qwen3.8 Max (0902) |
| LMArena Text: Polish | 1466 | 1471 | Qwen3.8 Max (0902) |
| LMArena Text: multi-turn | 1472 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: longer queries | 1483 | 1488 | Qwen3.8 Max (0902) |
| LMArena Text: writing and language | 1469 | 1477 | Qwen3.8 Max (0902) |
| LMArena Text: entertainment and media | 1447 | 1464 | Qwen3.8 Max (0902) |
| LMArena Vision: English | Not measured | 1311 | Only Qwen3.8 Max (0902) measured |
| LMArena Vision: Chinese | Not measured | 1358 | Only Qwen3.8 Max (0902) measured |
| LMArena Vision: reading text | Not measured | 1322 | Only Qwen3.8 Max (0902) measured |
| LMArena Vision: humor | Not measured | 1304 | Only Qwen3.8 Max (0902) measured |
| LMArena Vision: creative writing | Not measured | 1312 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 13 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | Qwen3.8 Max (0902) | 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 costs less
Repository review
50K input + 3K output tokens
GLM 5.3 costs less
Agent loop
200K input + 10K output tokens
GLM 5.3 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 | Z.ai GLM 5.3 | Qwen3.8 Max (0902) |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | August 18, 2026 | September 3, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $1.40 | $2.00 |
| Output $/Mtok | $4.40 | $6.00 |
| Coding index | 70.6 | 62.4 |
| Independent measurements behind it | 6 | 5 |
| Cost per solved task | $0.36 | $1.32 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than Qwen3.8 Max (0902) for coding?
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 62.4 for Qwen3.8 Max (0902). Pick Qwen3.8 Max (0902) for work measured by LMArena WebDev or AA Coding Index, where it leads.
Which is cheaper, GLM 5.3 or Qwen3.8 Max (0902)?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Per solved SWE-bench Verified task, GLM 5.3 costs $0.36 and Qwen3.8 Max (0902) $1.32.
Which has the bigger context window, GLM 5.3 or Qwen3.8 Max (0902)?
GLM 5.3, with 1.05M tokens against 1M.
How many benchmarks is the GLM 5.3 vs Qwen3.8 Max (0902) comparison based on?
91 benchmarks have a result for both models as of October 22, 2026. GLM 5.3's coding index rests on 6 independent benchmarks and Qwen3.8 Max (0902)'s on 5. Where only one model has a result, the page says so instead of naming a winner.