GLM 5V Turbo vs Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads GLM 5V Turbo on the Artificial Analysis Intelligence Index, 45.4 to 23.5, and GLM 5V Turbo costs 1.5x less per output token ($4.00 vs $6.00 per million). Updated October 22, 2026; 56 benchmarks have a result for both.
GLM 5V Turbo or Qwen3.8 Max (0902): which to pick
Only Qwen3.8 Max (0902) has a coding score, 62.4 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding.
Pick GLM 5V Turbo for output tokens 1.5x cheaper ($4.00 vs $6.00 per million) and work measured by Design Arena: agents building Android apps, where it leads.
- Shared results
- 56
- GLM 5V Turbo only
- 9
- Qwen3.8 Max (0902) only
- 58
- 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 Qwen3.8 Max (0902) has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 45.4 against 23.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 Max (0902) has a AA Agentic Index result, 56.0.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.8 Max (0902) has a GPQA Diamond result, 93.7%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.8 Max (0902) has a MMLU Pro result, 88.6%.
Directional only
Web design
Design Arena: websites
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) leads, 1275 against 1230.
Same board, both measured
Long documents
Listed context window
Qwen3.8 Max (0902)
Qwen3.8 Max (0902) takes 1M tokens against 203K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5V Turbo
GLM 5V Turbo $0.0032, Qwen3.8 Max (0902) $0.0050 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 Max (0902) has the number, $1.32.
Directional only
At a glance
- Coding index
- GLM 5V Turbo not benchmarked, Qwen3.8 Max (0902) 62.4 from 5 benchmarks.
- Widest category
- Coding, 26.1 points apart on average over 6 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: GLM 5V Turbo 1356 against Qwen3.8 Max (0902) 1649, 40.6 points apart on a 0 to 100 scale.
- Output price
- GLM 5V Turbo $4.00 against Qwen3.8 Max (0902) $6.00 per million output tokens, 1.5x apart.
- Context window
- GLM 5V Turbo takes 203K 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 5V Turbo | Bars | Qwen3.8 Max (0902) | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 1 | 5 | ||
| Reasoning | 0 | 7 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 7 | ||
| General | 0 | 23 | ||
| All shared boards | 1 | 55 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | GLM 5V Turbo | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 60.3% | Only Qwen3.8 Max (0902) measured |
| LMArena WebDev | 1402 | 1674 | Qwen3.8 Max (0902) |
| AA Coding Index | Not measured | 76.2 | Only Qwen3.8 Max (0902) measured |
| LiveCodeBench (Vals AI) | Not measured | 87.8% | Only Qwen3.8 Max (0902) measured |
| SWE-bench Verified (Vals AI) | Not measured | 85.6% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench 4 (Vals AI) | Not measured | 34.3% | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench (Vals AI) | Not measured | 64.7% | Only Qwen3.8 Max (0902) measured |
| Code migration (Vals AI) | Not measured | 24.0% | Only Qwen3.8 Max (0902) measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.8 Max (0902) measured |
| IOI (Vals AI) | Not measured | 68.9% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 67.4% | Only Qwen3.8 Max (0902) measured |
| SWE-bench Multimodal (Vals AI) | Not measured | 42.5% | Only Qwen3.8 Max (0902) measured |
| Vibe Code Bench 1-100 (Vals AI) | Not measured | 12.8% | Only Qwen3.8 Max (0902) measured |
| SkillsBench (Vals AI) | Not measured | 42.0% | Only Qwen3.8 Max (0902) measured |
| CyberBench (Vals AI) | Not measured | 28.6% | Only Qwen3.8 Max (0902) measured |
| SciCode | Not measured | 53.2% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench Hard (Artificial Analysis run) | 32.6% | Not measured | Only GLM 5V Turbo measured |
| FrontierSWE | Not measured | 17.8% | Only Qwen3.8 Max (0902) measured |
| LMArena Text: coding | 1466 | 1502 | Qwen3.8 Max (0902) |
| LMArena Text: software and IT | 1460 | 1498 | Qwen3.8 Max (0902) |
| LMArena WebDev: React | 1386 | 1692 | Qwen3.8 Max (0902) |
| LMArena WebDev: HTML | 1356 | 1649 | Qwen3.8 Max (0902) |
| LMArena WebDev: from an image | 1425 | 1637 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5V Turbo | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.5 | 45.4 | Qwen3.8 Max (0902) |
| Epoch Capabilities Index | Not measured | 157.4 | Only Qwen3.8 Max (0902) measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | GLM 5V Turbo | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 56.0 | Only Qwen3.8 Max (0902) measured |
| Design Arena: agents building web apps | 1124 | 1300 | Qwen3.8 Max (0902) |
| Design Arena: agents building full-stack apps | 1149 | 1305 | Qwen3.8 Max (0902) |
| Design Arena: agents building mobile apps | 1134 | 1224 | Qwen3.8 Max (0902) |
| Design Arena: agents building Android apps | 1267 | 1190 | GLM 5V Turbo |
| Design Arena: agents building Godot games | 957 | 1234 | Qwen3.8 Max (0902) |
| Design Arena: agents building games | 1102 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: HTML slides | 1060 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: PowerPoint slides | 1126 | 1232 | Qwen3.8 Max (0902) |
| Design Arena: spreadsheets | Not measured | 993 | Only Qwen3.8 Max (0902) measured |
| LiveBench research | Not measured | 69.9% | Only Qwen3.8 Max (0902) measured |
| LiveBench browser | Not measured | 32.0% | Only Qwen3.8 Max (0902) measured |
| LiveBench long agentic | Not measured | 42.3% | Only Qwen3.8 Max (0902) measured |
| Finance Agent (Vals AI) | Not measured | 50.6% | Only Qwen3.8 Max (0902) measured |
| Tax Agent Bench (Vals AI) | Not measured | 32.1% | Only Qwen3.8 Max (0902) measured |
| Legal Research Bench (Vals AI) | Not measured | 47.6% | Only Qwen3.8 Max (0902) measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 60.1% | Only Qwen3.8 Max (0902) measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 12.9% | Only Qwen3.8 Max (0902) measured |
| τ²-Bench Telecom | 98.5% | Not measured | Only GLM 5V Turbo measured |
| APEX-Agents | Not measured | 63.3% | Only Qwen3.8 Max (0902) measured |
| LMArena Search | Not measured | 1261 | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 5V Turbo | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 93.7% | Only Qwen3.8 Max (0902) measured |
| LiveBench reasoning | Not measured | 88.2% | Only Qwen3.8 Max (0902) measured |
| MysteryMechanism (Vals AI) | Not measured | 23.9% | Only Qwen3.8 Max (0902) measured |
| MMMU Pro (Vals AI) | Not measured | 88.0% | Only Qwen3.8 Max (0902) measured |
| GPQA Diamond (Artificial Analysis run) | 80.9% | 92.8% | Qwen3.8 Max (0902) |
| Humanity's Last Exam (Artificial Analysis run) | 17.1% | 43.1% | Qwen3.8 Max (0902) |
| GPQA Diamond (Epoch AI run) | Not measured | 92.7% | Only Qwen3.8 Max (0902) measured |
| Chess puzzles | Not measured | 46.0% | Only Qwen3.8 Max (0902) measured |
| Mystery game puzzles | Not measured | 38.0% | Only Qwen3.8 Max (0902) measured |
| LMArena Text: hard prompts | 1443 | 1496 | Qwen3.8 Max (0902) |
| LMArena Text: expert | 1453 | 1507 | Qwen3.8 Max (0902) |
| LMArena Text: hard prompts in English | 1456 | 1503 | Qwen3.8 Max (0902) |
| LMArena Vision: diagrams | 1286 | 1323 | Qwen3.8 Max (0902) |
| LMArena Vision: homework | 1273 | 1336 | Qwen3.8 Max (0902) |
| Neither model has a result on the other 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5V Turbo | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 100.0% | Only Qwen3.8 Max (0902) measured |
| FrontierMath, tiers 1 to 3 | Not measured | 74.7% | Only Qwen3.8 Max (0902) measured |
| FrontierMath, tier 4 | Not measured | 46.3% | Only Qwen3.8 Max (0902) measured |
| ProofBench | Not measured | 58.0% | Only Qwen3.8 Max (0902) measured |
| LMArena Text: math | 1442 | 1501 | Qwen3.8 Max (0902) |
| LMArena Text: mathematical work | 1447 | 1491 | Qwen3.8 Max (0902) |
| MathArena | Not measured | 51.6% | Only Qwen3.8 Max (0902) measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 5V Turbo | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 88.6% | Only Qwen3.8 Max (0902) measured |
| LegalBench (Vals AI) | Not measured | 83.6% | Only Qwen3.8 Max (0902) measured |
| CorpFin (Vals AI) | Not measured | 65.8% | Only Qwen3.8 Max (0902) measured |
| TaxEval (Vals AI) | Not measured | 75.5% | Only Qwen3.8 Max (0902) measured |
| MortgageTax (Vals AI) | Not measured | 64.0% | Only Qwen3.8 Max (0902) measured |
| MedCode (Vals AI) | Not measured | 40.7% | Only Qwen3.8 Max (0902) measured |
| SimpleQA Verified | Not measured | 47.3% | Only Qwen3.8 Max (0902) measured |
| LMArena Text: science | 1448 | 1512 | Qwen3.8 Max (0902) |
| LMArena Text: medicine and healthcare | 1433 | 1503 | Qwen3.8 Max (0902) |
| LMArena Text: legal and government | 1436 | 1492 | Qwen3.8 Max (0902) |
| LMArena Text: business and finance | 1435 | 1480 | Qwen3.8 Max (0902) |
| 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 | GLM 5V Turbo | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| Design Arena: websites | 1230 | 1275 | Qwen3.8 Max (0902) |
| Design Arena: UI components | 1210 | 1336 | Qwen3.8 Max (0902) |
| Design Arena: game dev | 1218 | 1312 | Qwen3.8 Max (0902) |
| Design Arena: data viz | 1202 | 1282 | Qwen3.8 Max (0902) |
| Design Arena: 3D | 1208 | 1336 | Qwen3.8 Max (0902) |
| Design Arena: all code categories | 1227 | 1292 | Qwen3.8 Max (0902) |
| Design Arena: SVG | 1150 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: ASCII art | 1116 | 1200 | Qwen3.8 Max (0902) |
| LiveBench design | Not measured | 78.6% | Only Qwen3.8 Max (0902) measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index023
| Benchmark | GLM 5V Turbo | Qwen3.8 Max (0902) | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 78.4% | Only Qwen3.8 Max (0902) measured |
| Public Benefits Bench (Vals AI) | Not measured | 67.1% | Only Qwen3.8 Max (0902) measured |
| SAGE (Vals AI) | Not measured | 51.2% | Only Qwen3.8 Max (0902) measured |
| MedScribe (Vals AI) | Not measured | 85.0% | Only Qwen3.8 Max (0902) measured |
| Vals Multimodal Index | Not measured | 65.4% | Only Qwen3.8 Max (0902) measured |
| IFBench | 61.1% | Not measured | Only GLM 5V Turbo measured |
| Long context reasoning (AA-LCR) | 70.3% | 80.3% | Qwen3.8 Max (0902) |
| DTBench | Not measured | 92.0% | Only Qwen3.8 Max (0902) measured |
| LMCA | Not measured | 46.2% | Only Qwen3.8 Max (0902) measured |
| Furniture assembly | Not measured | 20.0% | Only Qwen3.8 Max (0902) measured |
| LMArena Text | 1437 | 1483 | Qwen3.8 Max (0902) |
| LMArena Text: instruction following | 1423 | 1481 | Qwen3.8 Max (0902) |
| LMArena Text: creative writing | 1417 | 1477 | Qwen3.8 Max (0902) |
| LMArena Vision | 1264 | 1313 | Qwen3.8 Max (0902) |
| LMArena Text: English | 1454 | 1491 | Qwen3.8 Max (0902) |
| LMArena Text: non-English | 1420 | 1473 | Qwen3.8 Max (0902) |
| LMArena Text: Chinese | 1491 | 1539 | Qwen3.8 Max (0902) |
| LMArena Text: German | 1422 | 1478 | Qwen3.8 Max (0902) |
| LMArena Text: Japanese | Not measured | 1473 | Only Qwen3.8 Max (0902) measured |
| LMArena Text: Korean | 1397 | 1460 | Qwen3.8 Max (0902) |
| LMArena Text: French | 1443 | 1498 | Qwen3.8 Max (0902) |
| LMArena Text: Spanish | 1452 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: Russian | 1432 | 1482 | Qwen3.8 Max (0902) |
| LMArena Text: Polish | 1432 | 1471 | Qwen3.8 Max (0902) |
| LMArena Text: multi-turn | 1431 | 1489 | Qwen3.8 Max (0902) |
| LMArena Text: longer queries | 1438 | 1488 | Qwen3.8 Max (0902) |
| LMArena Text: writing and language | 1420 | 1477 | Qwen3.8 Max (0902) |
| LMArena Text: entertainment and media | 1407 | 1464 | Qwen3.8 Max (0902) |
| LMArena Vision: English | 1274 | 1311 | Qwen3.8 Max (0902) |
| LMArena Vision: Chinese | 1329 | 1358 | Qwen3.8 Max (0902) |
| LMArena Vision: reading text | 1270 | 1322 | Qwen3.8 Max (0902) |
| LMArena Vision: humor | 1268 | 1304 | Qwen3.8 Max (0902) |
| LMArena Vision: captioning | 1264 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: recognizing things | 1241 | Not measured | Only GLM 5V Turbo measured |
| LMArena Vision: creative writing | 1252 | 1312 | Qwen3.8 Max (0902) |
| LMArena Document | 1416 | Not measured | 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 | GLM 5V Turbo | 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 5V Turbo 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 | Z.ai GLM 5V Turbo | Qwen3.8 Max (0902) |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | No public weights listed | No public weights listed |
| Released | April 1, 2026 | September 3, 2026 |
| Context window | 203K | 1M |
| Input $/Mtok | $1.20 | $2.00 |
| Output $/Mtok | $4.00 | $6.00 |
| Coding index | Not benchmarked | 62.4 |
| Independent measurements behind it | 1 | 5 |
| Cost per solved task | Not computable | $1.32 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5V Turbo better than Qwen3.8 Max (0902) for coding?
Only Qwen3.8 Max (0902) has a coding score, 62.4 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding. Pick GLM 5V Turbo for output tokens 1.5x cheaper ($4.00 vs $6.00 per million) and work measured by Design Arena: agents building Android apps, where it leads.
Which is cheaper, GLM 5V Turbo or Qwen3.8 Max (0902)?
GLM 5V Turbo costs $1.20 per million input tokens and $4.00 per million output tokens; Qwen3.8 Max (0902) costs $2.00 and $6.00. Cost per solved task is known for Qwen3.8 Max (0902) only.
Which has the bigger context window, GLM 5V Turbo or Qwen3.8 Max (0902)?
Qwen3.8 Max (0902), with 1M tokens against 203K.
How many benchmarks is the GLM 5V Turbo vs Qwen3.8 Max (0902) comparison based on?
56 benchmarks have a result for both models as of October 22, 2026. GLM 5V Turbo's coding index rests on 1 independent benchmark and Qwen3.8 Max (0902)'s on 5. Where only one model has a result, the page says so instead of naming a winner.