GLM 5V Turbo vs Qwen3.8 27B
Qwen3.8 27B leads GLM 5V Turbo on the Artificial Analysis Intelligence Index, 33.7 to 23.5, and Qwen3.8 27B costs 1.6x less per output token ($2.55 vs $4.00 per million). Updated October 22, 2026; 43 benchmarks have a result for both.
GLM 5V Turbo or Qwen3.8 27B: which to pick
Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding.
Pick GLM 5V Turbo for work measured by LMArena Vision: diagrams or LMArena Text: creative writing, where it leads.
- Shared results
- 43
- GLM 5V Turbo only
- 22
- Qwen3.8 27B only
- 42
- Like-for-like categories
- 6 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 27B has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.8 27B
Qwen3.8 27B leads, 33.7 against 23.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.8 27B has a AA Agentic Index result, 45.8.
Directional only
Reasoning
GPQA Diamond
No call
Only Qwen3.8 27B has a GPQA Diamond result, 88.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Qwen3.8 27B has a MMLU Pro result, 84.3%.
Directional only
Web design
Design Arena: websites
No call
Only GLM 5V Turbo has a Design Arena: websites result, 1230.
Directional only
Long documents
Listed context window
Qwen3.8 27B
Qwen3.8 27B takes 1M tokens against 203K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 27B
GLM 5V Turbo $0.0032, Qwen3.8 27B $0.0017 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.8 27B has the number, $1.26.
Directional only
At a glance
- Coding index
- GLM 5V Turbo not benchmarked, Qwen3.8 27B 74.2 from 4 benchmarks.
- Widest category
- Coding, 21.7 points apart on average over 6 shared benchmarks.
- Widest single gap
- LMArena WebDev: HTML: GLM 5V Turbo 1356 against Qwen3.8 27B 1570, 35.6 points apart on a 0 to 100 scale.
- Output price
- GLM 5V Turbo $4.00 against Qwen3.8 27B $2.55 per million output tokens, 1.6x apart.
- Context window
- GLM 5V Turbo takes 203K tokens, Qwen3.8 27B 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 27B | Ties |
|---|---|---|---|---|
| Coding | 0 | 6 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 1 | 6 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| General | 11 | 12 | ||
| All shared boards | 12 | 31 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index06
| Benchmark | GLM 5V Turbo | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 86.8% | Only Qwen3.8 27B measured |
| LMArena WebDev | 1402 | 1593 | Qwen3.8 27B |
| AA Coding Index | Not measured | 68.1 | Only Qwen3.8 27B measured |
| LiveCodeBench (Vals AI) | Not measured | 84.0% | Only Qwen3.8 27B measured |
| SWE-bench Verified (Vals AI) | Not measured | 86.0% | Only Qwen3.8 27B measured |
| Vibe Code Bench (Vals AI) | Not measured | 64.8% | Only Qwen3.8 27B measured |
| Code migration (Vals AI) | Not measured | 14.2% | Only Qwen3.8 27B measured |
| ProgramBench (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| IOI (Vals AI) | Not measured | 39.1% | Only Qwen3.8 27B measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 58.4% | Only Qwen3.8 27B measured |
| SkillsBench (Vals AI) | Not measured | 38.1% | Only Qwen3.8 27B measured |
| CyberBench (Vals AI) | Not measured | 67.8% | Only Qwen3.8 27B measured |
| SciCode | Not measured | 46.6% | Only Qwen3.8 27B measured |
| Terminal-Bench Hard (Artificial Analysis run) | 32.6% | Not measured | Only GLM 5V Turbo measured |
| LMArena Text: coding | 1466 | 1483 | Qwen3.8 27B |
| LMArena Text: software and IT | 1460 | 1478 | Qwen3.8 27B |
| LMArena WebDev: React | 1386 | 1602 | Qwen3.8 27B |
| LMArena WebDev: HTML | 1356 | 1570 | Qwen3.8 27B |
| LMArena WebDev: from an image | 1425 | 1577 | Qwen3.8 27B |
| Neither model has a result on the other 24 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5V Turbo | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.5 | 33.7 | Qwen3.8 27B |
| Epoch Capabilities Index | Not measured | 151.7 | Only Qwen3.8 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index19 boards, none shared
| Benchmark | GLM 5V Turbo | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 45.8 | Only Qwen3.8 27B measured |
| Design Arena: agents building web apps | 1124 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building full-stack apps | 1149 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building mobile apps | 1134 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building Android apps | 1267 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: agents building Godot games | 957 | Not measured | Only GLM 5V Turbo measured |
| 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 | Not measured | Only GLM 5V Turbo measured |
| LiveBench research | Not measured | 59.4% | Only Qwen3.8 27B measured |
| LiveBench browser | Not measured | 32.0% | Only Qwen3.8 27B measured |
| LiveBench long agentic | Not measured | 27.9% | Only Qwen3.8 27B measured |
| Finance Agent (Vals AI) | Not measured | 48.5% | Only Qwen3.8 27B measured |
| Tax Agent Bench (Vals AI) | Not measured | 30.9% | Only Qwen3.8 27B measured |
| Legal Research Bench (Vals AI) | Not measured | 36.1% | Only Qwen3.8 27B measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 59.7% | Only Qwen3.8 27B measured |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| τ²-Bench Telecom | 98.5% | Not measured | Only GLM 5V Turbo measured |
| APEX-Agents | Not measured | 47.5% | Only Qwen3.8 27B measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index16
| Benchmark | GLM 5V Turbo | Qwen3.8 27B | Leader |
|---|---|---|---|
| GPQA Diamond | Not measured | 88.9% | Only Qwen3.8 27B measured |
| LiveBench reasoning | Not measured | 80.0% | Only Qwen3.8 27B measured |
| MMMU Pro (Vals AI) | Not measured | 83.9% | Only Qwen3.8 27B measured |
| GPQA Diamond (Artificial Analysis run) | 80.9% | 90.5% | Qwen3.8 27B |
| Humanity's Last Exam (Artificial Analysis run) | 17.1% | 33.9% | Qwen3.8 27B |
| ARC-AGI | Not measured | 87.5% | Only Qwen3.8 27B measured |
| ARC-AGI-2 | Not measured | 42.4% | Only Qwen3.8 27B measured |
| LMArena Text: hard prompts | 1443 | 1459 | Qwen3.8 27B |
| LMArena Text: expert | 1453 | 1479 | Qwen3.8 27B |
| LMArena Text: hard prompts in English | 1456 | 1461 | Qwen3.8 27B |
| LMArena Vision: diagrams | 1286 | 1285 | GLM 5V Turbo |
| LMArena Vision: homework | 1273 | 1295 | Qwen3.8 27B |
| Neither model has a result on the other 11 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5V Turbo | Qwen3.8 27B | Leader |
|---|---|---|---|
| ProofBench | Not measured | 16.0% | Only Qwen3.8 27B measured |
| LMArena Text: math | 1442 | 1455 | Qwen3.8 27B |
| LMArena Text: mathematical work | 1447 | 1458 | Qwen3.8 27B |
| Neither model has a result on the other 11 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 5V Turbo | Qwen3.8 27B | Leader |
|---|---|---|---|
| MMLU Pro | Not measured | 84.3% | Only Qwen3.8 27B measured |
| LegalBench (Vals AI) | Not measured | 82.4% | Only Qwen3.8 27B measured |
| TaxEval (Vals AI) | Not measured | 70.8% | Only Qwen3.8 27B measured |
| MortgageTax (Vals AI) | Not measured | 64.9% | Only Qwen3.8 27B measured |
| MedCode (Vals AI) | Not measured | 28.7% | Only Qwen3.8 27B measured |
| LMArena Text: science | 1448 | 1467 | Qwen3.8 27B |
| LMArena Text: medicine and healthcare | 1433 | 1437 | Qwen3.8 27B |
| LMArena Text: legal and government | 1436 | 1447 | Qwen3.8 27B |
| LMArena Text: business and finance | 1435 | 1438 | Qwen3.8 27B |
| Neither model has a result on the other 11 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 5V Turbo | Qwen3.8 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1230 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: UI components | 1210 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: game dev | 1218 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: data viz | 1202 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: 3D | 1208 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: all code categories | 1227 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: SVG | 1150 | Not measured | Only GLM 5V Turbo measured |
| Design Arena: ASCII art | 1116 | Not measured | Only GLM 5V Turbo measured |
| LiveBench design | Not measured | 75.9% | Only Qwen3.8 27B measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index1112
| Benchmark | GLM 5V Turbo | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 76.6% | Only Qwen3.8 27B measured |
| SAGE (Vals AI) | Not measured | 52.4% | Only Qwen3.8 27B measured |
| MedScribe (Vals AI) | Not measured | 83.8% | Only Qwen3.8 27B measured |
| IFBench | 61.1% | Not measured | Only GLM 5V Turbo measured |
| Long context reasoning (AA-LCR) | 70.3% | 82.0% | Qwen3.8 27B |
| DTBench | Not measured | 88.0% | Only Qwen3.8 27B measured |
| LMCA | Not measured | 41.4% | Only Qwen3.8 27B measured |
| Surface Evolver Bench | Not measured | 45.0% | Only Qwen3.8 27B measured |
| LMArena Text | 1437 | 1440 | Qwen3.8 27B |
| LMArena Text: instruction following | 1423 | 1436 | Qwen3.8 27B |
| LMArena Text: creative writing | 1417 | 1383 | GLM 5V Turbo |
| LMArena Vision | 1264 | 1269 | Qwen3.8 27B |
| LMArena Text: English | 1454 | 1448 | GLM 5V Turbo |
| LMArena Text: non-English | 1420 | 1428 | Qwen3.8 27B |
| LMArena Text: Chinese | 1491 | 1501 | Qwen3.8 27B |
| LMArena Text: German | 1422 | 1443 | Qwen3.8 27B |
| LMArena Text: Japanese | Not measured | 1387 | Only Qwen3.8 27B measured |
| LMArena Text: Korean | 1397 | 1384 | GLM 5V Turbo |
| LMArena Text: French | 1443 | 1465 | Qwen3.8 27B |
| LMArena Text: Spanish | 1452 | 1449 | GLM 5V Turbo |
| LMArena Text: Russian | 1432 | 1415 | GLM 5V Turbo |
| LMArena Text: Polish | 1432 | 1439 | Qwen3.8 27B |
| LMArena Text: multi-turn | 1431 | 1440 | Qwen3.8 27B |
| LMArena Text: longer queries | 1438 | 1448 | Qwen3.8 27B |
| LMArena Text: writing and language | 1420 | 1405 | GLM 5V Turbo |
| LMArena Text: entertainment and media | 1407 | 1393 | GLM 5V Turbo |
| LMArena Vision: English | 1274 | 1267 | GLM 5V Turbo |
| LMArena Vision: Chinese | 1329 | 1326 | GLM 5V Turbo |
| LMArena Vision: reading text | 1270 | 1281 | Qwen3.8 27B |
| LMArena Vision: humor | 1268 | 1235 | GLM 5V Turbo |
| 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 | 1237 | GLM 5V Turbo |
| LMArena Document | 1416 | Not measured | Only GLM 5V Turbo measured |
| Neither model has a result on the other 14 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5V Turbo | Qwen3.8 27B | 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
Qwen3.8 27B costs less
Repository review
50K input + 3K output tokens
Qwen3.8 27B costs less
Agent loop
200K input + 10K output tokens
Qwen3.8 27B 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 27B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | No public weights listed | Open weights |
| Released | April 1, 2026 | August 14, 2026 |
| Context window | 203K | 1M |
| Input $/Mtok | $1.20 | $0.425 |
| Output $/Mtok | $4.00 | $2.55 |
| Coding index | Not benchmarked | 74.2 |
| Independent measurements behind it | 1 | 4 |
| Cost per solved task | Not computable | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5V Turbo better than Qwen3.8 27B for coding?
Only Qwen3.8 27B has a coding score, 74.2 on the Vibe Coding Index; GLM 5V Turbo has none, so the two cannot be ranked on coding. Pick GLM 5V Turbo for work measured by LMArena Vision: diagrams or LMArena Text: creative writing, where it leads.
Which is cheaper, GLM 5V Turbo or Qwen3.8 27B?
GLM 5V Turbo costs $1.20 per million input tokens and $4.00 per million output tokens; Qwen3.8 27B costs $0.425 and $2.55. Cost per solved task is known for Qwen3.8 27B only.
Which has the bigger context window, GLM 5V Turbo or Qwen3.8 27B?
Qwen3.8 27B, with 1M tokens against 203K.
How many benchmarks is the GLM 5V Turbo vs Qwen3.8 27B comparison based on?
43 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 27B's on 4. Where only one model has a result, the page says so instead of naming a winner.