GLM 5.2 vs Qwen3.8 27B
Updated October 21, 2026. 62 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 57.7 for GLM 5.2.
Pick GLM 5.2 when a solved task costs $0.86 against $1.26, or when the context window is larger, 1.05M against 1M tokens.
- Shared results
- 62
- GLM 5.2 only
- 35
- Qwen3.8 27B only
- 23
- 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
Qwen3.8 27B
Qwen3.8 27B scores 73.6 against 57.7.
1 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
No call
Tied at 33.7.
Same board, both measured
Agentic work
AA Agentic Index
Qwen3.8 27B
Qwen3.8 27B leads, 45.8 against 38.4.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.8 27B
Qwen3.8 27B leads, 88.9% against 85.6%.
Same board, both measured
Knowledge
MMLU Pro
GLM 5.2
GLM 5.2 leads, 86.7% against 84.3%.
Same board, both measured
Web design
Design Arena: websites
No call
Only GLM 5.2 has a Design Arena: websites result, 1285.
Directional only
Long documents
Listed context window
GLM 5.2
GLM 5.2 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.8 27B
GLM 5.2 $0.0035, Qwen3.8 27B $0.0007 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.2
GLM 5.2 $0.86, Qwen3.8 27B $1.26.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.2 57.7 from 1 benchmark, Qwen3.8 27B 73.6 from 2 benchmarks.
- Widest category
- Math, 9.7 points apart on average over 3 shared benchmarks.
- Widest single gap
- Code migration (Vals AI): GLM 5.2 37.9% against Qwen3.8 27B 14.2%, 23.7 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $7.00 against Qwen3.8 27B $1.35 per million output tokens, 5.2x apart.
- Context window
- GLM 5.2 takes 1.05M 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 5.2 | Bars | Qwen3.8 27B | Ties |
|---|---|---|---|---|
| Coding | 10 | 4 | ||
| Intelligence | 1 | 0 | 1 tie | |
| Agentic work | 2 | 3 | ||
| Reasoning | 4 | 4 | ||
| Math | 3 | 0 | ||
| Knowledge | 8 | 0 | ||
| General | 20 | 2 | ||
| All shared boards | 48 | 13 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index104
| Benchmark | GLM 5.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 86.8% | Only Qwen3.8 27B measured |
| LMArena WebDev | 1604 | 1593 | GLM 5.2 |
| AA Coding Index | 68.8 | 68.1 | GLM 5.2 |
| LiveCodeBench (Vals AI) | 69.5% | 84.0% | Qwen3.8 27B |
| SWE-bench Verified (Vals AI) | 82.8% | 86.0% | Qwen3.8 27B |
| Vibe Code Bench (Vals AI) | 64.0% | 64.8% | Qwen3.8 27B |
| Code migration (Vals AI) | 37.9% | 14.2% | GLM 5.2 |
| ProgramBench (Vals AI) | 0.5% | 0.0% | GLM 5.2 |
| IOI (Vals AI) | Not measured | 39.1% | Only Qwen3.8 27B measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | 58.4% | GLM 5.2 |
| SkillsBench (Vals AI) | 45.1% | 38.1% | GLM 5.2 |
| CyberBench (Vals AI) | Not measured | 67.8% | Only Qwen3.8 27B measured |
| SciCode | 51.2% | 46.6% | GLM 5.2 |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | Not measured | Only GLM 5.2 measured |
| FrontierCode | 24.5% | Not measured | Only GLM 5.2 measured |
| WeirdML | 70.1% | Not measured | Only GLM 5.2 measured |
| ALE-Bench | 1047.0 | Not measured | Only GLM 5.2 measured |
| LMArena Text: coding | 1487 | 1483 | GLM 5.2 |
| LMArena Text: software and IT | 1486 | 1478 | GLM 5.2 |
| LMArena WebDev: React | 1617 | 1602 | GLM 5.2 |
| LMArena WebDev: HTML | 1565 | 1570 | Qwen3.8 27B |
| LMArena WebDev: from an image | Not measured | 1577 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GLM 5.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 33.7 | Tie |
| Epoch Capabilities Index | 151.9 | 151.7 | GLM 5.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index23
| Benchmark | GLM 5.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | 45.8 | Qwen3.8 27B |
| Design Arena: agents building web apps | 1222 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building full-stack apps | 1241 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building mobile apps | 1160 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Android apps | 1184 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building Godot games | 1142 | Not measured | Only GLM 5.2 measured |
| Design Arena: agents building games | 1206 | Not measured | Only GLM 5.2 measured |
| Design Arena: HTML slides | 1175 | Not measured | Only GLM 5.2 measured |
| Design Arena: PowerPoint slides | 1195 | Not measured | Only GLM 5.2 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 |
| SREBench (Vals AI) | 0.0% | Not measured | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 49.7% | 48.5% | GLM 5.2 |
| Tax Agent Bench (Vals AI) | Not measured | 30.9% | Only Qwen3.8 27B measured |
| Legal Research Bench (Vals AI) | 31.3% | 36.1% | Qwen3.8 27B |
| Excel Modeling Benchmark (Vals AI) | 61.5% | 59.7% | GLM 5.2 |
| Terminal-Bench-Science (Vals AI) | Not measured | 0.0% | Only Qwen3.8 27B measured |
| τ²-Bench Telecom | 99.1% | Not measured | Only GLM 5.2 measured |
| APEX-Agents | 45.2% | 47.5% | Qwen3.8 27B |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| Vending-Bench 2 | $8,314 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 17 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index44
| Benchmark | GLM 5.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | 88.9% | Qwen3.8 27B |
| 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) | 89.5% | 90.5% | Qwen3.8 27B |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 33.9% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 91.9% | Not measured | Only GLM 5.2 measured |
| ARC-AGI | 77.0% | 87.5% | Qwen3.8 27B |
| ARC-AGI-2 | 22.8% | 42.4% | Qwen3.8 27B |
| SimpleBench | 58.8% | Not measured | Only GLM 5.2 measured |
| Chess puzzles | 22.0% | Not measured | Only GLM 5.2 measured |
| Mystery game puzzles | 19.0% | Not measured | Only GLM 5.2 measured |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1481 | 1459 | GLM 5.2 |
| LMArena Text: expert | 1487 | 1479 | GLM 5.2 |
| LMArena Text: hard prompts in English | 1481 | 1461 | GLM 5.2 |
| LMArena Vision: diagrams | Not measured | 1285 | Only Qwen3.8 27B measured |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 4 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GLM 5.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 86.4% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tiers 1 to 3 | 59.2% | Not measured | Only GLM 5.2 measured |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.2 measured |
| ProofBench | 35.0% | 16.0% | GLM 5.2 |
| LMArena Text: math | 1483 | 1455 | GLM 5.2 |
| LMArena Text: mathematical work | 1489 | 1458 | GLM 5.2 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | 84.3% | GLM 5.2 |
| LegalBench (Vals AI) | 84.1% | 82.4% | GLM 5.2 |
| CorpFin (Vals AI) | 66.1% | Not measured | Only GLM 5.2 measured |
| TaxEval (Vals AI) | 73.3% | 70.8% | GLM 5.2 |
| MortgageTax (Vals AI) | Not measured | 64.9% | Only Qwen3.8 27B measured |
| MedCode (Vals AI) | 40.8% | 28.7% | GLM 5.2 |
| SimpleQA Verified | 34.2% | Not measured | Only GLM 5.2 measured |
| LMArena Text: science | 1489 | 1467 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1475 | 1437 | GLM 5.2 |
| LMArena Text: legal and government | 1486 | 1447 | GLM 5.2 |
| LMArena Text: business and finance | 1451 | 1438 | GLM 5.2 |
| Neither model has a result on the other 6 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index9 boards, none shared
| Benchmark | GLM 5.2 | Qwen3.8 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | Not measured | Only GLM 5.2 measured |
| Design Arena: UI components | 1296 | Not measured | Only GLM 5.2 measured |
| Design Arena: game dev | 1268 | Not measured | Only GLM 5.2 measured |
| Design Arena: data viz | 1285 | Not measured | Only GLM 5.2 measured |
| Design Arena: 3D | 1288 | Not measured | Only GLM 5.2 measured |
| Design Arena: all code categories | 1288 | Not measured | Only GLM 5.2 measured |
| Design Arena: SVG | 1214 | Not measured | Only GLM 5.2 measured |
| Design Arena: ASCII art | 1190 | Not measured | Only GLM 5.2 measured |
| LiveBench design | Not measured | 75.9% | Only Qwen3.8 27B measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index202
| Benchmark | GLM 5.2 | 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) | 83.5% | 83.8% | Qwen3.8 27B |
| IFBench | 73.3% | Not measured | Only GLM 5.2 measured |
| Long context reasoning (AA-LCR) | 78.3% | 82.0% | Qwen3.8 27B |
| DTBench | 93.6% | 88.0% | GLM 5.2 |
| LMCA | 45.8% | 41.4% | GLM 5.2 |
| Surface Evolver Bench | 55.6% | 45.0% | GLM 5.2 |
| LMArena Text | 1470 | 1440 | GLM 5.2 |
| LMArena Text: instruction following | 1466 | 1436 | GLM 5.2 |
| LMArena Text: creative writing | 1463 | 1383 | GLM 5.2 |
| LMArena Vision | Not measured | 1269 | Only Qwen3.8 27B measured |
| LMArena Text: English | 1475 | 1448 | GLM 5.2 |
| LMArena Text: non-English | 1459 | 1428 | GLM 5.2 |
| LMArena Text: Chinese | 1522 | 1501 | GLM 5.2 |
| LMArena Text: German | 1470 | 1443 | GLM 5.2 |
| LMArena Text: Japanese | 1450 | 1387 | GLM 5.2 |
| LMArena Text: Korean | 1440 | 1384 | GLM 5.2 |
| LMArena Text: French | 1479 | 1465 | GLM 5.2 |
| LMArena Text: Spanish | 1478 | 1449 | GLM 5.2 |
| LMArena Text: Russian | 1468 | 1415 | GLM 5.2 |
| LMArena Text: Polish | 1456 | 1439 | GLM 5.2 |
| LMArena Text: multi-turn | 1469 | 1440 | GLM 5.2 |
| LMArena Text: longer queries | 1480 | 1448 | GLM 5.2 |
| LMArena Text: writing and language | 1463 | 1405 | GLM 5.2 |
| LMArena Text: entertainment and media | 1450 | 1393 | GLM 5.2 |
| LMArena Vision: English | Not measured | 1267 | Only Qwen3.8 27B measured |
| LMArena Vision: Chinese | Not measured | 1326 | Only Qwen3.8 27B measured |
| LMArena Vision: reading text | Not measured | 1281 | Only Qwen3.8 27B measured |
| LMArena Vision: humor | Not measured | 1235 | Only Qwen3.8 27B measured |
| LMArena Vision: creative writing | Not measured | 1237 | Only Qwen3.8 27B measured |
| Neither model has a result on the other 15 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | 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 5.2 | Qwen3.8 27B |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | Open weights |
| Released | June 16, 2026 | August 14, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $0.0341 | $0.04 |
| Output $/Mtok | $7.00 | $1.35 |
| Coding index | 57.7 | 73.6 |
| Independent measurements behind it | 1 | 2 |
| Cost per solved task | $0.86 | $1.26 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than Qwen3.8 27B for coding?
Pick Qwen3.8 27B for coding: it scores 73.6 on the Vibe Coding Index against 57.7 for GLM 5.2. Pick GLM 5.2 when a solved task costs $0.86 against $1.26, or when the context window is larger, 1.05M against 1M tokens.
Which is cheaper, GLM 5.2 or Qwen3.8 27B?
GLM 5.2 costs $0.0341 per million input tokens and $7.00 per million output tokens; Qwen3.8 27B costs $0.04 and $1.35. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and Qwen3.8 27B $1.26.
Which has the bigger context window, GLM 5.2 or Qwen3.8 27B?
GLM 5.2, with 1.05M tokens against 1M.
How many benchmarks is the GLM 5.2 vs Qwen3.8 27B comparison based on?
62 benchmarks have a result for both models as of October 21, 2026. GLM 5.2's coding index rests on 1 independent benchmark and Qwen3.8 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.