GLM 5.2 vs Qwen3.6 Plus
Updated October 21, 2026. 74 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GLM 5.2 for coding: it scores 57.7 on the Vibe Coding Index against 37.4 for Qwen3.6 Plus.
Pick Qwen3.6 Plus when output tokens cost 3.6x less, or when what matters is LiveCodeBench (Vals AI) or GPQA Diamond, where it leads.
- Shared results
- 74
- GLM 5.2 only
- 23
- Qwen3.6 Plus only
- 9
- Like-for-like categories
- 8 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
GLM 5.2
GLM 5.2 scores 57.7 against 37.4.
One benchmark behind each score
Intelligence
AA Intelligence Index
GLM 5.2
GLM 5.2 leads, 33.7 against 27.0.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only GLM 5.2 has a AA Agentic Index result, 38.4.
Directional only
Reasoning
GPQA Diamond
Qwen3.6 Plus
Qwen3.6 Plus leads, 87.4% against 85.6%.
Same board, both measured
Math
AIME
No call
Only Qwen3.6 Plus has a AIME result, 94.6%.
Directional only
Knowledge
MMLU Pro
Qwen3.6 Plus
Qwen3.6 Plus leads, 87.7% against 86.7%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.2
GLM 5.2 leads, 1285 against 1243.
Same board, both measured
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
Qwen3.6 Plus
GLM 5.2 $0.0035, Qwen3.6 Plus $0.0013 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.2
GLM 5.2 $0.86, Qwen3.6 Plus $1.19.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.2 57.7 from 1 benchmark, Qwen3.6 Plus 37.4 from 1 benchmark.
- Widest category
- Agentic work, 15.1 points apart on average over 5 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 5.2 64.0% against Qwen3.6 Plus 25.6%, 38.4 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $7.00 against Qwen3.6 Plus $1.95 per million output tokens, 3.6x apart.
- Context window
- GLM 5.2 takes 1.05M tokens, Qwen3.6 Plus 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 | Qwen3.6 Plus | Ties |
|---|---|---|---|---|
| Coding | 13 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 5 | 0 | ||
| Reasoning | 8 | 1 | ||
| Math | 3 | 1 | ||
| Knowledge | 6 | 4 | ||
| Web design | 8 | 0 | ||
| General | 20 | 1 | 1 tie | |
| All shared boards | 65 | 8 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index131
| Benchmark | GLM 5.2 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| LMArena WebDev | 1604 | 1461 | GLM 5.2 |
| AA Coding Index | 68.8 | 54.5 | GLM 5.2 |
| LiveCodeBench (Vals AI) | 69.5% | 86.0% | Qwen3.6 Plus |
| SWE-bench Verified (Vals AI) | 82.8% | 73.4% | GLM 5.2 |
| Vibe Code Bench (Vals AI) | 64.0% | 25.6% | GLM 5.2 |
| Code migration (Vals AI) | 37.9% | 11.1% | GLM 5.2 |
| ProgramBench (Vals AI) | 0.5% | 0.0% | GLM 5.2 |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | 53.2% | GLM 5.2 |
| SkillsBench (Vals AI) | 45.1% | Not measured | Only GLM 5.2 measured |
| SciCode | 51.2% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | 43.9% | GLM 5.2 |
| FrontierCode | 24.5% | Not measured | Only GLM 5.2 measured |
| WeirdML | 70.1% | Not measured | Only GLM 5.2 measured |
| ALE-Bench | 1047.0 | 670.1 | GLM 5.2 |
| LMArena Text: coding | 1487 | 1467 | GLM 5.2 |
| LMArena Text: software and IT | 1486 | 1462 | GLM 5.2 |
| LMArena WebDev: React | 1617 | 1454 | GLM 5.2 |
| LMArena WebDev: HTML | 1565 | 1463 | GLM 5.2 |
| LMArena WebDev: from an image | Not measured | 1472 | Only Qwen3.6 Plus measured |
| Neither model has a result on the other 19 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.2 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 27.0 | GLM 5.2 |
| Epoch Capabilities Index | 151.9 | 147.3 | GLM 5.2 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.2 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | Not measured | Only GLM 5.2 measured |
| 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 |
| SREBench (Vals AI) | 0.0% | Not measured | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 49.7% | 40.9% | GLM 5.2 |
| Tax Agent Bench (Vals AI) | Not measured | 7.7% | Only Qwen3.6 Plus measured |
| Legal Research Bench (Vals AI) | 31.3% | 14.9% | GLM 5.2 |
| Excel Modeling Benchmark (Vals AI) | 61.5% | 32.9% | GLM 5.2 |
| τ²-Bench Telecom | 99.1% | 97.7% | GLM 5.2 |
| APEX-Agents | 45.2% | Not measured | Only GLM 5.2 measured |
| PostTrainBench | 31.7% | Not measured | Only GLM 5.2 measured |
| Vending-Bench 2 | $8,314 | $5,115 | GLM 5.2 |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index81
| Benchmark | GLM 5.2 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | 87.4% | Qwen3.6 Plus |
| MMMU Pro (Vals AI) | Not measured | 84.2% | Only Qwen3.6 Plus measured |
| GPQA Diamond (Artificial Analysis run) | 89.5% | 88.2% | GLM 5.2 |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 27.8% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 91.9% | 88.4% | GLM 5.2 |
| ARC-AGI | 77.0% | Not measured | Only GLM 5.2 measured |
| ARC-AGI-2 | 22.8% | Not measured | Only GLM 5.2 measured |
| SimpleBench | 58.8% | Not measured | Only GLM 5.2 measured |
| Chess puzzles | 22.0% | 14.0% | GLM 5.2 |
| Mystery game puzzles | 19.0% | 12.0% | GLM 5.2 |
| EBR-bench | 9.5% | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts | 1481 | 1448 | GLM 5.2 |
| LMArena Text: expert | 1487 | 1454 | GLM 5.2 |
| LMArena Text: hard prompts in English | 1481 | 1455 | GLM 5.2 |
| Neither model has a result on the other 7 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 5.2 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| AIME | Not measured | 94.6% | Only Qwen3.6 Plus measured |
| OTIS Mock AIME | 86.4% | 93.3% | Qwen3.6 Plus |
| FrontierMath, tiers 1 to 3 | 59.2% | 38.2% | GLM 5.2 |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.2 measured |
| ProofBench | 35.0% | Not measured | Only GLM 5.2 measured |
| LMArena Text: math | 1483 | 1449 | GLM 5.2 |
| LMArena Text: mathematical work | 1489 | 1448 | GLM 5.2 |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index64
| Benchmark | GLM 5.2 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | 87.7% | Qwen3.6 Plus |
| LegalBench (Vals AI) | 84.1% | 84.2% | Qwen3.6 Plus |
| CaseLaw (Vals AI) | Not measured | 51.5% | Only Qwen3.6 Plus measured |
| CorpFin (Vals AI) | 66.1% | 61.9% | GLM 5.2 |
| TaxEval (Vals AI) | 73.3% | 74.7% | Qwen3.6 Plus |
| MortgageTax (Vals AI) | Not measured | 68.0% | Only Qwen3.6 Plus measured |
| MedCode (Vals AI) | 40.8% | 36.9% | GLM 5.2 |
| SimpleQA Verified | 34.2% | 44.1% | Qwen3.6 Plus |
| LMArena Text: science | 1489 | 1439 | GLM 5.2 |
| LMArena Text: medicine and healthcare | 1475 | 1442 | GLM 5.2 |
| LMArena Text: legal and government | 1486 | 1437 | GLM 5.2 |
| LMArena Text: business and finance | 1451 | 1430 | GLM 5.2 |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | GLM 5.2 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | 1243 | GLM 5.2 |
| Design Arena: UI components | 1296 | 1233 | GLM 5.2 |
| Design Arena: game dev | 1268 | 1211 | GLM 5.2 |
| Design Arena: data viz | 1285 | 1228 | GLM 5.2 |
| Design Arena: 3D | 1288 | 1202 | GLM 5.2 |
| Design Arena: all code categories | 1288 | 1236 | GLM 5.2 |
| Design Arena: SVG | 1214 | 1160 | GLM 5.2 |
| Design Arena: ASCII art | 1190 | 1115 | GLM 5.2 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index201
| Benchmark | GLM 5.2 | Qwen3.6 Plus | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 44.9% | Only Qwen3.6 Plus measured |
| MedScribe (Vals AI) | 83.5% | 77.0% | GLM 5.2 |
| Vals Multimodal Index | Not measured | 51.5% | Only Qwen3.6 Plus measured |
| IFBench | 73.3% | 75.2% | Qwen3.6 Plus |
| Long context reasoning (AA-LCR) | 78.3% | 78.3% | Tie |
| DTBench | 93.6% | 81.9% | GLM 5.2 |
| LMCA | 45.8% | 33.1% | GLM 5.2 |
| CL-bench | Not measured | 20.3% | Only Qwen3.6 Plus measured |
| Surface Evolver Bench | 55.6% | Not measured | Only GLM 5.2 measured |
| LMArena Text | 1470 | 1436 | GLM 5.2 |
| LMArena Text: instruction following | 1466 | 1424 | GLM 5.2 |
| LMArena Text: creative writing | 1463 | 1404 | GLM 5.2 |
| LMArena Text: English | 1475 | 1445 | GLM 5.2 |
| LMArena Text: non-English | 1459 | 1424 | GLM 5.2 |
| LMArena Text: Chinese | 1522 | 1477 | GLM 5.2 |
| LMArena Text: German | 1470 | 1451 | GLM 5.2 |
| LMArena Text: Japanese | 1450 | 1390 | GLM 5.2 |
| LMArena Text: Korean | 1440 | 1379 | GLM 5.2 |
| LMArena Text: French | 1479 | 1455 | GLM 5.2 |
| LMArena Text: Spanish | 1478 | 1432 | GLM 5.2 |
| LMArena Text: Russian | 1468 | 1434 | GLM 5.2 |
| LMArena Text: Polish | 1456 | 1433 | GLM 5.2 |
| LMArena Text: multi-turn | 1469 | 1438 | GLM 5.2 |
| LMArena Text: longer queries | 1480 | 1439 | GLM 5.2 |
| LMArena Text: writing and language | 1463 | 1418 | GLM 5.2 |
| LMArena Text: entertainment and media | 1450 | 1402 | GLM 5.2 |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | Qwen3.6 Plus | 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
Qwen3.6 Plus costs less
Repository review
50K input + 3K output tokens
Qwen3.6 Plus costs less
Agent loop
200K input + 10K output tokens
GLM 5.2 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 | Qwen3.6 Plus |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | June 16, 2026 | April 2, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $0.0341 | $0.325 |
| Output $/Mtok | $7.00 | $1.95 |
| Coding index | 57.7 | 37.4 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.86 | $1.19 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than Qwen3.6 Plus for coding?
Pick GLM 5.2 for coding: it scores 57.7 on the Vibe Coding Index against 37.4 for Qwen3.6 Plus. Pick Qwen3.6 Plus when output tokens cost 3.6x less, or when what matters is LiveCodeBench (Vals AI) or GPQA Diamond, where it leads.
Which is cheaper, GLM 5.2 or Qwen3.6 Plus?
GLM 5.2 costs $0.0341 per million input tokens and $7.00 per million output tokens; Qwen3.6 Plus costs $0.325 and $1.95. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and Qwen3.6 Plus $1.19.
Which has the bigger context window, GLM 5.2 or Qwen3.6 Plus?
GLM 5.2, with 1.05M tokens against 1M.
How many benchmarks is the GLM 5.2 vs Qwen3.6 Plus comparison based on?
74 benchmarks have a result for both models as of October 21, 2026. GLM 5.2's coding index rests on 1 independent benchmark and Qwen3.6 Plus's on 1. Where only one model has a result, the page says so instead of naming a winner.