GLM 5.2 vs Qwen3.7 Max
Updated October 21, 2026. 58 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 45.1 for Qwen3.7 Max.
Pick Qwen3.7 Max when output tokens cost 1.6x less, or when what matters is LiveCodeBench (Vals AI) or ALE-Bench, where it leads.
- Shared results
- 58
- GLM 5.2 only
- 39
- Qwen3.7 Max only
- 1
- 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 45.1.
One benchmark behind each score
Intelligence
AA Intelligence Index
GLM 5.2
GLM 5.2 leads, 33.7 against 29.5.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.2
GLM 5.2 leads, 38.4 against 22.5.
Same board, both measured
Reasoning
GPQA Diamond
Qwen3.7 Max
Qwen3.7 Max leads, 90.2% against 85.6%.
Same board, both measured
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 86.7%.
Same board, both measured
Web design
Design Arena: websites
GLM 5.2
GLM 5.2 leads, 1285 against 1274.
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
GLM 5.2
GLM 5.2 $0.0035, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5.2
GLM 5.2 $0.86, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5.2 57.7 from 1 benchmark, Qwen3.7 Max 45.1 from 1 benchmark.
- Widest category
- Coding, 10.4 points apart on average over 12 shared benchmarks.
- Widest single gap
- Code migration (Vals AI): GLM 5.2 37.9% against Qwen3.7 Max 13.1%, 24.8 points apart on a 0 to 100 scale.
- Output price
- GLM 5.2 $7.00 against Qwen3.7 Max $4.425 per million output tokens, 1.6x apart.
- Context window
- GLM 5.2 takes 1.05M tokens, Qwen3.7 Max 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 | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 9 | 2 | 1 tie | |
| Intelligence | 1 | 1 | ||
| Agentic work | 11 | 2 | ||
| Reasoning | 2 | 5 | 1 tie | |
| Math | 1 | 3 | ||
| Knowledge | 2 | 4 | ||
| Web design | 6 | 2 | ||
| General | 3 | 2 | ||
| All shared boards | 35 | 21 | 2 ties |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index92
| Benchmark | GLM 5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| LMArena WebDev | 1604 | 1515 | GLM 5.2 |
| AA Coding Index | 68.8 | 66.0 | GLM 5.2 |
| LiveCodeBench (Vals AI) | 69.5% | 87.1% | Qwen3.7 Max |
| SWE-bench Verified (Vals AI) | 82.8% | 68.8% | GLM 5.2 |
| Vibe Code Bench (Vals AI) | 64.0% | 47.7% | GLM 5.2 |
| Code migration (Vals AI) | 37.9% | 13.1% | GLM 5.2 |
| ProgramBench (Vals AI) | 0.5% | Not measured | Only GLM 5.2 measured |
| Terminal-Bench 2.1 (Vals AI) | 67.8% | 61.0% | GLM 5.2 |
| SkillsBench (Vals AI) | 45.1% | Not measured | Only GLM 5.2 measured |
| SciCode | 51.2% | 49.5% | GLM 5.2 |
| Terminal-Bench Hard (Artificial Analysis run) | 50.8% | 50.8% | Tie |
| FrontierCode | 24.5% | Not measured | Only GLM 5.2 measured |
| WeirdML | 70.1% | Not measured | Only GLM 5.2 measured |
| ALE-Bench | 1047.0 | 1189.4 | Qwen3.7 Max |
| LMArena Text: coding | 1487 | Not measured | Only GLM 5.2 measured |
| LMArena Text: software and IT | 1486 | Not measured | Only GLM 5.2 measured |
| LMArena WebDev: React | 1617 | 1514 | GLM 5.2 |
| LMArena WebDev: HTML | 1565 | 1512 | GLM 5.2 |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index11
| Benchmark | GLM 5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 33.7 | 29.5 | GLM 5.2 |
| Epoch Capabilities Index | 151.9 | 154.0 | Qwen3.7 Max |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index112
| Benchmark | GLM 5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | 38.4 | 22.5 | GLM 5.2 |
| Design Arena: agents building web apps | 1222 | 1208 | GLM 5.2 |
| Design Arena: agents building full-stack apps | 1241 | 1191 | GLM 5.2 |
| Design Arena: agents building mobile apps | 1160 | 1153 | GLM 5.2 |
| Design Arena: agents building Android apps | 1184 | 1165 | GLM 5.2 |
| Design Arena: agents building Godot games | 1142 | 1187 | Qwen3.7 Max |
| Design Arena: agents building games | 1206 | 1161 | GLM 5.2 |
| Design Arena: HTML slides | 1175 | 1158 | GLM 5.2 |
| Design Arena: PowerPoint slides | 1195 | 1196 | Qwen3.7 Max |
| SREBench (Vals AI) | 0.0% | Not measured | Only GLM 5.2 measured |
| Finance Agent (Vals AI) | 49.7% | 47.8% | GLM 5.2 |
| Tax Agent Bench (Vals AI) | Not measured | 15.8% | Only Qwen3.7 Max measured |
| Legal Research Bench (Vals AI) | 31.3% | 25.5% | GLM 5.2 |
| Excel Modeling Benchmark (Vals AI) | 61.5% | 57.0% | GLM 5.2 |
| τ²-Bench Telecom | 99.1% | 94.7% | 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 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 21 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index25
| Benchmark | GLM 5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | 90.2% | Qwen3.7 Max |
| GPQA Diamond (Artificial Analysis run) | 89.5% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 41.1% | 40.5% | GLM 5.2 |
| GPQA Diamond (Epoch AI run) | 91.9% | 90.9% | 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% | 70.4% | Qwen3.7 Max |
| Chess puzzles | 22.0% | 26.0% | Qwen3.7 Max |
| Mystery game puzzles | 19.0% | 32.0% | Qwen3.7 Max |
| EBR-bench | 9.5% | 9.5% | Tie |
| LMArena Text: hard prompts | 1481 | Not measured | Only GLM 5.2 measured |
| LMArena Text: expert | 1487 | Not measured | Only GLM 5.2 measured |
| LMArena Text: hard prompts in English | 1481 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index13
| Benchmark | GLM 5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| OTIS Mock AIME | 86.4% | 95.6% | Qwen3.7 Max |
| FrontierMath, tiers 1 to 3 | 59.2% | 64.6% | Qwen3.7 Max |
| FrontierMath, tier 4 | 29.3% | 34.1% | Qwen3.7 Max |
| ProofBench | 35.0% | 26.0% | GLM 5.2 |
| LMArena Text: math | 1483 | Not measured | Only GLM 5.2 measured |
| LMArena Text: mathematical work | 1489 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index24
| Benchmark | GLM 5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | 86.7% | 89.3% | Qwen3.7 Max |
| LegalBench (Vals AI) | 84.1% | 84.9% | Qwen3.7 Max |
| CorpFin (Vals AI) | 66.1% | 63.7% | GLM 5.2 |
| TaxEval (Vals AI) | 73.3% | 75.3% | Qwen3.7 Max |
| MedCode (Vals AI) | 40.8% | 38.8% | GLM 5.2 |
| SimpleQA Verified | 34.2% | 55.8% | Qwen3.7 Max |
| LMArena Text: science | 1489 | Not measured | Only GLM 5.2 measured |
| LMArena Text: medicine and healthcare | 1475 | Not measured | Only GLM 5.2 measured |
| LMArena Text: legal and government | 1486 | Not measured | Only GLM 5.2 measured |
| LMArena Text: business and finance | 1451 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index62
| Benchmark | GLM 5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1285 | 1274 | GLM 5.2 |
| Design Arena: UI components | 1296 | 1269 | GLM 5.2 |
| Design Arena: game dev | 1268 | 1265 | GLM 5.2 |
| Design Arena: data viz | 1285 | 1275 | GLM 5.2 |
| Design Arena: 3D | 1288 | 1266 | GLM 5.2 |
| Design Arena: all code categories | 1288 | 1275 | GLM 5.2 |
| Design Arena: SVG | 1214 | 1216 | Qwen3.7 Max |
| Design Arena: ASCII art | 1190 | 1211 | Qwen3.7 Max |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index32
| Benchmark | GLM 5.2 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | 83.5% | 79.4% | GLM 5.2 |
| IFBench | 73.3% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 78.3% | 79.0% | Qwen3.7 Max |
| DTBench | 93.6% | 92.3% | GLM 5.2 |
| LMCA | 45.8% | 44.0% | GLM 5.2 |
| Surface Evolver Bench | 55.6% | Not measured | Only GLM 5.2 measured |
| LMArena Text | 1470 | Not measured | Only GLM 5.2 measured |
| LMArena Text: instruction following | 1466 | Not measured | Only GLM 5.2 measured |
| LMArena Text: creative writing | 1463 | Not measured | Only GLM 5.2 measured |
| LMArena Text: English | 1475 | Not measured | Only GLM 5.2 measured |
| LMArena Text: non-English | 1459 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Chinese | 1522 | Not measured | Only GLM 5.2 measured |
| LMArena Text: German | 1470 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Japanese | 1450 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Korean | 1440 | Not measured | Only GLM 5.2 measured |
| LMArena Text: French | 1479 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Spanish | 1478 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Russian | 1468 | Not measured | Only GLM 5.2 measured |
| LMArena Text: Polish | 1456 | Not measured | Only GLM 5.2 measured |
| LMArena Text: multi-turn | 1469 | Not measured | Only GLM 5.2 measured |
| LMArena Text: longer queries | 1480 | Not measured | Only GLM 5.2 measured |
| LMArena Text: writing and language | 1463 | Not measured | Only GLM 5.2 measured |
| LMArena Text: entertainment and media | 1450 | Not measured | Only GLM 5.2 measured |
| Neither model has a result on the other 23 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.2 | Qwen3.7 Max | 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 5.2 costs less
Repository review
50K input + 3K output tokens
GLM 5.2 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 | Qwen3.7 Max |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | June 16, 2026 | May 21, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $0.0341 | $1.475 |
| Output $/Mtok | $7.00 | $4.425 |
| Coding index | 57.7 | 45.1 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | $0.86 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.2 better than Qwen3.7 Max for coding?
Pick GLM 5.2 for coding: it scores 57.7 on the Vibe Coding Index against 45.1 for Qwen3.7 Max. Pick Qwen3.7 Max when output tokens cost 1.6x less, or when what matters is LiveCodeBench (Vals AI) or ALE-Bench, where it leads.
Which is cheaper, GLM 5.2 or Qwen3.7 Max?
GLM 5.2 costs $0.0341 per million input tokens and $7.00 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, GLM 5.2 costs $0.86 and Qwen3.7 Max $4.69.
Which has the bigger context window, GLM 5.2 or Qwen3.7 Max?
GLM 5.2, with 1.05M tokens against 1M.
How many benchmarks is the GLM 5.2 vs Qwen3.7 Max comparison based on?
58 benchmarks have a result for both models as of October 21, 2026. GLM 5.2's coding index rests on 1 independent benchmark and Qwen3.7 Max's on 1. Where only one model has a result, the page says so instead of naming a winner.