GLM 5 vs Qwen3.7 Max
Updated October 21, 2026. 36 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 for coding: it scores 59.7 on the Vibe Coding Index against 45.1 for Qwen3.7 Max.
Pick Qwen3.7 Max when the context window is larger, 1M against 205K tokens, or when what matters is LMArena WebDev or LiveCodeBench (Vals AI), where it leads.
- Shared results
- 36
- GLM 5 only
- 39
- Qwen3.7 Max only
- 23
- 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
GLM 5 scores 59.7 against 45.1.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
Qwen3.7 Max
Qwen3.7 Max leads, 29.5 against 27.9.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Max has a AA Agentic Index result, 22.5.
Directional only
Reasoning
GPQA Diamond
Qwen3.7 Max
Qwen3.7 Max leads, 90.2% against 83.3%.
Same board, both measured
Math
AIME
No call
Only GLM 5 has a AIME result, 91.7%.
Directional only
Knowledge
MMLU Pro
Qwen3.7 Max
Qwen3.7 Max leads, 89.3% against 86.0%.
Same board, both measured
Web design
Design Arena: websites
Qwen3.7 Max
Qwen3.7 Max leads, 1274 against 1247.
Same board, both measured
Long documents
Listed context window
Qwen3.7 Max
Qwen3.7 Max takes 1M tokens against 205K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
GLM 5
GLM 5 $0.0026, Qwen3.7 Max $0.0037 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
GLM 5
GLM 5 $0.63, Qwen3.7 Max $4.69.
Published attempt cost and pass rate
At a glance
- Coding index
- GLM 5 59.7 from 2 benchmarks, Qwen3.7 Max 45.1 from 1 benchmark.
- Widest category
- Math, 15.6 points apart on average over 1 shared benchmark.
- Widest single gap
- Vibe Code Bench (Vals AI): GLM 5 23.4% against Qwen3.7 Max 47.7%, 24.3 points apart on a 0 to 100 scale.
- Output price
- GLM 5 $3.20 against Qwen3.7 Max $4.425 per million output tokens, 1.4x apart.
- Context window
- GLM 5 takes 205K 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 | Bars | Qwen3.7 Max | Ties |
|---|---|---|---|---|
| Coding | 1 | 7 | ||
| Intelligence | 0 | 2 | ||
| Agentic work | 1 | 5 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 1 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 8 | ||
| General | 0 | 2 | ||
| All shared boards | 2 | 34 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index17
| Benchmark | GLM 5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| SWE-bench Verified | 72.8% | Not measured | Only GLM 5 measured |
| SWE-bench Multilingual | 69.7% | Not measured | Only GLM 5 measured |
| LMArena WebDev | 1435 | 1515 | Qwen3.7 Max |
| AA Coding Index | Not measured | 66.0 | Only Qwen3.7 Max measured |
| LiveCodeBench (Vals AI) | 81.9% | 87.1% | Qwen3.7 Max |
| SWE-bench Verified (Vals AI) | 71.4% | 68.8% | GLM 5 |
| Vibe Code Bench (Vals AI) | 23.4% | 47.7% | Qwen3.7 Max |
| Code migration (Vals AI) | Not measured | 13.1% | Only Qwen3.7 Max measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 61.0% | Only Qwen3.7 Max measured |
| SciCode | Not measured | 49.5% | Only Qwen3.7 Max measured |
| Terminal-Bench Hard (Artificial Analysis run) | 43.2% | 50.8% | Qwen3.7 Max |
| Terminal-Bench | 52.4% | Not measured | Only GLM 5 measured |
| WeirdML | 48.2% | Not measured | Only GLM 5 measured |
| ALE-Bench | 765.6 | 1189.4 | Qwen3.7 Max |
| LMArena Text: coding | 1461 | Not measured | Only GLM 5 measured |
| LMArena Text: software and IT | 1460 | Not measured | Only GLM 5 measured |
| LMArena WebDev: React | 1424 | 1514 | Qwen3.7 Max |
| LMArena WebDev: HTML | 1450 | 1512 | Qwen3.7 Max |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index02
| Benchmark | GLM 5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Intelligence Index | 27.9 | 29.5 | Qwen3.7 Max |
| Epoch Capabilities Index | 145.8 | 154.0 | Qwen3.7 Max |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index15
| Benchmark | GLM 5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 22.5 | Only Qwen3.7 Max measured |
| Design Arena: agents building web apps | Not measured | 1208 | Only Qwen3.7 Max measured |
| Design Arena: agents building full-stack apps | 1123 | 1191 | Qwen3.7 Max |
| Design Arena: agents building mobile apps | 1136 | 1153 | Qwen3.7 Max |
| Design Arena: agents building Android apps | 1152 | 1165 | Qwen3.7 Max |
| Design Arena: agents building Godot games | 1144 | 1187 | Qwen3.7 Max |
| Design Arena: agents building games | Not measured | 1161 | Only Qwen3.7 Max measured |
| Design Arena: HTML slides | 1148 | 1158 | Qwen3.7 Max |
| Design Arena: PowerPoint slides | Not measured | 1196 | Only Qwen3.7 Max measured |
| Finance Agent (Vals AI) | Not measured | 47.8% | Only Qwen3.7 Max measured |
| Tax Agent Bench (Vals AI) | Not measured | 15.8% | Only Qwen3.7 Max measured |
| Legal Research Bench (Vals AI) | Not measured | 25.5% | Only Qwen3.7 Max measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 57.0% | Only Qwen3.7 Max measured |
| τ²-Bench Telecom | 98.2% | 94.7% | GLM 5 |
| Vending-Bench 2 | $4,432 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 24 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GLM 5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| GPQA Diamond | 83.3% | 90.2% | Qwen3.7 Max |
| GPQA Diamond (Artificial Analysis run) | 82.0% | 92.3% | Qwen3.7 Max |
| Humanity's Last Exam (Artificial Analysis run) | 29.3% | 40.5% | Qwen3.7 Max |
| GPQA Diamond (Epoch AI run) | 87.8% | 90.9% | Qwen3.7 Max |
| ARC-AGI | 44.7% | Not measured | Only GLM 5 measured |
| ARC-AGI-2 | 4.9% | Not measured | Only GLM 5 measured |
| SimpleBench | 53.2% | 70.4% | Qwen3.7 Max |
| Chess puzzles | Not measured | 26.0% | Only Qwen3.7 Max measured |
| Mystery game puzzles | Not measured | 32.0% | Only Qwen3.7 Max measured |
| EBR-bench | Not measured | 9.5% | Only Qwen3.7 Max measured |
| LMArena Text: hard prompts | 1452 | Not measured | Only GLM 5 measured |
| LMArena Text: expert | 1454 | Not measured | Only GLM 5 measured |
| LMArena Text: hard prompts in English | 1460 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 8 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| AIME | 91.7% | Not measured | Only GLM 5 measured |
| OTIS Mock AIME | 80.0% | 95.6% | Qwen3.7 Max |
| FrontierMath, tiers 1 to 3 | Not measured | 64.6% | Only Qwen3.7 Max measured |
| FrontierMath, tier 4 | Not measured | 34.1% | Only Qwen3.7 Max measured |
| ProofBench | Not measured | 26.0% | Only Qwen3.7 Max measured |
| LMArena Text: math | 1440 | Not measured | Only GLM 5 measured |
| LMArena Text: mathematical work | 1451 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 6 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MMLU Pro | 86.0% | 89.3% | Qwen3.7 Max |
| LegalBench (Vals AI) | 84.1% | 84.9% | Qwen3.7 Max |
| CaseLaw (Vals AI) | 52.5% | Not measured | Only GLM 5 measured |
| CorpFin (Vals AI) | 62.9% | 63.7% | Qwen3.7 Max |
| TaxEval (Vals AI) | 70.0% | 75.3% | Qwen3.7 Max |
| MedQA (Vals AI) | 94.3% | Not measured | Only GLM 5 measured |
| MedCode (Vals AI) | Not measured | 38.8% | Only Qwen3.7 Max measured |
| SimpleQA Verified | Not measured | 55.8% | Only Qwen3.7 Max measured |
| LMArena Text: science | 1460 | Not measured | Only GLM 5 measured |
| LMArena Text: medicine and healthcare | 1455 | Not measured | Only GLM 5 measured |
| LMArena Text: legal and government | 1453 | Not measured | Only GLM 5 measured |
| LMArena Text: business and finance | 1430 | Not measured | Only GLM 5 measured |
| Neither model has a result on the other 5 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index08
| Benchmark | GLM 5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| Design Arena: websites | 1247 | 1274 | Qwen3.7 Max |
| Design Arena: UI components | 1230 | 1269 | Qwen3.7 Max |
| Design Arena: game dev | 1234 | 1265 | Qwen3.7 Max |
| Design Arena: data viz | 1228 | 1275 | Qwen3.7 Max |
| Design Arena: 3D | 1229 | 1266 | Qwen3.7 Max |
| Design Arena: all code categories | 1246 | 1275 | Qwen3.7 Max |
| Design Arena: SVG | 1168 | 1216 | Qwen3.7 Max |
| Design Arena: ASCII art | 1146 | 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 index02
| Benchmark | GLM 5 | Qwen3.7 Max | Leader |
|---|---|---|---|
| MedScribe (Vals AI) | Not measured | 79.4% | Only Qwen3.7 Max measured |
| IFBench | 72.3% | 80.5% | Qwen3.7 Max |
| Long context reasoning (AA-LCR) | 75.7% | 79.0% | Qwen3.7 Max |
| DTBench | Not measured | 92.3% | Only Qwen3.7 Max measured |
| LMCA | Not measured | 44.0% | Only Qwen3.7 Max measured |
| CL-bench | 18.7% | Not measured | Only GLM 5 measured |
| LMArena Text | 1446 | Not measured | Only GLM 5 measured |
| LMArena Text: instruction following | 1428 | Not measured | Only GLM 5 measured |
| LMArena Text: creative writing | 1438 | Not measured | Only GLM 5 measured |
| LMArena Text: English | 1458 | Not measured | Only GLM 5 measured |
| LMArena Text: non-English | 1430 | Not measured | Only GLM 5 measured |
| LMArena Text: Chinese | 1511 | Not measured | Only GLM 5 measured |
| LMArena Text: German | 1447 | Not measured | Only GLM 5 measured |
| LMArena Text: Japanese | 1416 | Not measured | Only GLM 5 measured |
| LMArena Text: Korean | 1423 | Not measured | Only GLM 5 measured |
| LMArena Text: French | 1456 | Not measured | Only GLM 5 measured |
| LMArena Text: Spanish | 1454 | Not measured | Only GLM 5 measured |
| LMArena Text: Russian | 1436 | Not measured | Only GLM 5 measured |
| LMArena Text: Polish | 1434 | Not measured | Only GLM 5 measured |
| LMArena Text: multi-turn | 1456 | Not measured | Only GLM 5 measured |
| LMArena Text: longer queries | 1446 | Not measured | Only GLM 5 measured |
| LMArena Text: writing and language | 1437 | Not measured | Only GLM 5 measured |
| LMArena Text: entertainment and media | 1426 | Not measured | Only GLM 5 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 | 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 costs less
Repository review
50K input + 3K output tokens
GLM 5 costs less
Agent loop
200K input + 10K output tokens
GLM 5 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 | Qwen3.7 Max |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | February 11, 2026 | May 21, 2026 |
| Context window | 205K | 1M |
| Input $/Mtok | $1.00 | $1.475 |
| Output $/Mtok | $3.20 | $4.425 |
| Coding index | 59.7 | 45.1 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | $0.63 | $4.69 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5 better than Qwen3.7 Max for coding?
Pick GLM 5 for coding: it scores 59.7 on the Vibe Coding Index against 45.1 for Qwen3.7 Max. Pick Qwen3.7 Max when the context window is larger, 1M against 205K tokens, or when what matters is LMArena WebDev or LiveCodeBench (Vals AI), where it leads.
Which is cheaper, GLM 5 or Qwen3.7 Max?
GLM 5 costs $1.00 per million input tokens and $3.20 per million output tokens; Qwen3.7 Max costs $1.475 and $4.425. Per solved SWE-bench Verified task, GLM 5 costs $0.63 and Qwen3.7 Max $4.69.
Which has the bigger context window, GLM 5 or Qwen3.7 Max?
Qwen3.7 Max, with 1M tokens against 205K.
How many benchmarks is the GLM 5 vs Qwen3.7 Max comparison based on?
36 benchmarks have a result for both models as of October 21, 2026. GLM 5's coding index rests on 2 independent benchmarks and Qwen3.7 Max's on 1. Where only one model has a result, the page says so instead of naming a winner.