GLM 4.5 vs Qwen3.7 Plus
Qwen3.7 Plus leads GLM 4.5 on the Artificial Analysis Intelligence Index, 25.2 to 12.8, and Qwen3.7 Plus costs 1.7x less per output token ($1.28 vs $2.20 per million). Updated October 21, 2026; 42 benchmarks have a result for both.
GLM 4.5 or Qwen3.7 Plus: which to pick
Only Qwen3.7 Plus has a coding score, 33.2 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding.
Pick GLM 4.5 for work measured by LMArena Text: Japanese, where it leads.
- Shared results
- 42
- GLM 4.5 only
- 18
- Qwen3.7 Plus only
- 33
- Like-for-like categories
- 8 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 Qwen3.7 Plus has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Qwen3.7 Plus
Qwen3.7 Plus leads, 25.2 against 12.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.7 Plus has a AA Agentic Index result, 17.5.
Directional only
Reasoning
GPQA Diamond
No call
Only GLM 4.5 has a GPQA Diamond result, 72.2%.
Directional only
Math
AIME
No call
Only GLM 4.5 has a AIME result, 86.7%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 4.5 has a MMLU Pro result, 81.2%.
Directional only
Web design
Design Arena: websites
Qwen3.7 Plus
Qwen3.7 Plus leads, 1269 against 1170.
Same board, both measured
Long documents
Listed context window
Qwen3.7 Plus
Qwen3.7 Plus takes 1M tokens against 131K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Plus
GLM 4.5 $0.0017, Qwen3.7 Plus $0.0010 per turn.
List prices
At a glance
- Coding index
- GLM 4.5 not benchmarked, Qwen3.7 Plus 33.2 from 2 benchmarks.
- Widest category
- Agentic work, 50.0 points apart on average over 1 shared benchmark.
- Widest single gap
- τ²-Bench Telecom: GLM 4.5 43.0% against Qwen3.7 Plus 93.0%, 50.0 points apart on a 0 to 100 scale.
- Output price
- GLM 4.5 $2.20 against Qwen3.7 Plus $1.28 per million output tokens, 1.7x apart.
- Context window
- GLM 4.5 takes 131K tokens, Qwen3.7 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 4.5 | Bars | Qwen3.7 Plus | Ties |
|---|---|---|---|---|
| Coding | 0 | 3 | ||
| Intelligence | 0 | 1 | ||
| Agentic work | 0 | 1 | ||
| Reasoning | 0 | 5 | ||
| Math | 0 | 2 | ||
| Knowledge | 0 | 4 | ||
| Web design | 0 | 7 | ||
| General | 1 | 18 | ||
| All shared boards | 1 | 41 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index03
| Benchmark | GLM 4.5 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| SWE-bench Verified | 64.2% | Not measured | Only GLM 4.5 measured |
| AA Coding Index | Not measured | 55.9 | Only Qwen3.7 Plus measured |
| LiveCodeBench (Vals AI) | 67.5% | Not measured | Only GLM 4.5 measured |
| Vibe Code Bench (Vals AI) | Not measured | 46.4% | Only Qwen3.7 Plus measured |
| Code migration (Vals AI) | Not measured | 12.9% | Only Qwen3.7 Plus measured |
| Terminal-Bench 2.1 (Vals AI) | Not measured | 52.8% | Only Qwen3.7 Plus measured |
| SkillsBench (Vals AI) | Not measured | 54.3% | Only Qwen3.7 Plus measured |
| LiveCodeBench (Artificial Analysis run) | 73.8% | Not measured | Only GLM 4.5 measured |
| SciCode | Not measured | 46.1% | Only Qwen3.7 Plus measured |
| Terminal-Bench Hard (Artificial Analysis run) | 22.0% | 47.0% | Qwen3.7 Plus |
| FrontierCode | Not measured | 10.2% | Only Qwen3.7 Plus measured |
| WeirdML | 40.6% | Not measured | Only GLM 4.5 measured |
| ALE-Bench | 344.8 | Not measured | Only GLM 4.5 measured |
| LMArena Text: coding | 1434 | 1473 | Qwen3.7 Plus |
| LMArena Text: software and IT | 1440 | 1473 | Qwen3.7 Plus |
| Neither model has a result on the other 23 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 12.8 | 25.2 | Qwen3.7 Plus |
| Epoch Capabilities Index | Not measured | 147.0 | Only Qwen3.7 Plus measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GLM 4.5 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 17.5 | Only Qwen3.7 Plus measured |
| Finance Agent (Vals AI) | Not measured | 38.2% | Only Qwen3.7 Plus measured |
| Tax Agent Bench (Vals AI) | Not measured | 10.5% | Only Qwen3.7 Plus measured |
| Legal Research Bench (Vals AI) | Not measured | 16.4% | Only Qwen3.7 Plus measured |
| Excel Modeling Benchmark (Vals AI) | Not measured | 49.3% | Only Qwen3.7 Plus measured |
| τ²-Bench Telecom | 43.0% | 93.0% | Qwen3.7 Plus |
| OSWorld 2.0 | Not measured | 2.8% | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 32 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GLM 4.5 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 72.2% | Not measured | Only GLM 4.5 measured |
| GPQA Diamond (Artificial Analysis run) | 78.2% | 90.0% | Qwen3.7 Plus |
| Humanity's Last Exam (Artificial Analysis run) | 13.0% | 35.6% | Qwen3.7 Plus |
| GPQA Diamond (Epoch AI run) | Not measured | 87.9% | Only Qwen3.7 Plus measured |
| Mystery game puzzles | Not measured | 17.0% | Only Qwen3.7 Plus measured |
| LMArena Text: hard prompts | 1429 | 1460 | Qwen3.7 Plus |
| LMArena Text: expert | 1433 | 1466 | Qwen3.7 Plus |
| LMArena Text: hard prompts in English | 1429 | 1460 | Qwen3.7 Plus |
| LMArena Vision: diagrams | Not measured | 1292 | Only Qwen3.7 Plus measured |
| LMArena Vision: homework | Not measured | 1295 | Only Qwen3.7 Plus measured |
| 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 4.5 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AIME | 86.7% | Not measured | Only GLM 4.5 measured |
| MATH 500 (Vals AI) | 94.0% | Not measured | Only GLM 4.5 measured |
| MGSM (Vals AI) | 90.8% | Not measured | Only GLM 4.5 measured |
| AA Math Index | 73.7 | Not measured | Only GLM 4.5 measured |
| AIME 2025 (Artificial Analysis run) | 73.7% | Not measured | Only GLM 4.5 measured |
| OTIS Mock AIME | Not measured | 93.3% | Only Qwen3.7 Plus measured |
| FrontierMath, tiers 1 to 3 | Not measured | 34.4% | Only Qwen3.7 Plus measured |
| LMArena Text: math | 1427 | 1467 | Qwen3.7 Plus |
| LMArena Text: mathematical work | 1430 | 1470 | Qwen3.7 Plus |
| Neither model has a result on the other 4 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index04
| Benchmark | GLM 4.5 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| MMLU Pro | 81.2% | Not measured | Only GLM 4.5 measured |
| LegalBench (Vals AI) | 75.6% | Not measured | Only GLM 4.5 measured |
| CorpFin (Vals AI) | 61.0% | Not measured | Only GLM 4.5 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 4.5 measured |
| MortgageTax (Vals AI) | Not measured | 66.2% | Only Qwen3.7 Plus measured |
| MedQA (Vals AI) | 90.0% | Not measured | Only GLM 4.5 measured |
| MMLU Pro (Artificial Analysis run) | 83.5% | Not measured | Only GLM 4.5 measured |
| LMArena Text: science | 1444 | 1464 | Qwen3.7 Plus |
| LMArena Text: medicine and healthcare | 1455 | 1464 | Qwen3.7 Plus |
| LMArena Text: legal and government | 1438 | 1451 | Qwen3.7 Plus |
| LMArena Text: business and finance | 1420 | 1435 | Qwen3.7 Plus |
| Neither model has a result on the other 6 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index07
| Benchmark | GLM 4.5 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| Design Arena: websites | 1170 | 1269 | Qwen3.7 Plus |
| Design Arena: UI components | 1144 | 1261 | Qwen3.7 Plus |
| Design Arena: game dev | 1145 | 1262 | Qwen3.7 Plus |
| Design Arena: data viz | 1159 | 1276 | Qwen3.7 Plus |
| Design Arena: 3D | 1170 | 1239 | Qwen3.7 Plus |
| Design Arena: all code categories | 1165 | 1269 | Qwen3.7 Plus |
| Design Arena: SVG | 1096 | 1208 | Qwen3.7 Plus |
| Design Arena: ASCII art | Not measured | 1143 | Only Qwen3.7 Plus measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index118
| Benchmark | GLM 4.5 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| SAGE (Vals AI) | Not measured | 39.3% | Only Qwen3.7 Plus measured |
| Vals Multimodal Index | Not measured | 53.9% | Only Qwen3.7 Plus measured |
| IFBench | 44.1% | 78.0% | Qwen3.7 Plus |
| Long context reasoning (AA-LCR) | 52.7% | 73.0% | Qwen3.7 Plus |
| DTBench | Not measured | 84.0% | Only Qwen3.7 Plus measured |
| LMCA | Not measured | 37.6% | Only Qwen3.7 Plus measured |
| Creative writing (Lech Mazur) | 73.4% | Not measured | Only GLM 4.5 measured |
| LMArena Text | 1430 | 1454 | Qwen3.7 Plus |
| LMArena Text: instruction following | 1404 | 1439 | Qwen3.7 Plus |
| LMArena Text: creative writing | 1395 | 1438 | Qwen3.7 Plus |
| LMArena Vision | Not measured | 1278 | Only Qwen3.7 Plus measured |
| LMArena Text: English | 1437 | 1458 | Qwen3.7 Plus |
| LMArena Text: non-English | 1417 | 1445 | Qwen3.7 Plus |
| LMArena Text: Chinese | 1465 | 1509 | Qwen3.7 Plus |
| LMArena Text: German | 1407 | 1471 | Qwen3.7 Plus |
| LMArena Text: Japanese | 1415 | 1412 | GLM 4.5 |
| LMArena Text: Korean | 1380 | 1413 | Qwen3.7 Plus |
| LMArena Text: French | 1418 | 1472 | Qwen3.7 Plus |
| LMArena Text: Spanish | 1454 | 1457 | Qwen3.7 Plus |
| LMArena Text: Russian | 1413 | 1457 | Qwen3.7 Plus |
| LMArena Text: Polish | 1422 | 1449 | Qwen3.7 Plus |
| LMArena Text: multi-turn | 1415 | 1459 | Qwen3.7 Plus |
| LMArena Text: longer queries | 1412 | 1454 | Qwen3.7 Plus |
| LMArena Text: writing and language | 1403 | 1437 | Qwen3.7 Plus |
| LMArena Text: entertainment and media | 1404 | 1422 | Qwen3.7 Plus |
| LMArena Vision: English | Not measured | 1280 | Only Qwen3.7 Plus measured |
| LMArena Vision: Chinese | Not measured | 1332 | Only Qwen3.7 Plus measured |
| LMArena Vision: reading text | Not measured | 1288 | Only Qwen3.7 Plus measured |
| LMArena Vision: humor | Not measured | 1261 | Only Qwen3.7 Plus measured |
| LMArena Vision: creative writing | Not measured | 1258 | Only Qwen3.7 Plus measured |
| LMArena Document | Not measured | 1444 | Only Qwen3.7 Plus 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 4.5 | Qwen3.7 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
Qwen3.7 Plus costs less
Repository review
50K input + 3K output tokens
Qwen3.7 Plus costs less
Agent loop
200K input + 10K output tokens
Qwen3.7 Plus 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 4.5 | Qwen3.7 Plus |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | July 25, 2025 | June 3, 2026 |
| Context window | 131K | 1M |
| Input $/Mtok | $0.60 | $0.32 |
| Output $/Mtok | $2.20 | $1.28 |
| Coding index | Not benchmarked | 33.2 |
| Independent measurements behind it | 0 | 2 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 4.5 better than Qwen3.7 Plus for coding?
Only Qwen3.7 Plus has a coding score, 33.2 on the Vibe Coding Index; GLM 4.5 has none, so the two cannot be ranked on coding. Pick GLM 4.5 for work measured by LMArena Text: Japanese, where it leads.
Which is cheaper, GLM 4.5 or Qwen3.7 Plus?
GLM 4.5 costs $0.60 per million input tokens and $2.20 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for neither of them.
Which has the bigger context window, GLM 4.5 or Qwen3.7 Plus?
Qwen3.7 Plus, with 1M tokens against 131K.
How many benchmarks is the GLM 4.5 vs Qwen3.7 Plus comparison based on?
42 benchmarks have a result for both models as of October 21, 2026. GLM 4.5's coding index rests on 0 independent benchmarks and Qwen3.7 Plus's on 2. Where only one model has a result, the page says so instead of naming a winner.