GLM 5.3 vs Qwen3.7 Plus
GLM 5.3 leads Qwen3.7 Plus on coding, 70.6 to 33.2 on the Vibe Coding Index, and Qwen3.7 Plus costs 3.4x less per output token ($1.28 vs $4.40 per million). Updated October 21, 2026; 59 benchmarks have a result for both.
GLM 5.3 or Qwen3.7 Plus: which to pick
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus.
Pick Qwen3.7 Plus for output tokens 3.4x cheaper ($1.28 vs $4.40 per million) and work measured by SkillsBench (Vals AI) or OTIS Mock AIME, where it leads.
- Shared results
- 59
- GLM 5.3 only
- 41
- Qwen3.7 Plus only
- 16
- 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
GLM 5.3
GLM 5.3 scores 70.6 against 33.2.
6 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
GLM 5.3
GLM 5.3 leads, 44.8 against 25.2.
Same board, both measured
Agentic work
AA Agentic Index
GLM 5.3
GLM 5.3 leads, 53.1 against 17.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GLM 5.3 has a GPQA Diamond result, 88.1%.
Directional only
Knowledge
MMLU Pro
No call
Only GLM 5.3 has a MMLU Pro result, 86.8%.
Directional only
Web design
Design Arena: websites
GLM 5.3
GLM 5.3 leads, 1295 against 1269.
Same board, both measured
General
Vals Index
No call
Only GLM 5.3 has a Vals Index result, 59.7%.
Directional only
Long documents
Listed context window
GLM 5.3
GLM 5.3 takes 1.05M tokens against 1M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen3.7 Plus
GLM 5.3 $0.0036, Qwen3.7 Plus $0.0010 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GLM 5.3 has the number, $0.36.
Directional only
At a glance
- Coding index
- GLM 5.3 70.6 from 6 benchmarks, Qwen3.7 Plus 33.2 from 2 benchmarks.
- Widest category
- Agentic work, 24.4 points apart on average over 5 shared benchmarks.
- Widest single gap
- FrontierCode: GLM 5.3 40.1% against Qwen3.7 Plus 10.2%, 46.1 points apart on a 0 to 100 scale.
- Output price
- GLM 5.3 $4.40 against Qwen3.7 Plus $1.28 per million output tokens, 3.4x apart.
- Context window
- GLM 5.3 takes 1.05M 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 5.3 | Bars | Qwen3.7 Plus | Ties |
|---|---|---|---|---|
| Coding | 8 | 1 | ||
| Intelligence | 2 | 0 | ||
| Agentic work | 5 | 0 | ||
| Reasoning | 7 | 0 | ||
| Math | 3 | 1 | ||
| Knowledge | 4 | 0 | ||
| Web design | 7 | 1 | ||
| General | 20 | 0 | ||
| All shared boards | 56 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index81
| Benchmark | GLM 5.3 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| LiveBench coding | 82.8% | Not measured | Only GLM 5.3 measured |
| LMArena WebDev | 1621 | Not measured | Only GLM 5.3 measured |
| AA Coding Index | 74.8 | 55.9 | GLM 5.3 |
| LiveCodeBench (Vals AI) | 80.5% | Not measured | Only GLM 5.3 measured |
| SWE-bench Verified (Vals AI) | 95.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 4 (Vals AI) | 38.9% | Not measured | Only GLM 5.3 measured |
| Vibe Code Bench (Vals AI) | 78.1% | 46.4% | GLM 5.3 |
| Code migration (Vals AI) | 44.2% | 12.9% | GLM 5.3 |
| ProgramBench (Vals AI) | 1.5% | Not measured | Only GLM 5.3 measured |
| IOI (Vals AI) | 68.4% | Not measured | Only GLM 5.3 measured |
| Terminal-Bench 2.1 (Vals AI) | 71.5% | 52.8% | GLM 5.3 |
| Vibe Code Bench 1-100 (Vals AI) | 20.0% | Not measured | Only GLM 5.3 measured |
| SkillsBench (Vals AI) | 47.5% | 54.3% | Qwen3.7 Plus |
| CyberBench (Vals AI) | 72.1% | Not measured | Only GLM 5.3 measured |
| SciCode | 59.0% | 46.1% | GLM 5.3 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 47.0% | Only Qwen3.7 Plus measured |
| FrontierCode | 40.1% | 10.2% | GLM 5.3 |
| FrontierSWE | 30.2% | Not measured | Only GLM 5.3 measured |
| WeirdML | 75.4% | Not measured | Only GLM 5.3 measured |
| ALE-Bench | 1317.4 | Not measured | Only GLM 5.3 measured |
| LMArena Text: coding | 1497 | 1473 | GLM 5.3 |
| LMArena Text: software and IT | 1493 | 1473 | GLM 5.3 |
| LMArena WebDev: React | 1641 | Not measured | Only GLM 5.3 measured |
| LMArena WebDev: HTML | 1595 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 15 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GLM 5.3 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Intelligence Index | 44.8 | 25.2 | GLM 5.3 |
| Epoch Capabilities Index | 156.0 | 147.0 | GLM 5.3 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | GLM 5.3 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| AA Agentic Index | 53.1 | 17.5 | GLM 5.3 |
| Design Arena: agents building mobile apps | 1194 | Not measured | Only GLM 5.3 measured |
| Design Arena: HTML slides | 1189 | Not measured | Only GLM 5.3 measured |
| Design Arena: PowerPoint slides | 1254 | Not measured | Only GLM 5.3 measured |
| LiveBench research | 73.2% | Not measured | Only GLM 5.3 measured |
| LiveBench browser | 0.0% | Not measured | Only GLM 5.3 measured |
| LiveBench long agentic | 35.9% | Not measured | Only GLM 5.3 measured |
| SREBench (Vals AI) | 9.2% | Not measured | Only GLM 5.3 measured |
| Finance Agent (Vals AI) | 55.8% | 38.2% | GLM 5.3 |
| Tax Agent Bench (Vals AI) | 39.7% | 10.5% | GLM 5.3 |
| Legal Research Bench (Vals AI) | 49.0% | 16.4% | GLM 5.3 |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.6% | Not measured | Only GLM 5.3 measured |
| Excel Modeling Benchmark (Vals AI) | 56.3% | 49.3% | GLM 5.3 |
| Terminal-Bench-Science (Vals AI) | 5.7% | Not measured | Only GLM 5.3 measured |
| τ²-Bench Telecom | Not measured | 93.0% | Only Qwen3.7 Plus measured |
| APEX-Agents | 56.6% | Not measured | Only GLM 5.3 measured |
| OSWorld 2.0 | Not measured | 2.8% | Only Qwen3.7 Plus measured |
| Vending-Bench 2 | $8,164 | Not measured | Only GLM 5.3 measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index70
| Benchmark | GLM 5.3 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| GPQA Diamond | 88.1% | Not measured | Only GLM 5.3 measured |
| LiveBench reasoning | 85.8% | Not measured | Only GLM 5.3 measured |
| MysteryMechanism (Vals AI) | 23.0% | Not measured | Only GLM 5.3 measured |
| GPQA Diamond (Artificial Analysis run) | 91.7% | 90.0% | GLM 5.3 |
| Humanity's Last Exam (Artificial Analysis run) | 42.3% | 35.6% | GLM 5.3 |
| GPQA Diamond (Epoch AI run) | 90.9% | 87.9% | GLM 5.3 |
| Chess puzzles | 37.0% | Not measured | Only GLM 5.3 measured |
| Mystery game puzzles | 33.0% | 17.0% | GLM 5.3 |
| LMArena Text: hard prompts | 1490 | 1460 | GLM 5.3 |
| LMArena Text: expert | 1514 | 1466 | GLM 5.3 |
| LMArena Text: hard prompts in English | 1496 | 1460 | GLM 5.3 |
| 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 9 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index31
| Benchmark | GLM 5.3 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| OTIS Mock AIME | 91.1% | 93.3% | Qwen3.7 Plus |
| FrontierMath, tiers 1 to 3 | 68.8% | 34.4% | GLM 5.3 |
| FrontierMath, tier 4 | 29.3% | Not measured | Only GLM 5.3 measured |
| ProofBench | 49.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: math | 1495 | 1467 | GLM 5.3 |
| LMArena Text: mathematical work | 1497 | 1470 | GLM 5.3 |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | GLM 5.3 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| MMLU Pro | 86.8% | Not measured | Only GLM 5.3 measured |
| LegalBench (Vals AI) | 84.8% | Not measured | Only GLM 5.3 measured |
| TaxEval (Vals AI) | 72.4% | Not measured | Only GLM 5.3 measured |
| MortgageTax (Vals AI) | Not measured | 66.2% | Only Qwen3.7 Plus measured |
| MedCode (Vals AI) | 42.9% | Not measured | Only GLM 5.3 measured |
| SimpleQA Verified | 41.0% | Not measured | Only GLM 5.3 measured |
| LMArena Text: science | 1498 | 1464 | GLM 5.3 |
| LMArena Text: medicine and healthcare | 1481 | 1464 | GLM 5.3 |
| LMArena Text: legal and government | 1473 | 1451 | GLM 5.3 |
| LMArena Text: business and finance | 1459 | 1435 | GLM 5.3 |
| Neither model has a result on the other 8 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index71
| Benchmark | GLM 5.3 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| Design Arena: websites | 1295 | 1269 | GLM 5.3 |
| Design Arena: UI components | 1314 | 1261 | GLM 5.3 |
| Design Arena: game dev | 1311 | 1262 | GLM 5.3 |
| Design Arena: data viz | 1259 | 1276 | Qwen3.7 Plus |
| Design Arena: 3D | 1332 | 1239 | GLM 5.3 |
| Design Arena: all code categories | 1304 | 1269 | GLM 5.3 |
| Design Arena: SVG | 1293 | 1208 | GLM 5.3 |
| Design Arena: ASCII art | 1213 | 1143 | GLM 5.3 |
| LiveBench design | 77.3% | Not measured | Only GLM 5.3 measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index200
| Benchmark | GLM 5.3 | Qwen3.7 Plus | Leader |
|---|---|---|---|
| Vals Index | 59.7% | Not measured | Only GLM 5.3 measured |
| LiveBench data analysis | 70.2% | Not measured | Only GLM 5.3 measured |
| Public Benefits Bench (Vals AI) | 68.5% | Not measured | Only GLM 5.3 measured |
| SAGE (Vals AI) | Not measured | 39.3% | Only Qwen3.7 Plus measured |
| MedScribe (Vals AI) | 88.8% | Not measured | Only GLM 5.3 measured |
| Vals Multimodal Index | Not measured | 53.9% | Only Qwen3.7 Plus measured |
| IFBench | Not measured | 78.0% | Only Qwen3.7 Plus measured |
| Long context reasoning (AA-LCR) | 79.7% | 73.0% | GLM 5.3 |
| DTBench | 87.7% | 84.0% | GLM 5.3 |
| LMCA | 55.5% | 37.6% | GLM 5.3 |
| LMArena Text | 1474 | 1454 | GLM 5.3 |
| LMArena Text: instruction following | 1479 | 1439 | GLM 5.3 |
| LMArena Text: creative writing | 1458 | 1438 | GLM 5.3 |
| LMArena Vision | Not measured | 1278 | Only Qwen3.7 Plus measured |
| LMArena Text: English | 1482 | 1458 | GLM 5.3 |
| LMArena Text: non-English | 1459 | 1445 | GLM 5.3 |
| LMArena Text: Chinese | 1532 | 1509 | GLM 5.3 |
| LMArena Text: German | 1488 | 1471 | GLM 5.3 |
| LMArena Text: Japanese | 1460 | 1412 | GLM 5.3 |
| LMArena Text: Korean | 1460 | 1413 | GLM 5.3 |
| LMArena Text: French | 1503 | 1472 | GLM 5.3 |
| LMArena Text: Spanish | 1462 | 1457 | GLM 5.3 |
| LMArena Text: Russian | 1469 | 1457 | GLM 5.3 |
| LMArena Text: Polish | 1466 | 1449 | GLM 5.3 |
| LMArena Text: multi-turn | 1472 | 1459 | GLM 5.3 |
| LMArena Text: longer queries | 1483 | 1454 | GLM 5.3 |
| LMArena Text: writing and language | 1469 | 1437 | GLM 5.3 |
| LMArena Text: entertainment and media | 1447 | 1422 | GLM 5.3 |
| 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 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GLM 5.3 | 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 5.3 | Qwen3.7 Plus |
|---|---|---|
| Maker | Z.ai | Qwen |
| Weights | Open weights | No public weights listed |
| Released | August 18, 2026 | June 3, 2026 |
| Context window | 1.05M | 1M |
| Input $/Mtok | $1.40 | $0.32 |
| Output $/Mtok | $4.40 | $1.28 |
| Coding index | 70.6 | 33.2 |
| Independent measurements behind it | 6 | 2 |
| Cost per solved task | $0.36 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GLM 5.3 better than Qwen3.7 Plus for coding?
Pick GLM 5.3 for coding: it scores 70.6 on the Vibe Coding Index against 33.2 for Qwen3.7 Plus. Pick Qwen3.7 Plus for output tokens 3.4x cheaper ($1.28 vs $4.40 per million) and work measured by SkillsBench (Vals AI) or OTIS Mock AIME, where it leads.
Which is cheaper, GLM 5.3 or Qwen3.7 Plus?
GLM 5.3 costs $1.40 per million input tokens and $4.40 per million output tokens; Qwen3.7 Plus costs $0.32 and $1.28. Cost per solved task is known for GLM 5.3 only.
Which has the bigger context window, GLM 5.3 or Qwen3.7 Plus?
GLM 5.3, with 1.05M tokens against 1M.
How many benchmarks is the GLM 5.3 vs Qwen3.7 Plus comparison based on?
59 benchmarks have a result for both models as of October 21, 2026. GLM 5.3's coding index rests on 6 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.