Model comparison
GLM-5.3-Flash vs Qwen3.7 Plus
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 45.3 on the Noometry Index.
Last verified . 27 shared benchmarks.
Alibaba (Qwen)Summary
- They share 27 benchmarks with published results for both. GLM-5.3-Flash scores higher in 10 categories and Qwen3.7 Plus in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-5.3-Flash leads 53.1 to 36.6.
- The biggest single-benchmark swing is FrontierCode: 31.8% for GLM-5.3-Flash and 10.2% for Qwen3.7 Plus.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.40 / $1.60 for Qwen3.7 Plus.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | Qwen3.7 Plus | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.8 | 45.3 |
| Released | 2026-08-20 | 2026-06-02 |
| Weights | Open | Proprietary |
| Context window | 1M | 1M |
| Max output | 131K | 131K |
| Input $ / M tokens | $0.15 | $0.40 |
| Output $ / M tokens | $0.50 | $1.60 |
| Results tracked | 40 | 32 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Qwen3.7 Plus: 36.6 (#206)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Plus |
|---|---|---|
| FrontierCode | 31.8% | 10.2% |
| SciCode | 51.6% | 45.5% |
| LMArena Coding | 1508 | 1473 |
| DeepSWE | 63.4% | — |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), Qwen3.7 Plus: 21.4 (#138)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Plus |
|---|---|---|
| APEX-Agents | 52.8% | — |
| OSWorld 2.0 | — | 2.8% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Qwen3.7 Plus: 39.3 (#59)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Plus |
|---|---|---|
| CritPt | 15.4% | 9.1% |
| Chess Puzzles | 14% | 24% |
| LMArena Hard Prompts | 1491 | 1460 |
| Mystery Game Puzzles | 8% | 17% |
| Epoch Capabilities Index | 151.88 | 147.37 |
| ARC-AGI-2 | 65.8% | — |
| NYT Connections (extended) | — | 74.8% |
| ARC-AGI-1 | 91% | — |
| DTBench | — | 84% |
| LMCA | — | 37.6% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), Qwen3.7 Plus: 50.5 (#56)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Plus |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 34.4% |
| OTIS Mock AIME 2024-2025 | 93.9% | 93.3% |
| LMArena Math | 1500 | 1466 |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Qwen3.7 Plus: 54.9 (#51)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Plus |
|---|---|---|
| GPQA Diamond | 90.2% | 87.9% |
| LMArena Expert | 1513 | 1467 |
Multimodal GLM-5.3-Flash leads
GLM-5.3-Flash: 42.8 (#27), Qwen3.7 Plus: 41.8 (#33)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Plus |
|---|---|---|
| LMArena Vision | 1296 | 1279 |
| LMArena Document | — | 1444 |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Qwen3.7 Plus: 54.8 (#38)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Plus |
|---|---|---|
| LMArena Non-English | 1462 | 1445 |
| LMArena Chinese | 1527 | 1510 |
| LMArena French | 1496 | 1473 |
| LMArena German | 1470 | 1471 |
| LMArena Japanese | 1429 | 1413 |
| LMArena Korean | 1446 | 1415 |
| LMArena Russian | 1469 | 1457 |
| LMArena Spanish | 1471 | 1457 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Qwen3.7 Plus: 75.8 (#52)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Plus |
|---|---|---|
| LMArena Instruction Following | 1478 | 1440 |
Long Context Too close to call
GLM-5.3-Flash: 45.4 (#39), Qwen3.7 Plus: 44.5 (#65)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Plus |
|---|---|---|
| LMArena Longer Query | 1482 | 1455 |
Writing & Preference Too close to call
GLM-5.3-Flash: 65.3 (#50), Qwen3.7 Plus: 64.3 (#56)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Plus |
|---|---|---|
| LMArena Text | 1471 | 1455 |
| LMArena Creative Writing | 1442 | 1439 |
| LMArena Multi-Turn | 1467 | 1460 |
Frequently asked questions
Is GLM-5.3-Flash better than Qwen3.7 Plus?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 45.3 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or Qwen3.7 Plus?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.7 Plus lists at $0.40 and $1.60.
Is GLM-5.3-Flash or Qwen3.7 Plus better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 36.6 in the Noometry coding category.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do GLM-5.3-Flash and Qwen3.7 Plus share?
27 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen3.7 Plus has 32.