Model comparison
GLM-5.3-Flash vs Qwen3.7 Max
GLM-5.3-Flash and Qwen3.7 Max score almost the same on the Noometry Index (51.8 vs 51.5), so choose on price, context window or the category you care about most.
Last verified . 24 shared benchmarks.
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. GLM-5.3-Flash scores higher in 4 categories and Qwen3.7 Max in 5 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where GLM-5.3-Flash leads 34.2 to 22.1.
- The biggest single-benchmark swing is Mystery Game Puzzles: 8% for GLM-5.3-Flash and 32% for Qwen3.7 Max.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $2.50 / $7.50 for Qwen3.7 Max.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | Qwen3.7 Max | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.8 | 51.5 |
| Released | 2026-08-20 | 2026-05-19 |
| Weights | Open | Proprietary |
| Context window | 1M | 1M |
| Max output | 131K | 131K |
| Input $ / M tokens | $0.15 | $2.50 |
| Output $ / M tokens | $0.50 | $7.50 |
| Results tracked | 40 | 33 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Qwen3.7 Max: 50.4 (#45)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Max |
|---|---|---|
| LMArena WebDev | 1609 | 1515 |
| SciCode | 51.6% | 48.8% |
| LMArena Coding | 1508 | 1498 |
| ALE-Bench | 303.55 | 1,189 |
| SWE-bench Verified | — | 77.3% |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| FrontierSWE | 18.1% | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), Qwen3.7 Max: 22.1 (#135)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Max |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GBAEval | — | 0.4% |
| GDP.pdf | 14% | — |
Reasoning Qwen3.7 Max leads
GLM-5.3-Flash: 48.0 (#42), Qwen3.7 Max: 49.2 (#38)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Max |
|---|---|---|
| CritPt | 15.4% | 13.4% |
| Chess Puzzles | 14% | 19% |
| LMArena Hard Prompts | 1491 | 1483 |
| Mystery Game Puzzles | 8% | 32% |
| Epoch Capabilities Index | 151.88 | 153.68 |
| ARC-AGI-2 | 65.8% | — |
| SimpleBench | — | 70.4% |
| NYT Connections (extended) | — | 85.1% |
| ARC-AGI-1 | 91% | — |
| EBR-Bench | — | 9.5% |
| DTBench | — | 92.3% |
| LMCA | — | 44% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
Math Qwen3.7 Max leads
GLM-5.3-Flash: 53.3 (#47), Qwen3.7 Max: 62.4 (#32)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 64.6% |
| FrontierMath Tier 4 | 17.1% | 34.1% |
| OTIS Mock AIME 2024-2025 | 93.9% | 95.6% |
| ProofBench | 21% | 26% |
| LMArena Math | 1500 | 1490 |
Knowledge Qwen3.7 Max leads
GLM-5.3-Flash: 58.4 (#36), Qwen3.7 Max: 61.6 (#28)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Max |
|---|---|---|
| GPQA Diamond | 90.2% | 90.9% |
| LMArena Expert | 1513 | 1488 |
| SimpleQA Verified | — | 55.8% |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), Qwen3.7 Max: —
| Benchmark | GLM-5.3-Flash | Qwen3.7 Max |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual Too close to call
GLM-5.3-Flash: 56.0 (#25), Qwen3.7 Max: 56.9 (#15)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Max |
|---|---|---|
| LMArena Non-English | 1462 | 1474 |
| LMArena Chinese | 1527 | 1530 |
| LMArena Russian | 1469 | 1484 |
| LMArena French | 1496 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1429 | — |
| LMArena Korean | 1446 | — |
| LMArena Spanish | 1471 | — |
Instruction Following Too close to call
GLM-5.3-Flash: 77.5 (#20), Qwen3.7 Max: 76.7 (#38)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Max |
|---|---|---|
| LMArena Instruction Following | 1478 | 1460 |
Long Context Too close to call
GLM-5.3-Flash: 45.4 (#39), Qwen3.7 Max: 45.4 (#40)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Max |
|---|---|---|
| LMArena Longer Query | 1482 | 1482 |
Writing & Preference Too close to call
GLM-5.3-Flash: 65.3 (#50), Qwen3.7 Max: 65.0 (#54)
| Benchmark | GLM-5.3-Flash | Qwen3.7 Max |
|---|---|---|
| LMArena Text | 1471 | 1476 |
| LMArena Creative Writing | 1442 | 1449 |
| LMArena Multi-Turn | 1467 | 1481 |
| EQ-Bench 4 | — | 1110 |
Frequently asked questions
Is GLM-5.3-Flash better than Qwen3.7 Max?
GLM-5.3-Flash and Qwen3.7 Max score almost the same on the Noometry Index (51.8 vs 51.5), so choose on price, context window or the category you care about most.
Which is cheaper, GLM-5.3-Flash or Qwen3.7 Max?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.7 Max lists at $2.50 and $7.50.
Is GLM-5.3-Flash or Qwen3.7 Max better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 50.4 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 Max share?
24 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen3.7 Max has 33.