Model comparison
GLM-5.3-Flash vs Qwen3 Max
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 43.7 on the Noometry Index.
Last verified . 24 shared benchmarks.
Alibaba (Qwen)Summary
- They share 24 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and Qwen3 Max in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 22.6.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 55.8% for GLM-5.3-Flash and 18.9% for Qwen3 Max.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $1.20 / $6 for Qwen3 Max.
- GLM-5.3-Flash accepts more context: 1M tokens versus 262K.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | Qwen3 Max | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.8 | 43.7 |
| Released | 2026-08-20 | 2025-09-23 |
| Weights | Open | Proprietary |
| Context window | 1M | 262K |
| Max output | 131K | 66K |
| Input $ / M tokens | $0.15 | $1.20 |
| Output $ / M tokens | $0.50 | $6 |
| 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 Max: 43.0 (#93)
| Benchmark | GLM-5.3-Flash | Qwen3 Max |
|---|---|---|
| LMArena Coding | 1508 | 1456 |
| ALE-Bench | 303.55 | 370.45 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.2 (#47), Qwen3 Max: —
| Benchmark | GLM-5.3-Flash | Qwen3 Max |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
| Vending-Bench 2 | — | 71.56 |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Qwen3 Max: 22.6 (#190)
| Benchmark | GLM-5.3-Flash | Qwen3 Max |
|---|---|---|
| Chess Puzzles | 14% | 4% |
| LMArena Hard Prompts | 1491 | 1448 |
| Mystery Game Puzzles | 8% | 5% |
| Epoch Capabilities Index | 151.88 | 142.38 |
| ARC-AGI-2 | 65.8% | — |
| Kagi LLM Benchmark | — | 72.5% |
| NYT Connections (extended) | — | 30.1% |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| DTBench | — | 82.1% |
| LMCA | — | 28.3% |
| 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 Max: 38.7 (#131)
| Benchmark | GLM-5.3-Flash | Qwen3 Max |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 18.9% |
| OTIS Mock AIME 2024-2025 | 93.9% | 73.3% |
| LMArena Math | 1500 | 1446 |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| MATH Level 5 | — | 97.1% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Qwen3 Max: 48.1 (#78)
| Benchmark | GLM-5.3-Flash | Qwen3 Max |
|---|---|---|
| GPQA Diamond | 90.2% | 72.6% |
| LMArena Expert | 1513 | 1455 |
| SimpleQA Verified | — | 48.7% |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), Qwen3 Max: —
| Benchmark | GLM-5.3-Flash | Qwen3 Max |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Qwen3 Max: 53.7 (#62)
| Benchmark | GLM-5.3-Flash | Qwen3 Max |
|---|---|---|
| LMArena Non-English | 1462 | 1429 |
| LMArena Chinese | 1527 | 1478 |
| LMArena French | 1496 | 1449 |
| LMArena German | 1470 | 1463 |
| LMArena Japanese | 1429 | 1397 |
| LMArena Korean | 1446 | 1399 |
| LMArena Russian | 1469 | 1428 |
| LMArena Spanish | 1471 | 1462 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Qwen3 Max: 74.8 (#87)
| Benchmark | GLM-5.3-Flash | Qwen3 Max |
|---|---|---|
| LMArena Instruction Following | 1478 | 1419 |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), Qwen3 Max: 41.6 (#134)
| Benchmark | GLM-5.3-Flash | Qwen3 Max |
|---|---|---|
| LMArena Longer Query | 1482 | 1438 |
| Fiction.LiveBench | — | 66.7% |
| CL-bench | — | 14.5% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Qwen3 Max: 62.4 (#76)
| Benchmark | GLM-5.3-Flash | Qwen3 Max |
|---|---|---|
| LMArena Text | 1471 | 1439 |
| LMArena Creative Writing | 1442 | 1402 |
| LMArena Multi-Turn | 1467 | 1446 |
Frequently asked questions
Is GLM-5.3-Flash better than Qwen3 Max?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 43.7 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or Qwen3 Max?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3 Max lists at $1.20 and $6.
Is GLM-5.3-Flash or Qwen3 Max better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 43.0 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 262K.
How many benchmarks do GLM-5.3-Flash and Qwen3 Max share?
24 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen3 Max has 33.