Model comparison
GLM-5.3-Flash vs Qwen3.6 Plus
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 47.5 on the Noometry Index.
Last verified . 27 shared benchmarks.
Summary
- They share 27 benchmarks with published results for both. GLM-5.3-Flash scores higher in 8 categories and Qwen3.6 Plus in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 29.3.
- The biggest single-benchmark swing is FrontierMath (Tiers 1-3): 55.8% for GLM-5.3-Flash and 38.2% for Qwen3.6 Plus.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.50 / $3 for Qwen3.6 Plus.
- GLM-5.3-Flash has downloadable open weights; the other is API-only.
Side by side
| GLM-5.3-Flash | Qwen3.6 Plus | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.8 | 47.5 |
| Released | 2026-08-20 | 2026-03-31 |
| Weights | Open | Proprietary |
| Context window | 1M | 1M |
| Max output | 131K | 66K |
| Input $ / M tokens | $0.15 | $0.50 |
| Output $ / M tokens | $0.50 | $3 |
| Results tracked | 40 | 37 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Qwen3.6 Plus: 40.8 (#130)
| Benchmark | GLM-5.3-Flash | Qwen3.6 Plus |
|---|---|---|
| LMArena WebDev | 1609 | 1461 |
| SciCode | 51.6% | 40.7% |
| LMArena Coding | 1508 | 1467 |
| ALE-Bench | 303.55 | 670.15 |
| SWE-bench Verified | — | 57.9% |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| FrontierSWE | 18.1% | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.2 (#47), Qwen3.6 Plus: —
| Benchmark | GLM-5.3-Flash | Qwen3.6 Plus |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
| Vending-Bench 2 | — | 5,115 |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Qwen3.6 Plus: 29.3 (#93)
| Benchmark | GLM-5.3-Flash | Qwen3.6 Plus |
|---|---|---|
| CritPt | 15.4% | 2.9% |
| Chess Puzzles | 14% | 17% |
| LMArena Hard Prompts | 1491 | 1449 |
| Mystery Game Puzzles | 8% | 12% |
| Epoch Capabilities Index | 151.88 | 147.65 |
| ARC-AGI-2 | 65.8% | — |
| NYT Connections (extended) | — | 60.3% |
| ARC-AGI-1 | 91% | — |
| Thematic Generalization | — | 59.5% |
| DTBench | — | 81.9% |
| LMCA | — | 33.1% |
| 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), Qwen3.6 Plus: 51.8 (#54)
| Benchmark | GLM-5.3-Flash | Qwen3.6 Plus |
|---|---|---|
| FrontierMath (Tiers 1-3) | 55.8% | 38.2% |
| OTIS Mock AIME 2024-2025 | 93.9% | 93.3% |
| LMArena Math | 1500 | 1450 |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| FrontierMath (Feb 2025 set) | — | 26.2% |
| FrontierMath Tier 4 (v1) | — | 8.3% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Qwen3.6 Plus: 56.1 (#45)
| Benchmark | GLM-5.3-Flash | Qwen3.6 Plus |
|---|---|---|
| GPQA Diamond | 90.2% | 88.4% |
| LMArena Expert | 1513 | 1454 |
| SimpleQA Verified | — | 44.1% |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), Qwen3.6 Plus: —
| Benchmark | GLM-5.3-Flash | Qwen3.6 Plus |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Qwen3.6 Plus: 53.3 (#70)
| Benchmark | GLM-5.3-Flash | Qwen3.6 Plus |
|---|---|---|
| LMArena Non-English | 1462 | 1424 |
| LMArena Chinese | 1527 | 1477 |
| LMArena French | 1496 | 1455 |
| LMArena German | 1470 | 1452 |
| LMArena Japanese | 1429 | 1389 |
| LMArena Korean | 1446 | 1379 |
| LMArena Russian | 1469 | 1434 |
| LMArena Spanish | 1471 | 1432 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Qwen3.6 Plus: 75.0 (#74)
| Benchmark | GLM-5.3-Flash | Qwen3.6 Plus |
|---|---|---|
| LMArena Instruction Following | 1478 | 1425 |
Long Context Too close to call
GLM-5.3-Flash: 45.4 (#39), Qwen3.6 Plus: 45.2 (#49)
| Benchmark | GLM-5.3-Flash | Qwen3.6 Plus |
|---|---|---|
| LMArena Longer Query | 1482 | 1439 |
| CL-bench | — | 20.3% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Qwen3.6 Plus: 62.2 (#82)
| Benchmark | GLM-5.3-Flash | Qwen3.6 Plus |
|---|---|---|
| LMArena Text | 1471 | 1437 |
| LMArena Creative Writing | 1442 | 1404 |
| LMArena Multi-Turn | 1467 | 1438 |
Frequently asked questions
Is GLM-5.3-Flash better than Qwen3.6 Plus?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 47.5 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or Qwen3.6 Plus?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3.6 Plus lists at $0.50 and $3.
Is GLM-5.3-Flash or Qwen3.6 Plus better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 40.8 in the Noometry coding category.
Which has the bigger context window?
Both accept 1M tokens.
How many benchmarks do GLM-5.3-Flash and Qwen3.6 Plus share?
27 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen3.6 Plus has 37.