Model comparison
GLM-5.3-Flash vs Qwen-14B
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 31.4 on the Noometry Index.
Last verified . 11 shared benchmarks.
Alibaba (Qwen)Summary
- They share 11 benchmarks with published results for both. GLM-5.3-Flash scores higher in 7 categories and Qwen-14B in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in writing & preference, where GLM-5.3-Flash leads 65.3 to 27.6.
Side by side
| GLM-5.3-Flash | Qwen-14B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.8 | 31.4 |
| Released | 2026-08-20 | 2023-09-24 |
| Weights | Open | Open |
| Context window | 1M | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.15 | — |
| Output $ / M tokens | $0.50 | — |
| Results tracked | 40 | 18 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Qwen-14B: 31.2 (#288)
| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Coding | 1508 | 1071 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use Not comparable
GLM-5.3-Flash: 34.2 (#47), Qwen-14B: —
| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| APEX-Agents | 52.8% | — |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Qwen-14B: 19.6 (#257)
| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Hard Prompts | 1491 | 1027 |
| Epoch Capabilities Index | 151.88 | 113.03 |
| ARC-AGI-2 | 65.8% | — |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| Mystery Game Puzzles | 8% | — |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| BIG-Bench Hard | — | 55% |
| LAMBADA | — | 71.1% |
| PIQA | — | 79.9% |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), Qwen-14B: 31.2 (#227)
| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Math | 1500 | 1068 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| OTIS Mock AIME 2024-2025 | 93.9% | — |
| ProofBench | 21% | — |
| GSM8K | — | 61.3% |
Knowledge Not comparable
GLM-5.3-Flash: 58.4 (#36), Qwen-14B: —
| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| GPQA Diamond | 90.2% | — |
| LMArena Expert | 1513 | — |
| ARC (AI2) Challenge | — | 84.4% |
| BoolQ | — | 86.2% |
| MMLU | — | 66.3% |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), Qwen-14B: —
| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Qwen-14B: 27.5 (#275)
| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Non-English | 1462 | 1041 |
| LMArena Chinese | 1527 | 1077 |
| LMArena French | 1496 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1429 | — |
| LMArena Korean | 1446 | — |
| LMArena Russian | 1469 | — |
| LMArena Spanish | 1471 | — |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Qwen-14B: 52.4 (#289)
| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Instruction Following | 1478 | 1031 |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), Qwen-14B: 31.3 (#280)
| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Longer Query | 1482 | 1028 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Qwen-14B: 27.6 (#299)
| Benchmark | GLM-5.3-Flash | Qwen-14B |
|---|---|---|
| LMArena Text | 1471 | 1051 |
| LMArena Creative Writing | 1442 | 1028 |
| LMArena Multi-Turn | 1467 | 1022 |
Frequently asked questions
Is GLM-5.3-Flash better than Qwen-14B?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 31.4 on the Noometry Index.
Is GLM-5.3-Flash or Qwen-14B better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 31.2 in the Noometry coding category.
How many benchmarks do GLM-5.3-Flash and Qwen-14B share?
11 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen-14B has 18.