Model comparison
GLM-5.3-Flash vs Qwen3-1.7B
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 26.6 on the Noometry Index.
Last verified . 3 shared benchmarks.
Alibaba (Qwen)Summary
- They share 3 benchmarks with published results for both. GLM-5.3-Flash scores higher in 4 categories and Qwen3-1.7B in 0 categories; 4 gaps are clear of the uncertainty.
- The widest gap is in knowledge, where GLM-5.3-Flash leads 58.4 to 19.6.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 8.1% for Qwen3-1.7B.
Side by side
| GLM-5.3-Flash | Qwen3-1.7B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.8 | 26.6 |
| Released | 2026-08-20 | 2025-04-29 |
| Weights | Open | Open |
| Context window | 1M | — |
| Max output | 131K | — |
| Input $ / M tokens | $0.15 | — |
| Output $ / M tokens | $0.50 | — |
| Results tracked | 40 | 4 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding Not comparable
GLM-5.3-Flash: 53.1 (#31), Qwen3-1.7B: —
| Benchmark | GLM-5.3-Flash | Qwen3-1.7B |
|---|---|---|
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| LMArena Coding | 1508 | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), Qwen3-1.7B: 24.7 (#115)
| Benchmark | GLM-5.3-Flash | Qwen3-1.7B |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 28.4% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Qwen3-1.7B: 19.2 (#267)
| Benchmark | GLM-5.3-Flash | Qwen3-1.7B |
|---|---|---|
| Chess Puzzles | 14% | 0% |
| ARC-AGI-2 | 65.8% | — |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| LMArena Hard Prompts | 1491 | — |
| Mystery Game Puzzles | 8% | — |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| Epoch Capabilities Index | 151.88 | — |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), Qwen3-1.7B: 16.3 (#294)
| Benchmark | GLM-5.3-Flash | Qwen3-1.7B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 8.1% |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| LMArena Math | 1500 | — |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Qwen3-1.7B: 19.6 (#278)
| Benchmark | GLM-5.3-Flash | Qwen3-1.7B |
|---|---|---|
| GPQA Diamond | 90.2% | 38% |
| LMArena Expert | 1513 | — |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), Qwen3-1.7B: —
| Benchmark | GLM-5.3-Flash | Qwen3-1.7B |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual Not comparable
GLM-5.3-Flash: 56.0 (#25), Qwen3-1.7B: —
| Benchmark | GLM-5.3-Flash | Qwen3-1.7B |
|---|---|---|
| LMArena Non-English | 1462 | — |
| LMArena Chinese | 1527 | — |
| LMArena French | 1496 | — |
| LMArena German | 1470 | — |
| LMArena Japanese | 1429 | — |
| LMArena Korean | 1446 | — |
| LMArena Russian | 1469 | — |
| LMArena Spanish | 1471 | — |
Instruction Following Not comparable
GLM-5.3-Flash: 77.5 (#20), Qwen3-1.7B: —
| Benchmark | GLM-5.3-Flash | Qwen3-1.7B |
|---|---|---|
| LMArena Instruction Following | 1478 | — |
Long Context Not comparable
GLM-5.3-Flash: 45.4 (#39), Qwen3-1.7B: —
| Benchmark | GLM-5.3-Flash | Qwen3-1.7B |
|---|---|---|
| LMArena Longer Query | 1482 | — |
Writing & Preference Not comparable
GLM-5.3-Flash: 65.3 (#50), Qwen3-1.7B: —
| Benchmark | GLM-5.3-Flash | Qwen3-1.7B |
|---|---|---|
| LMArena Text | 1471 | — |
| LMArena Creative Writing | 1442 | — |
| LMArena Multi-Turn | 1467 | — |
Frequently asked questions
Is GLM-5.3-Flash better than Qwen3-1.7B?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 26.6 on the Noometry Index.
How many benchmarks do GLM-5.3-Flash and Qwen3-1.7B share?
3 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen3-1.7B has 4.