Model comparison
GLM-5.3-Flash vs Qwen3 32B
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 39.2 on the Noometry Index.
Last verified . 19 shared benchmarks.
Alibaba (Qwen)Summary
- They share 19 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and Qwen3 32B in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-5.3-Flash leads 48.0 to 20.2.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 66.9% for Qwen3 32B.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $0.70 / $2.80 for Qwen3 32B.
- GLM-5.3-Flash accepts more context: 1M tokens versus 131K.
Side by side
| GLM-5.3-Flash | Qwen3 32B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.8 | 39.2 |
| Released | 2026-08-20 | 2025-04 |
| Weights | Open | Open |
| Context window | 1M | 131K |
| Max output | 131K | 16K |
| Input $ / M tokens | $0.15 | $0.70 |
| Output $ / M tokens | $0.50 | $2.80 |
| Results tracked | 40 | 26 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Qwen3 32B: 37.7 (#190)
| Benchmark | GLM-5.3-Flash | Qwen3 32B |
|---|---|---|
| SciCode | 51.6% | 35.4% |
| LMArena Coding | 1508 | 1358 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| Aider Polyglot | — | 40% |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), Qwen3 32B: 32.6 (#62)
| Benchmark | GLM-5.3-Flash | Qwen3 32B |
|---|---|---|
| APEX-Agents | 52.8% | — |
| Berkeley Function Calling Leaderboard | — | 48.7% |
| GDP.pdf | 14% | — |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Qwen3 32B: 20.2 (#241)
| Benchmark | GLM-5.3-Flash | Qwen3 32B |
|---|---|---|
| CritPt | 15.4% | 0.3% |
| Chess Puzzles | 14% | 5% |
| LMArena Hard Prompts | 1491 | 1334 |
| Epoch Capabilities Index | 151.88 | 138.51 |
| ARC-AGI-2 | 65.8% | — |
| Kagi LLM Benchmark | — | 54.9% |
| ARC-AGI-1 | 91% | — |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 67.5% |
| LMCA | — | 17.3% |
| 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 32B: 39.7 (#99)
| Benchmark | GLM-5.3-Flash | Qwen3 32B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 66.9% |
| LMArena Math | 1500 | 1399 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Qwen3 32B: 40.0 (#125)
| Benchmark | GLM-5.3-Flash | Qwen3 32B |
|---|---|---|
| GPQA Diamond | 90.2% | 65.7% |
| LMArena Expert | 1513 | 1362 |
| Vectara Hallucination Rate | — | 5.9% |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), Qwen3 32B: —
| Benchmark | GLM-5.3-Flash | Qwen3 32B |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Qwen3 32B: 45.6 (#167)
| Benchmark | GLM-5.3-Flash | Qwen3 32B |
|---|---|---|
| LMArena Non-English | 1462 | 1317 |
| LMArena Chinese | 1527 | 1357 |
| LMArena German | 1470 | 1341 |
| LMArena Russian | 1469 | 1311 |
| LMArena French | 1496 | — |
| LMArena Japanese | 1429 | — |
| LMArena Korean | 1446 | — |
| LMArena Spanish | 1471 | — |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Qwen3 32B: 68.9 (#179)
| Benchmark | GLM-5.3-Flash | Qwen3 32B |
|---|---|---|
| LMArena Instruction Following | 1478 | 1305 |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), Qwen3 32B: 43.8 (#87)
| Benchmark | GLM-5.3-Flash | Qwen3 32B |
|---|---|---|
| LMArena Longer Query | 1482 | 1327 |
| Fiction.LiveBench | — | 74.2% |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Qwen3 32B: 52.9 (#163)
| Benchmark | GLM-5.3-Flash | Qwen3 32B |
|---|---|---|
| LMArena Text | 1471 | 1340 |
| LMArena Creative Writing | 1442 | 1297 |
| LMArena Multi-Turn | 1467 | 1331 |
Frequently asked questions
Is GLM-5.3-Flash better than Qwen3 32B?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 39.2 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or Qwen3 32B?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen3 32B lists at $0.70 and $2.80.
Is GLM-5.3-Flash or Qwen3 32B better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 37.7 in the Noometry coding category.
Which has the bigger context window?
GLM-5.3-Flash does, with 1M tokens against 131K.
How many benchmarks do GLM-5.3-Flash and Qwen3 32B share?
19 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen3 32B has 26.