Model comparison
GLM-5.3-Flash vs Qwen2.5 72B Instruct
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 31.9 on the Noometry Index.
Last verified . 20 shared benchmarks.
Alibaba (Qwen)Summary
- They share 20 benchmarks with published results for both. GLM-5.3-Flash scores higher in 9 categories and Qwen2.5 72B Instruct in 0 categories; 9 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-5.3-Flash leads 53.3 to 19.3.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 93.9% for GLM-5.3-Flash and 8.1% for Qwen2.5 72B Instruct.
- GLM-5.3-Flash is cheaper at $0.15 / $0.50 per million input/output tokens, against $1.40 / $5.60 for Qwen2.5 72B Instruct.
- GLM-5.3-Flash accepts more context: 1M tokens versus 131K.
Side by side
| GLM-5.3-Flash | Qwen2.5 72B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 51.8 | 31.9 |
| Released | 2026-08-20 | 2024-09 |
| Weights | Open | Open |
| Context window | 1M | 131K |
| Max output | 131K | 8K |
| Input $ / M tokens | $0.15 | $1.40 |
| Output $ / M tokens | $0.50 | $5.60 |
| Results tracked | 40 | 43 |
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Category by category
Coding GLM-5.3-Flash leads
GLM-5.3-Flash: 53.1 (#31), Qwen2.5 72B Instruct: 33.2 (#260)
| Benchmark | GLM-5.3-Flash | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Coding | 1508 | 1292 |
| DeepSWE | 63.4% | — |
| FrontierCode | 31.8% | — |
| CursorBench | 36.8% | — |
| LMArena WebDev | 1609 | — |
| FrontierSWE | 18.1% | — |
| SciCode | 51.6% | — |
| WeirdML | — | 16% |
| BigCodeBench Instruct | — | 45.8% |
| BigCodeBench Complete | — | 55.9% |
| ALE-Bench | 303.55 | — |
Agentic & Tool Use GLM-5.3-Flash leads
GLM-5.3-Flash: 34.2 (#47), Qwen2.5 72B Instruct: 22.1 (#133)
| Benchmark | GLM-5.3-Flash | Qwen2.5 72B Instruct |
|---|---|---|
| APEX-Agents | 52.8% | — |
| TheAgentCompany | — | 5.7% |
| BALROG | — | 16.2% |
| GDP.pdf | 14% | — |
| METR Time Horizons | — | 35.8% |
Reasoning GLM-5.3-Flash leads
GLM-5.3-Flash: 48.0 (#42), Qwen2.5 72B Instruct: 22.3 (#199)
| Benchmark | GLM-5.3-Flash | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1491 | 1271 |
| Epoch Capabilities Index | 151.88 | 129 |
| ARC-AGI-2 | 65.8% | — |
| ARC-AGI-1 | 91% | — |
| CritPt | 15.4% | — |
| Chess Puzzles | 14% | — |
| Mystery Game Puzzles | 8% | — |
| DTBench | — | 62.9% |
| LMCA | — | 13.4% |
| Surface Evolver Bench | 52.5% | — |
| Bench to the Future 3 | 0.15 | — |
| BIG-Bench Hard | — | 79.8% |
| ForecastBench | — | 57.5 |
| HellaSwag | — | 84.8% |
| PIQA | — | 82.6% |
| WinoGrande | — | 82.3% |
Math GLM-5.3-Flash leads
GLM-5.3-Flash: 53.3 (#47), Qwen2.5 72B Instruct: 19.3 (#287)
| Benchmark | GLM-5.3-Flash | Qwen2.5 72B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 93.9% | 8.1% |
| LMArena Math | 1500 | 1283 |
| FrontierMath (Tiers 1-3) | 55.8% | — |
| FrontierMath Tier 4 | 17.1% | — |
| ProofBench | 21% | — |
| Omni-MATH | — | 33% |
| MATH Level 5 | — | 63.2% |
Knowledge GLM-5.3-Flash leads
GLM-5.3-Flash: 58.4 (#36), Qwen2.5 72B Instruct: 27.0 (#253)
| Benchmark | GLM-5.3-Flash | Qwen2.5 72B Instruct |
|---|---|---|
| GPQA Diamond | 90.2% | 49.1% |
| LMArena Expert | 1513 | 1245 |
| MMLU-Pro | — | 63.1% |
| Confabulations | — | 19.1% |
| GPQA (HELM) | — | 42.6% |
| ARC (AI2) Challenge | — | 94.5% |
| MMLU | — | 85.3% |
| TriviaQA | — | 71.9% |
Multimodal Not comparable
GLM-5.3-Flash: 42.8 (#27), Qwen2.5 72B Instruct: —
| Benchmark | GLM-5.3-Flash | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Vision | 1296 | — |
Multilingual GLM-5.3-Flash leads
GLM-5.3-Flash: 56.0 (#25), Qwen2.5 72B Instruct: 41.0 (#213)
| Benchmark | GLM-5.3-Flash | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Non-English | 1462 | 1252 |
| LMArena Chinese | 1527 | 1272 |
| LMArena French | 1496 | 1280 |
| LMArena German | 1470 | 1234 |
| LMArena Japanese | 1429 | 1180 |
| LMArena Korean | 1446 | 1188 |
| LMArena Russian | 1469 | 1264 |
| LMArena Spanish | 1471 | 1256 |
Instruction Following GLM-5.3-Flash leads
GLM-5.3-Flash: 77.5 (#20), Qwen2.5 72B Instruct: 65.5 (#221)
| Benchmark | GLM-5.3-Flash | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1478 | 1254 |
| IFEval | — | 80.6% |
Long Context GLM-5.3-Flash leads
GLM-5.3-Flash: 45.4 (#39), Qwen2.5 72B Instruct: 38.9 (#188)
| Benchmark | GLM-5.3-Flash | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1482 | 1282 |
Writing & Preference GLM-5.3-Flash leads
GLM-5.3-Flash: 65.3 (#50), Qwen2.5 72B Instruct: 46.7 (#215)
| Benchmark | GLM-5.3-Flash | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Text | 1471 | 1269 |
| LMArena Creative Writing | 1442 | 1221 |
| LMArena Multi-Turn | 1467 | 1272 |
| WildBench | — | 80.2% |
Frequently asked questions
Is GLM-5.3-Flash better than Qwen2.5 72B Instruct?
GLM-5.3-Flash is the stronger model overall, scoring 51.8 to 31.9 on the Noometry Index.
Which is cheaper, GLM-5.3-Flash or Qwen2.5 72B Instruct?
GLM-5.3-Flash is cheaper. It lists at $0.15 per million input tokens and $0.50 per million output tokens; Qwen2.5 72B Instruct lists at $1.40 and $5.60.
Is GLM-5.3-Flash or Qwen2.5 72B Instruct better for coding?
GLM-5.3-Flash scores higher on coding benchmarks: 53.1 versus 33.2 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 Qwen2.5 72B Instruct share?
20 benchmarks have published results for both models. GLM-5.3-Flash has 40 scored results on Noometry and Qwen2.5 72B Instruct has 43.