Model comparison
GLM-4.5 vs Qwen2.5 72B Instruct
GLM-4.5 is the stronger model overall, scoring 42.0 to 31.9 on the Noometry Index.
Last verified . 19 shared benchmarks.
Alibaba (Qwen)Summary
- They share 19 benchmarks with published results for both. GLM-4.5 scores higher in 7 categories and Qwen2.5 72B Instruct in 1 category; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.5 leads 39.0 to 19.3.
- The biggest single-benchmark swing is WeirdML: 40.6% for GLM-4.5 and 16% for Qwen2.5 72B Instruct.
- GLM-4.5 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.40 / $5.60 for Qwen2.5 72B Instruct.
Side by side
| GLM-4.5 | Qwen2.5 72B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 42.0 | 31.9 |
| Released | 2025-07-27 | 2024-09 |
| Weights | Open | Open |
| Context window | 131K | 131K |
| Max output | 98K | 8K |
| Input $ / M tokens | $0.60 | $1.40 |
| Output $ / M tokens | $2.20 | $5.60 |
| Results tracked | 27 | 43 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-4.5 leads
GLM-4.5: 41.4 (#125), Qwen2.5 72B Instruct: 33.2 (#260)
| Benchmark | GLM-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| WeirdML | 40.6% | 16% |
| LMArena Coding | 1434 | 1292 |
| SWE-bench Verified (bash only) | 54.2% | — |
| BigCodeBench Instruct | — | 45.8% |
| BigCodeBench Complete | — | 55.9% |
| ALE-Bench | 344.82 | — |
| AlgoTune | 1.52 | — |
Agentic & Tool Use Not comparable
GLM-4.5: —, Qwen2.5 72B Instruct: 22.1 (#133)
| Benchmark | GLM-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| TheAgentCompany | — | 5.7% |
| BALROG | — | 16.2% |
| METR Time Horizons | — | 35.8% |
Reasoning GLM-4.5 leads
GLM-4.5: 28.6 (#100), Qwen2.5 72B Instruct: 22.3 (#199)
| Benchmark | GLM-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1429 | 1271 |
| Kagi LLM Benchmark | 57.9% | — |
| DTBench | — | 62.9% |
| LMCA | — | 13.4% |
| BIG-Bench Hard | — | 79.8% |
| Epoch Capabilities Index | — | 129 |
| ForecastBench | — | 57.5 |
| HellaSwag | — | 84.8% |
| PIQA | — | 82.6% |
| WinoGrande | — | 82.3% |
Math GLM-4.5 leads
GLM-4.5: 39.0 (#116), Qwen2.5 72B Instruct: 19.3 (#287)
| Benchmark | GLM-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Math | 1427 | 1283 |
| OTIS Mock AIME 2024-2025 | — | 8.1% |
| Omni-MATH | — | 33% |
| MATH Level 5 | — | 63.2% |
Knowledge GLM-4.5 leads
GLM-4.5: 35.9 (#179), Qwen2.5 72B Instruct: 27.0 (#253)
| Benchmark | GLM-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| Confabulations | 11.3% | 19.1% |
| LMArena Expert | 1433 | 1245 |
| GPQA Diamond | — | 49.1% |
| Humanity's Last Exam | 8.3% | — |
| MMLU-Pro | — | 63.1% |
| GPQA (HELM) | — | 42.6% |
| ARC (AI2) Challenge | — | 94.5% |
| MMLU | — | 85.3% |
| TriviaQA | — | 71.9% |
Multilingual GLM-4.5 leads
GLM-4.5: 52.8 (#77), Qwen2.5 72B Instruct: 41.0 (#213)
| Benchmark | GLM-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Non-English | 1417 | 1252 |
| LMArena Chinese | 1465 | 1272 |
| LMArena French | 1418 | 1280 |
| LMArena German | 1407 | 1234 |
| LMArena Japanese | 1415 | 1180 |
| LMArena Korean | 1380 | 1188 |
| LMArena Russian | 1414 | 1264 |
| LMArena Spanish | 1454 | 1256 |
Instruction Following GLM-4.5 leads
GLM-4.5: 74.1 (#104), Qwen2.5 72B Instruct: 65.5 (#221)
| Benchmark | GLM-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1404 | 1254 |
| IFEval | — | 80.6% |
Long Context Too close to call
GLM-4.5: 38.2 (#201), Qwen2.5 72B Instruct: 38.9 (#188)
| Benchmark | GLM-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1412 | 1282 |
| Fiction.LiveBench | 58.3% | — |
Writing & Preference GLM-4.5 leads
GLM-4.5: 57.5 (#127), Qwen2.5 72B Instruct: 46.7 (#215)
| Benchmark | GLM-4.5 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Text | 1430 | 1269 |
| LMArena Creative Writing | 1395 | 1221 |
| LMArena Multi-Turn | 1415 | 1272 |
| Short-Story Creative Writing | 73.4% | — |
| EQ-Bench Creative Writing | 1343 | — |
| WildBench | — | 80.2% |
Frequently asked questions
Is GLM-4.5 better than Qwen2.5 72B Instruct?
GLM-4.5 is the stronger model overall, scoring 42.0 to 31.9 on the Noometry Index.
Which is cheaper, GLM-4.5 or Qwen2.5 72B Instruct?
GLM-4.5 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen2.5 72B Instruct lists at $1.40 and $5.60.
Is GLM-4.5 or Qwen2.5 72B Instruct better for coding?
GLM-4.5 scores higher on coding benchmarks: 41.4 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
Both accept 131K tokens.
How many benchmarks do GLM-4.5 and Qwen2.5 72B Instruct share?
19 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Qwen2.5 72B Instruct has 43.