Model comparison
GLM-4.5V vs Qwen2.5 72B Instruct
GLM-4.5V is the stronger model overall, scoring 39.8 to 31.9 on the Noometry Index.
Last verified . 13 shared benchmarks.
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. GLM-4.5V scores higher in 8 categories and Qwen2.5 72B Instruct in 0 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in math, where GLM-4.5V leads 37.4 to 19.3.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $1.40 / $5.60 for Qwen2.5 72B Instruct.
- Qwen2.5 72B Instruct accepts more context: 131K tokens versus 64K.
Side by side
| GLM-4.5V | Qwen2.5 72B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 39.8 | 31.9 |
| Released | 2025-08-11 | 2024-09 |
| Weights | Open | Open |
| Context window | 64K | 131K |
| Max output | 16K | 8K |
| Input $ / M tokens | $0.60 | $1.40 |
| Output $ / M tokens | $1.80 | $5.60 |
| Results tracked | 15 | 43 |
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Category by category
Coding GLM-4.5V leads
GLM-4.5V: 39.5 (#155), Qwen2.5 72B Instruct: 33.2 (#260)
| Benchmark | GLM-4.5V | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Coding | 1347 | 1292 |
| WeirdML | — | 16% |
| BigCodeBench Instruct | — | 45.8% |
| BigCodeBench Complete | — | 55.9% |
Agentic & Tool Use Not comparable
GLM-4.5V: —, Qwen2.5 72B Instruct: 22.1 (#133)
| Benchmark | GLM-4.5V | Qwen2.5 72B Instruct |
|---|---|---|
| TheAgentCompany | — | 5.7% |
| BALROG | — | 16.2% |
| METR Time Horizons | — | 35.8% |
Reasoning GLM-4.5V leads
GLM-4.5V: 27.4 (#119), Qwen2.5 72B Instruct: 22.3 (#199)
| Benchmark | GLM-4.5V | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1334 | 1271 |
| Kagi LLM Benchmark | 59.8% | — |
| 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-4.5V leads
GLM-4.5V: 37.4 (#159), Qwen2.5 72B Instruct: 19.3 (#287)
| Benchmark | GLM-4.5V | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Math | 1354 | 1283 |
| OTIS Mock AIME 2024-2025 | — | 8.1% |
| Omni-MATH | — | 33% |
| MATH Level 5 | — | 63.2% |
Knowledge GLM-4.5V leads
GLM-4.5V: 37.5 (#156), Qwen2.5 72B Instruct: 27.0 (#253)
| Benchmark | GLM-4.5V | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Expert | 1353 | 1245 |
| GPQA Diamond | — | 49.1% |
| 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-4.5V: 34.3 (#92), Qwen2.5 72B Instruct: —
| Benchmark | GLM-4.5V | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Vision | 1154 | — |
Multilingual GLM-4.5V leads
GLM-4.5V: 44.6 (#177), Qwen2.5 72B Instruct: 41.0 (#213)
| Benchmark | GLM-4.5V | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Non-English | 1303 | 1252 |
| LMArena Chinese | 1337 | 1272 |
| LMArena Russian | 1298 | 1264 |
| LMArena Spanish | 1336 | 1256 |
| LMArena French | — | 1280 |
| LMArena German | — | 1234 |
| LMArena Japanese | — | 1180 |
| LMArena Korean | — | 1188 |
Instruction Following GLM-4.5V leads
GLM-4.5V: 69.2 (#175), Qwen2.5 72B Instruct: 65.5 (#221)
| Benchmark | GLM-4.5V | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1311 | 1254 |
| IFEval | — | 80.6% |
Long Context Too close to call
GLM-4.5V: 39.6 (#171), Qwen2.5 72B Instruct: 38.9 (#188)
| Benchmark | GLM-4.5V | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1304 | 1282 |
Writing & Preference GLM-4.5V leads
GLM-4.5V: 52.5 (#170), Qwen2.5 72B Instruct: 46.7 (#215)
| Benchmark | GLM-4.5V | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Text | 1333 | 1269 |
| LMArena Creative Writing | 1295 | 1221 |
| LMArena Multi-Turn | 1332 | 1272 |
| WildBench | — | 80.2% |
Frequently asked questions
Is GLM-4.5V better than Qwen2.5 72B Instruct?
GLM-4.5V is the stronger model overall, scoring 39.8 to 31.9 on the Noometry Index.
Which is cheaper, GLM-4.5V or Qwen2.5 72B Instruct?
GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Qwen2.5 72B Instruct lists at $1.40 and $5.60.
Is GLM-4.5V or Qwen2.5 72B Instruct better for coding?
GLM-4.5V scores higher on coding benchmarks: 39.5 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
Qwen2.5 72B Instruct does, with 131K tokens against 64K.
How many benchmarks do GLM-4.5V and Qwen2.5 72B Instruct share?
13 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen2.5 72B Instruct has 43.