Model comparison
GLM-4.7 vs Qwen2.5 72B Instruct
GLM-4.7 is the stronger model overall, scoring 42.0 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-4.7 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 knowledge, where GLM-4.7 leads 47.0 to 27.0.
- The biggest single-benchmark swing is OTIS Mock AIME 2024-2025: 83.3% for GLM-4.7 and 8.1% for Qwen2.5 72B Instruct.
- GLM-4.7 is cheaper at $0.60 / $2.20 per million input/output tokens, against $1.40 / $5.60 for Qwen2.5 72B Instruct.
- GLM-4.7 accepts more context: 205K tokens versus 131K.
Side by side
| GLM-4.7 | Qwen2.5 72B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 42.0 | 31.9 |
| Released | 2025-12-22 | 2024-09 |
| Weights | Open | Open |
| Context window | 205K | 131K |
| Max output | 131K | 8K |
| Input $ / M tokens | $0.60 | $1.40 |
| Output $ / M tokens | $2.20 | $5.60 |
| Results tracked | 36 | 43 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Qwen2.5 72B Instruct: 33.2 (#260)
| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Coding | 1454 | 1292 |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| WeirdML | — | 16% |
| BigCodeBench Instruct | — | 45.8% |
| BigCodeBench Complete | — | 55.9% |
| ALE-Bench | 399.48 | — |
Agentic & Tool Use GLM-4.7 leads
GLM-4.7: 26.5 (#103), Qwen2.5 72B Instruct: 22.1 (#133)
| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| TheAgentCompany | — | 5.7% |
| BALROG | — | 16.2% |
| METR Time Horizons | — | 35.8% |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), Qwen2.5 72B Instruct: 22.3 (#199)
| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1271 |
| Epoch Capabilities Index | 143.51 | 129 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| DTBench | — | 62.9% |
| LMCA | — | 13.4% |
| BIG-Bench Hard | — | 79.8% |
| ForecastBench | — | 57.5 |
| HellaSwag | — | 84.8% |
| PIQA | — | 82.6% |
| WinoGrande | — | 82.3% |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), Qwen2.5 72B Instruct: 19.3 (#287)
| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 83.3% | 8.1% |
| LMArena Math | 1423 | 1283 |
| ProofBench | 6% | — |
| Omni-MATH | — | 33% |
| MATH Level 5 | — | 63.2% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Qwen2.5 72B Instruct: 27.0 (#253)
| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| GPQA Diamond | 83.3% | 49.1% |
| LMArena Expert | 1424 | 1245 |
| SimpleQA Verified | 32.2% | — |
| MMLU-Pro | — | 63.1% |
| Confabulations | — | 19.1% |
| Vectara Hallucination Rate | 11.7% | — |
| GPQA (HELM) | — | 42.6% |
| ARC (AI2) Challenge | — | 94.5% |
| MMLU | — | 85.3% |
| TriviaQA | — | 71.9% |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), Qwen2.5 72B Instruct: 41.0 (#213)
| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Non-English | 1417 | 1252 |
| LMArena Chinese | 1495 | 1272 |
| LMArena French | 1432 | 1280 |
| LMArena German | 1424 | 1234 |
| LMArena Japanese | 1439 | 1180 |
| LMArena Korean | 1399 | 1188 |
| LMArena Russian | 1423 | 1264 |
| LMArena Spanish | 1434 | 1256 |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Qwen2.5 72B Instruct: 65.5 (#221)
| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Instruction Following | 1411 | 1254 |
| IFEval | — | 80.6% |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#116), Qwen2.5 72B Instruct: 38.9 (#188)
| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Longer Query | 1432 | 1282 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Qwen2.5 72B Instruct: 46.7 (#215)
| Benchmark | GLM-4.7 | Qwen2.5 72B Instruct |
|---|---|---|
| LMArena Text | 1435 | 1269 |
| LMArena Creative Writing | 1401 | 1221 |
| LMArena Multi-Turn | 1446 | 1272 |
| EQ-Bench Creative Writing | 1413 | — |
| WildBench | — | 80.2% |
Frequently asked questions
Is GLM-4.7 better than Qwen2.5 72B Instruct?
GLM-4.7 is the stronger model overall, scoring 42.0 to 31.9 on the Noometry Index.
Which is cheaper, GLM-4.7 or Qwen2.5 72B Instruct?
GLM-4.7 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-4.7 or Qwen2.5 72B Instruct better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 33.2 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7 does, with 205K tokens against 131K.
How many benchmarks do GLM-4.7 and Qwen2.5 72B Instruct share?
20 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen2.5 72B Instruct has 43.