Model comparison
GLM-4.7 vs Qwen2.5-Coder-32B
GLM-4.7 is the stronger model overall, scoring 42.0 to 33.4 on the Noometry Index.
Last verified . 13 shared benchmarks.
Alibaba (Qwen)Summary
- They share 13 benchmarks with published results for both. GLM-4.7 scores higher in 8 categories and Qwen2.5-Coder-32B in 0 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in coding, where GLM-4.7 leads 44.0 to 22.6.
- Qwen2.5-Coder-32B is cheaper at $0.66 / $1 per million input/output tokens, against $0.60 / $2.20 for GLM-4.7.
- GLM-4.7 accepts more context: 205K tokens versus 33K.
Side by side
| GLM-4.7 | Qwen2.5-Coder-32B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 42.0 | 33.4 |
| Released | 2025-12-22 | 2024-09-18 |
| Weights | Open | Open |
| Context window | 205K | 33K |
| Max output | 131K | 29K |
| Input $ / M tokens | $0.60 | $0.66 |
| Output $ / M tokens | $2.20 | $1 |
| Results tracked | 36 | 31 |
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Category by category
Coding GLM-4.7 leads
GLM-4.7: 44.0 (#79), Qwen2.5-Coder-32B: 22.6 (#333)
| Benchmark | GLM-4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Coding | 1454 | 1276 |
| SWE-bench Verified (bash only) | — | 9% |
| Aider Polyglot | — | 16.4% |
| LMArena WebDev | 1435 | — |
| SciCode | 45.1% | — |
| BigCodeBench Instruct | — | 49% |
| LiveBench Coding | — | 56.9% |
| BigCodeBench Complete | — | 58% |
| ALE-Bench | 399.48 | — |
| HumanEval+ | — | 87.2% |
| MBPP+ | — | 77% |
Agentic & Tool Use Not comparable
GLM-4.7: 26.5 (#103), Qwen2.5-Coder-32B: —
| Benchmark | GLM-4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| Terminal-Bench | 33.4% | — |
| Vending-Bench 2 | 2,377 | — |
Reasoning GLM-4.7 leads
GLM-4.7: 24.3 (#164), Qwen2.5-Coder-32B: 21.2 (#225)
| Benchmark | GLM-4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Hard Prompts | 1443 | 1251 |
| Epoch Capabilities Index | 143.51 | 119.49 |
| SimpleBench | 47.7% | — |
| CritPt | 1.7% | — |
| Chess Puzzles | 6% | — |
| LiveBench Reasoning | — | 42.1% |
| LiveBench Data Analysis | — | 49.9% |
| HellaSwag | — | 83% |
| LiveBench | — | 46.2% |
| WinoGrande | — | 80.8% |
Math GLM-4.7 leads
GLM-4.7: 38.6 (#135), Qwen2.5-Coder-32B: 33.3 (#204)
| Benchmark | GLM-4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Math | 1423 | 1251 |
| OTIS Mock AIME 2024-2025 | 83.3% | — |
| ProofBench | 6% | — |
| LiveBench Math | — | 46.6% |
| FrontierMath (Feb 2025 set) | 2.4% | — |
| FrontierMath Tier 4 (v1) | 0% | — |
| GSM8K | — | 93% |
Knowledge GLM-4.7 leads
GLM-4.7: 47.0 (#80), Qwen2.5-Coder-32B: 33.4 (#203)
| Benchmark | GLM-4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Expert | 1424 | 1221 |
| GPQA Diamond | 83.3% | — |
| SimpleQA Verified | 32.2% | — |
| Vectara Hallucination Rate | 11.7% | — |
| ARC (AI2) Challenge | — | 70.5% |
| MMLU | — | 79.1% |
Multilingual GLM-4.7 leads
GLM-4.7: 52.8 (#79), Qwen2.5-Coder-32B: 37.8 (#235)
| Benchmark | GLM-4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Non-English | 1417 | 1205 |
| LMArena Chinese | 1495 | 1222 |
| LMArena Russian | 1423 | 1228 |
| LMArena French | 1432 | — |
| LMArena German | 1424 | — |
| LMArena Japanese | 1439 | — |
| LMArena Korean | 1399 | — |
| LMArena Spanish | 1434 | — |
Instruction Following GLM-4.7 leads
GLM-4.7: 74.4 (#95), Qwen2.5-Coder-32B: 61.4 (#245)
| Benchmark | GLM-4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Instruction Following | 1411 | 1223 |
| LiveBench Instruction Following | — | 58.7% |
Long Context GLM-4.7 leads
GLM-4.7: 42.8 (#116), Qwen2.5-Coder-32B: 38.0 (#208)
| Benchmark | GLM-4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Longer Query | 1432 | 1251 |
| CL-bench | 15.9% | — |
| CL-bench Life | 10.9% | — |
Writing & Preference GLM-4.7 leads
GLM-4.7: 60.9 (#93), Qwen2.5-Coder-32B: 41.6 (#240)
| Benchmark | GLM-4.7 | Qwen2.5-Coder-32B |
|---|---|---|
| LMArena Text | 1435 | 1230 |
| LMArena Creative Writing | 1401 | 1174 |
| LMArena Multi-Turn | 1446 | 1222 |
| EQ-Bench Creative Writing | 1413 | — |
| LiveBench Language | — | 23.3% |
Frequently asked questions
Is GLM-4.7 better than Qwen2.5-Coder-32B?
GLM-4.7 is the stronger model overall, scoring 42.0 to 33.4 on the Noometry Index.
Which is cheaper, GLM-4.7 or Qwen2.5-Coder-32B?
Qwen2.5-Coder-32B is cheaper. It lists at $0.66 per million input tokens and $1 per million output tokens; GLM-4.7 lists at $0.60 and $2.20.
Is GLM-4.7 or Qwen2.5-Coder-32B better for coding?
GLM-4.7 scores higher on coding benchmarks: 44.0 versus 22.6 in the Noometry coding category.
Which has the bigger context window?
GLM-4.7 does, with 205K tokens against 33K.
How many benchmarks do GLM-4.7 and Qwen2.5-Coder-32B share?
13 benchmarks have published results for both models. GLM-4.7 has 36 scored results on Noometry and Qwen2.5-Coder-32B has 31.