Model comparison
GLM-4.5 vs Qwen2.5 32B Instruct
GLM-4.5 is the stronger model overall, scoring 42.0 to 30.1 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in math, where GLM-4.5 leads 39.0 to 16.2.
- GLM-4.5 is cheaper at $0.60 / $2.20 per million input/output tokens, against $0.70 / $2.80 for Qwen2.5 32B Instruct.
Side by side
| GLM-4.5 | Qwen2.5 32B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 42.0 | 30.1 |
| Released | 2025-07-27 | 2024-09 |
| Weights | Open | Open |
| Context window | 131K | 131K |
| Max output | 98K | 8K |
| Input $ / M tokens | $0.60 | $0.70 |
| Output $ / M tokens | $2.20 | $2.80 |
| Results tracked | 27 | 7 |
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Category by category
Coding GLM-4.5 leads
GLM-4.5: 41.4 (#125), Qwen2.5 32B Instruct: 38.7 (#169)
| Benchmark | GLM-4.5 | Qwen2.5 32B Instruct |
|---|---|---|
| SWE-bench Verified (bash only) | 54.2% | — |
| WeirdML | 40.6% | — |
| BigCodeBench Instruct | — | 45% |
| LMArena Coding | 1434 | — |
| BigCodeBench Complete | — | 52.3% |
| ALE-Bench | 344.82 | — |
| AlgoTune | 1.52 | — |
Reasoning GLM-4.5 leads
GLM-4.5: 28.6 (#100), Qwen2.5 32B Instruct: 19.2 (#266)
| Benchmark | GLM-4.5 | Qwen2.5 32B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 57.9% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1429 | — |
| Epoch Capabilities Index | — | 128.52 |
Math GLM-4.5 leads
GLM-4.5: 39.0 (#116), Qwen2.5 32B Instruct: 16.2 (#296)
| Benchmark | GLM-4.5 | Qwen2.5 32B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 7.4% |
| LMArena Math | 1427 | — |
| MATH Level 5 | — | 56.1% |
Knowledge GLM-4.5 leads
GLM-4.5: 35.9 (#179), Qwen2.5 32B Instruct: 24.9 (#266)
| Benchmark | GLM-4.5 | Qwen2.5 32B Instruct |
|---|---|---|
| GPQA Diamond | — | 46.1% |
| Humanity's Last Exam | 8.3% | — |
| Confabulations | 11.3% | — |
| LMArena Expert | 1433 | — |
Multilingual Not comparable
GLM-4.5: 52.8 (#77), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.5 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Non-English | 1417 | — |
| LMArena Chinese | 1465 | — |
| LMArena French | 1418 | — |
| LMArena German | 1407 | — |
| LMArena Japanese | 1415 | — |
| LMArena Korean | 1380 | — |
| LMArena Russian | 1414 | — |
| LMArena Spanish | 1454 | — |
Instruction Following Not comparable
GLM-4.5: 74.1 (#104), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.5 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Instruction Following | 1404 | — |
Long Context Not comparable
GLM-4.5: 38.2 (#201), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.5 | Qwen2.5 32B Instruct |
|---|---|---|
| Fiction.LiveBench | 58.3% | — |
| LMArena Longer Query | 1412 | — |
Writing & Preference Not comparable
GLM-4.5: 57.5 (#127), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.5 | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Text | 1430 | — |
| LMArena Creative Writing | 1395 | — |
| Short-Story Creative Writing | 73.4% | — |
| EQ-Bench Creative Writing | 1343 | — |
| LMArena Multi-Turn | 1415 | — |
Frequently asked questions
Is GLM-4.5 better than Qwen2.5 32B Instruct?
GLM-4.5 is the stronger model overall, scoring 42.0 to 30.1 on the Noometry Index.
Which is cheaper, GLM-4.5 or Qwen2.5 32B Instruct?
GLM-4.5 is cheaper. It lists at $0.60 per million input tokens and $2.20 per million output tokens; Qwen2.5 32B Instruct lists at $0.70 and $2.80.
Is GLM-4.5 or Qwen2.5 32B Instruct better for coding?
GLM-4.5 scores higher on coding benchmarks: 41.4 versus 38.7 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 32B Instruct share?
0 benchmarks have published results for both models. GLM-4.5 has 27 scored results on Noometry and Qwen2.5 32B Instruct has 7.