Model comparison
GLM-4.5V vs Qwen2.5 32B Instruct
GLM-4.5V is the stronger model overall, scoring 39.2 to 29.7 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in math, where GLM-4.5V leads 34.7 to 15.3.
- GLM-4.5V is cheaper at $0.60 / $1.80 per million input/output tokens, against $0.70 / $2.80 for Qwen2.5 32B Instruct.
- Qwen2.5 32B Instruct accepts more context: 131K tokens versus 64K.
Side by side
| GLM-4.5V | Qwen2.5 32B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 39.2 | 29.7 |
| Released | 2025-08-11 | 2024-09 |
| Weights | Open | Open |
| Context window | 64K | 131K |
| Max output | 16K | 8K |
| Input $ / M tokens | $0.60 | $0.70 |
| Output $ / M tokens | $1.80 | $2.80 |
| Results tracked | 15 | 7 |
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Category by category
Coding Too close to call
GLM-4.5V: 38.9 (#152), Qwen2.5 32B Instruct: 38.1 (#165)
| Benchmark | GLM-4.5V | Qwen2.5 32B Instruct |
|---|---|---|
| BigCodeBench Instruct | — | 45% |
| LMArena Coding | 1347 | — |
| BigCodeBench Complete | — | 52.3% |
Reasoning GLM-4.5V leads
GLM-4.5V: 26.5 (#118), Qwen2.5 32B Instruct: 18.5 (#262)
| Benchmark | GLM-4.5V | Qwen2.5 32B Instruct |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | — |
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1335 | — |
| Epoch Capabilities Index | — | 128.52 |
Math GLM-4.5V leads
GLM-4.5V: 34.7 (#157), Qwen2.5 32B Instruct: 15.3 (#289)
| Benchmark | GLM-4.5V | Qwen2.5 32B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 7.4% |
| LMArena Math | 1353 | — |
| MATH Level 5 | — | 56.1% |
Knowledge GLM-4.5V leads
GLM-4.5V: 36.8 (#155), Qwen2.5 32B Instruct: 24.9 (#259)
| Benchmark | GLM-4.5V | Qwen2.5 32B Instruct |
|---|---|---|
| GPQA Diamond | — | 46.1% |
| LMArena Expert | 1353 | — |
Multimodal Not comparable
GLM-4.5V: 33.6 (#86), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.5V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Vision | 1154 | — |
Multilingual Not comparable
GLM-4.5V: 44.9 (#172), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.5V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Non-English | 1304 | — |
| LMArena Chinese | 1338 | — |
| LMArena Russian | 1299 | — |
| LMArena Spanish | 1335 | — |
Instruction Following Not comparable
GLM-4.5V: 69.4 (#170), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.5V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Instruction Following | 1311 | — |
Long Context Not comparable
GLM-4.5V: 39.6 (#166), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.5V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Longer Query | 1305 | — |
Writing & Preference Not comparable
GLM-4.5V: 52.7 (#162), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.5V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Text | 1333 | — |
| LMArena Creative Writing | 1295 | — |
| LMArena Multi-Turn | 1333 | — |
Frequently asked questions
Is GLM-4.5V better than Qwen2.5 32B Instruct?
GLM-4.5V is the stronger model overall, scoring 39.2 to 29.7 on the Noometry Index.
Which is cheaper, GLM-4.5V or Qwen2.5 32B Instruct?
GLM-4.5V is cheaper. It lists at $0.60 per million input tokens and $1.80 per million output tokens; Qwen2.5 32B Instruct lists at $0.70 and $2.80.
Is GLM-4.5V or Qwen2.5 32B Instruct better for coding?
They score almost the same on coding (38.9 vs 38.1); test both on your own repository before choosing.
Which has the bigger context window?
Qwen2.5 32B Instruct does, with 131K tokens against 64K.
How many benchmarks do GLM-4.5V and Qwen2.5 32B Instruct share?
0 benchmarks have published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen2.5 32B Instruct has 7.