Model comparison
GLM-4.6V vs Qwen2.5 32B Instruct
GLM-4.6V is the stronger model overall, scoring 40.9 to 29.7 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in knowledge, where GLM-4.6V leads 37.4 to 24.9.
- GLM-4.6V is cheaper at $0.30 / $0.90 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 128K.
Side by side
| GLM-4.6V | Qwen2.5 32B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 40.9 | 29.7 |
| Released | 2025-12-08 | 2024-09 |
| Weights | Open | Open |
| Context window | 128K | 131K |
| Max output | 33K | 8K |
| Input $ / M tokens | $0.30 | $0.70 |
| Output $ / M tokens | $0.90 | $2.80 |
| Results tracked | 12 | 7 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.2 (#128), Qwen2.5 32B Instruct: 38.1 (#165)
| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| BigCodeBench Instruct | — | 45% |
| LMArena Coding | 1390 | — |
| BigCodeBench Complete | — | 52.3% |
Reasoning GLM-4.6V leads
GLM-4.6V: 26.5 (#117), Qwen2.5 32B Instruct: 18.5 (#262)
| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1368 | — |
| Epoch Capabilities Index | — | 128.52 |
Math Not comparable
GLM-4.6V: —, Qwen2.5 32B Instruct: 15.3 (#289)
| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 7.4% |
| MATH Level 5 | — | 56.1% |
Knowledge GLM-4.6V leads
GLM-4.6V: 37.4 (#148), Qwen2.5 32B Instruct: 24.9 (#259)
| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| GPQA Diamond | — | 46.1% |
| LMArena Expert | 1372 | — |
Multimodal Not comparable
GLM-4.6V: 34.2 (#83), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual Not comparable
GLM-4.6V: 48.8 (#135), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Non-English | 1359 | — |
| LMArena Chinese | 1426 | — |
| LMArena Russian | 1341 | — |
Instruction Following Not comparable
GLM-4.6V: 71.6 (#145), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Instruction Following | 1351 | — |
Long Context Not comparable
GLM-4.6V: 41.3 (#138), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Longer Query | 1358 | — |
Writing & Preference Not comparable
GLM-4.6V: 56.8 (#132), Qwen2.5 32B Instruct: —
| Benchmark | GLM-4.6V | Qwen2.5 32B Instruct |
|---|---|---|
| LMArena Text | 1377 | — |
| LMArena Creative Writing | 1347 | — |
| LMArena Multi-Turn | 1359 | — |
Frequently asked questions
Is GLM-4.6V better than Qwen2.5 32B Instruct?
GLM-4.6V is the stronger model overall, scoring 40.9 to 29.7 on the Noometry Index.
Which is cheaper, GLM-4.6V or Qwen2.5 32B Instruct?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Qwen2.5 32B Instruct lists at $0.70 and $2.80.
Is GLM-4.6V or Qwen2.5 32B Instruct better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.2 versus 38.1 in the Noometry coding category.
Which has the bigger context window?
Qwen2.5 32B Instruct does, with 131K tokens against 128K.
How many benchmarks do GLM-4.6V and Qwen2.5 32B Instruct share?
0 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen2.5 32B Instruct has 7.