Model comparison
GLM-4.6V vs Qwen2.5 7B Instruct
GLM-4.6V is the stronger model overall, scoring 41.3 to 29.0 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in knowledge, where GLM-4.6V leads 38.0 to 17.0.
- Qwen2.5 7B Instruct is cheaper at $0.17 / $0.70 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
- Qwen2.5 7B Instruct accepts more context: 131K tokens versus 128K.
Side by side
| GLM-4.6V | Qwen2.5 7B Instruct | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.3 | 29.0 |
| Released | 2025-12-08 | 2024-09 |
| Weights | Open | Open |
| Context window | 128K | 131K |
| Max output | 33K | 8K |
| Input $ / M tokens | $0.30 | $0.17 |
| Output $ / M tokens | $0.90 | $0.70 |
| Results tracked | 12 | 15 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.9 (#128), Qwen2.5 7B Instruct: 36.5 (#208)
| Benchmark | GLM-4.6V | Qwen2.5 7B Instruct |
|---|---|---|
| BigCodeBench Instruct | — | 37.6% |
| LMArena Coding | 1390 | — |
| BigCodeBench Complete | — | 46.1% |
Agentic & Tool Use Not comparable
GLM-4.6V: —, Qwen2.5 7B Instruct: 23.8 (#124)
| Benchmark | GLM-4.6V | Qwen2.5 7B Instruct |
|---|---|---|
| BALROG | — | 7.8% |
Reasoning GLM-4.6V leads
GLM-4.6V: 27.6 (#115), Qwen2.5 7B Instruct: 14.8 (#322)
| Benchmark | GLM-4.6V | Qwen2.5 7B Instruct |
|---|---|---|
| Chess Puzzles | — | 0% |
| LMArena Hard Prompts | 1368 | — |
| DTBench | — | 47.7% |
| LMCA | — | 6.4% |
| Epoch Capabilities Index | — | 118.51 |
Math Not comparable
GLM-4.6V: —, Qwen2.5 7B Instruct: 12.6 (#306)
| Benchmark | GLM-4.6V | Qwen2.5 7B Instruct |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 2.5% |
| Omni-MATH | — | 29.4% |
Knowledge GLM-4.6V leads
GLM-4.6V: 38.0 (#149), Qwen2.5 7B Instruct: 17.0 (#286)
| Benchmark | GLM-4.6V | Qwen2.5 7B Instruct |
|---|---|---|
| GPQA Diamond | — | 35.5% |
| MMLU-Pro | — | 53.9% |
| GPQA (HELM) | — | 34.1% |
| LMArena Expert | 1371 | — |
| MMLU | — | 72.9% |
Multimodal Not comparable
GLM-4.6V: 34.8 (#90), Qwen2.5 7B Instruct: —
| Benchmark | GLM-4.6V | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual Not comparable
GLM-4.6V: 48.6 (#141), Qwen2.5 7B Instruct: —
| Benchmark | GLM-4.6V | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Non-English | 1359 | — |
| LMArena Chinese | 1425 | — |
| LMArena Russian | 1340 | — |
Instruction Following GLM-4.6V leads
GLM-4.6V: 71.4 (#151), Qwen2.5 7B Instruct: 63.2 (#231)
| Benchmark | GLM-4.6V | Qwen2.5 7B Instruct |
|---|---|---|
| IFEval | — | 74.1% |
| LMArena Instruction Following | 1352 | — |
Long Context Not comparable
GLM-4.6V: 41.3 (#143), Qwen2.5 7B Instruct: —
| Benchmark | GLM-4.6V | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Longer Query | 1358 | — |
Writing & Preference GLM-4.6V leads
GLM-4.6V: 56.6 (#137), Qwen2.5 7B Instruct: 48.8 (#195)
| Benchmark | GLM-4.6V | Qwen2.5 7B Instruct |
|---|---|---|
| LMArena Text | 1377 | — |
| LMArena Creative Writing | 1347 | — |
| WildBench | — | 73.1% |
| LMArena Multi-Turn | 1360 | — |
Frequently asked questions
Is GLM-4.6V better than Qwen2.5 7B Instruct?
GLM-4.6V is the stronger model overall, scoring 41.3 to 29.0 on the Noometry Index.
Which is cheaper, GLM-4.6V or Qwen2.5 7B Instruct?
Qwen2.5 7B Instruct is cheaper. It lists at $0.17 per million input tokens and $0.70 per million output tokens; GLM-4.6V lists at $0.30 and $0.90.
Is GLM-4.6V or Qwen2.5 7B Instruct better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.9 versus 36.5 in the Noometry coding category.
Which has the bigger context window?
Qwen2.5 7B Instruct does, with 131K tokens against 128K.
How many benchmarks do GLM-4.6V and Qwen2.5 7B Instruct share?
0 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen2.5 7B Instruct has 15.