Model comparison
GLM-4.6V vs Qwen3 8B
GLM-4.6V is the stronger model overall, scoring 41.3 to 33.7 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in reasoning, where GLM-4.6V leads 27.6 to 16.6.
- Qwen3 8B is cheaper at $0.18 / $0.70 per million input/output tokens, against $0.30 / $0.90 for GLM-4.6V.
- Qwen3 8B accepts more context: 131K tokens versus 128K.
Side by side
| GLM-4.6V | Qwen3 8B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 41.3 | 33.7 |
| Released | 2025-12-08 | 2025-04 |
| Weights | Open | Open |
| Context window | 128K | 131K |
| Max output | 33K | 8K |
| Input $ / M tokens | $0.30 | $0.18 |
| Output $ / M tokens | $0.90 | $0.70 |
| Results tracked | 12 | 11 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.9 (#128), Qwen3 8B: 34.0 (#248)
| Benchmark | GLM-4.6V | Qwen3 8B |
|---|---|---|
| SciCode | — | 22.6% |
| LMArena Coding | 1390 | — |
Agentic & Tool Use Not comparable
GLM-4.6V: —, Qwen3 8B: 30.2 (#78)
| Benchmark | GLM-4.6V | Qwen3 8B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 42.6% |
Reasoning GLM-4.6V leads
GLM-4.6V: 27.6 (#115), Qwen3 8B: 16.6 (#303)
| Benchmark | GLM-4.6V | Qwen3 8B |
|---|---|---|
| CritPt | — | 0% |
| Chess Puzzles | — | 5% |
| LMArena Hard Prompts | 1368 | — |
| DTBench | — | 59.7% |
| LMCA | — | 8.8% |
| Epoch Capabilities Index | — | 136.17 |
Math Not comparable
GLM-4.6V: —, Qwen3 8B: 34.9 (#191)
| Benchmark | GLM-4.6V | Qwen3 8B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 56.1% |
Knowledge GLM-4.6V leads
GLM-4.6V: 38.0 (#149), Qwen3 8B: 36.1 (#173)
| Benchmark | GLM-4.6V | Qwen3 8B |
|---|---|---|
| GPQA Diamond | — | 56.8% |
| Vectara Hallucination Rate | — | 4.8% |
| LMArena Expert | 1371 | — |
Multimodal Not comparable
GLM-4.6V: 34.8 (#90), Qwen3 8B: —
| Benchmark | GLM-4.6V | Qwen3 8B |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual Not comparable
GLM-4.6V: 48.6 (#141), Qwen3 8B: —
| Benchmark | GLM-4.6V | Qwen3 8B |
|---|---|---|
| LMArena Non-English | 1359 | — |
| LMArena Chinese | 1425 | — |
| LMArena Russian | 1340 | — |
Instruction Following Not comparable
GLM-4.6V: 71.4 (#151), Qwen3 8B: —
| Benchmark | GLM-4.6V | Qwen3 8B |
|---|---|---|
| LMArena Instruction Following | 1352 | — |
Long Context GLM-4.6V leads
GLM-4.6V: 41.3 (#143), Qwen3 8B: 37.9 (#210)
| Benchmark | GLM-4.6V | Qwen3 8B |
|---|---|---|
| Fiction.LiveBench | — | 62.1% |
| LMArena Longer Query | 1358 | — |
Writing & Preference Not comparable
GLM-4.6V: 56.6 (#137), Qwen3 8B: —
| Benchmark | GLM-4.6V | Qwen3 8B |
|---|---|---|
| LMArena Text | 1377 | — |
| LMArena Creative Writing | 1347 | — |
| LMArena Multi-Turn | 1360 | — |
Frequently asked questions
Is GLM-4.6V better than Qwen3 8B?
GLM-4.6V is the stronger model overall, scoring 41.3 to 33.7 on the Noometry Index.
Which is cheaper, GLM-4.6V or Qwen3 8B?
Qwen3 8B is cheaper. It lists at $0.18 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 Qwen3 8B better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.9 versus 34.0 in the Noometry coding category.
Which has the bigger context window?
Qwen3 8B does, with 131K tokens against 128K.
How many benchmarks do GLM-4.6V and Qwen3 8B share?
0 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen3 8B has 11.