Model comparison
GLM-4.6V vs Qwen3 14B
GLM-4.6V is the stronger model overall, scoring 40.9 to 35.0 on the Noometry Index.
Last verified . 0 shared benchmarks.
Alibaba (Qwen)Summary
- The widest gap is in reasoning, where GLM-4.6V leads 26.5 to 18.1.
- GLM-4.6V is cheaper at $0.30 / $0.90 per million input/output tokens, against $0.35 / $1.40 for Qwen3 14B.
- Qwen3 14B accepts more context: 131K tokens versus 128K.
Side by side
| GLM-4.6V | Qwen3 14B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 40.9 | 35.0 |
| Released | 2025-12-08 | 2025-04 |
| Weights | Open | Open |
| Context window | 128K | 131K |
| Max output | 33K | 8K |
| Input $ / M tokens | $0.30 | $0.35 |
| Output $ / M tokens | $0.90 | $1.40 |
| Results tracked | 12 | 12 |
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Category by category
Coding GLM-4.6V leads
GLM-4.6V: 40.2 (#128), Qwen3 14B: 36.7 (#191)
| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| SciCode | — | 31.6% |
| LMArena Coding | 1390 | — |
Agentic & Tool Use Not comparable
GLM-4.6V: —, Qwen3 14B: 29.4 (#78)
| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |
Reasoning GLM-4.6V leads
GLM-4.6V: 26.5 (#117), Qwen3 14B: 18.1 (#272)
| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | — | 49.1% |
| CritPt | — | 0% |
| Chess Puzzles | — | 4% |
| LMArena Hard Prompts | 1368 | — |
| DTBench | — | 64% |
| LMCA | — | 18.2% |
| Epoch Capabilities Index | — | 138.23 |
Math Not comparable
GLM-4.6V: —, Qwen3 14B: 37.0 (#102)
| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 66.4% |
Knowledge Qwen3 14B leads
GLM-4.6V: 37.4 (#148), Qwen3 14B: 39.2 (#124)
| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| GPQA Diamond | — | 63.8% |
| Vectara Hallucination Rate | — | 5.4% |
| LMArena Expert | 1372 | — |
Multimodal Not comparable
GLM-4.6V: 34.2 (#83), Qwen3 14B: —
| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| LMArena Vision | 1164 | — |
Multilingual Not comparable
GLM-4.6V: 48.8 (#135), Qwen3 14B: —
| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1359 | — |
| LMArena Chinese | 1426 | — |
| LMArena Russian | 1341 | — |
Instruction Following Not comparable
GLM-4.6V: 71.6 (#145), Qwen3 14B: —
| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| LMArena Instruction Following | 1351 | — |
Long Context GLM-4.6V leads
GLM-4.6V: 41.3 (#138), Qwen3 14B: 38.0 (#201)
| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
| LMArena Longer Query | 1358 | — |
Writing & Preference Not comparable
GLM-4.6V: 56.8 (#132), Qwen3 14B: —
| Benchmark | GLM-4.6V | Qwen3 14B |
|---|---|---|
| LMArena Text | 1377 | — |
| LMArena Creative Writing | 1347 | — |
| LMArena Multi-Turn | 1359 | — |
Frequently asked questions
Is GLM-4.6V better than Qwen3 14B?
GLM-4.6V is the stronger model overall, scoring 40.9 to 35.0 on the Noometry Index.
Which is cheaper, GLM-4.6V or Qwen3 14B?
GLM-4.6V is cheaper. It lists at $0.30 per million input tokens and $0.90 per million output tokens; Qwen3 14B lists at $0.35 and $1.40.
Is GLM-4.6V or Qwen3 14B better for coding?
GLM-4.6V scores higher on coding benchmarks: 40.2 versus 36.7 in the Noometry coding category.
Which has the bigger context window?
Qwen3 14B does, with 131K tokens against 128K.
How many benchmarks do GLM-4.6V and Qwen3 14B share?
0 benchmarks have published results for both models. GLM-4.6V has 12 scored results on Noometry and Qwen3 14B has 12.