Model comparison
GLM-4.5V vs Qwen3 14B
GLM-4.5V is the stronger model overall, scoring 39.8 to 35.5 on the Noometry Index.
Last verified . 1 shared benchmarks.
Alibaba (Qwen)Summary
- They share 1 benchmark with published results for both. GLM-4.5V scores higher in 3 categories and Qwen3 14B in 2 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in reasoning, where GLM-4.5V leads 27.4 to 18.5.
- The biggest single-benchmark swing is Kagi LLM Benchmark: 59.8% for GLM-4.5V and 49.1% for Qwen3 14B.
- Qwen3 14B is cheaper at $0.35 / $1.40 per million input/output tokens, against $0.60 / $1.80 for GLM-4.5V.
- Qwen3 14B accepts more context: 131K tokens versus 64K.
Side by side
| GLM-4.5V | Qwen3 14B | |
|---|---|---|
| Provider | Z.ai (Zhipu) | Alibaba (Qwen) |
| Noometry Index | 39.8 | 35.5 |
| Released | 2025-08-11 | 2025-04 |
| Weights | Open | Open |
| Context window | 64K | 131K |
| Max output | 16K | 8K |
| Input $ / M tokens | $0.60 | $0.35 |
| Output $ / M tokens | $1.80 | $1.40 |
| Results tracked | 15 | 12 |
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Category by category
Coding GLM-4.5V leads
GLM-4.5V: 39.5 (#155), Qwen3 14B: 37.3 (#195)
| Benchmark | GLM-4.5V | Qwen3 14B |
|---|---|---|
| SciCode | — | 31.6% |
| LMArena Coding | 1347 | — |
Agentic & Tool Use Not comparable
GLM-4.5V: —, Qwen3 14B: 29.6 (#83)
| Benchmark | GLM-4.5V | Qwen3 14B |
|---|---|---|
| Berkeley Function Calling Leaderboard | — | 41% |
Reasoning GLM-4.5V leads
GLM-4.5V: 27.4 (#119), Qwen3 14B: 18.5 (#280)
| Benchmark | GLM-4.5V | Qwen3 14B |
|---|---|---|
| Kagi LLM Benchmark | 59.8% | 49.1% |
| CritPt | — | 0% |
| Chess Puzzles | — | 4% |
| LMArena Hard Prompts | 1334 | — |
| DTBench | — | 64% |
| LMCA | — | 18.2% |
| Epoch Capabilities Index | — | 138.23 |
Math Qwen3 14B leads
GLM-4.5V: 37.4 (#159), Qwen3 14B: 38.6 (#133)
| Benchmark | GLM-4.5V | Qwen3 14B |
|---|---|---|
| OTIS Mock AIME 2024-2025 | — | 66.4% |
| LMArena Math | 1354 | — |
Knowledge Qwen3 14B leads
GLM-4.5V: 37.5 (#156), Qwen3 14B: 39.3 (#134)
| Benchmark | GLM-4.5V | Qwen3 14B |
|---|---|---|
| GPQA Diamond | — | 63.8% |
| Vectara Hallucination Rate | — | 5.4% |
| LMArena Expert | 1353 | — |
Multimodal Not comparable
GLM-4.5V: 34.3 (#92), Qwen3 14B: —
| Benchmark | GLM-4.5V | Qwen3 14B |
|---|---|---|
| LMArena Vision | 1154 | — |
Multilingual Not comparable
GLM-4.5V: 44.6 (#177), Qwen3 14B: —
| Benchmark | GLM-4.5V | Qwen3 14B |
|---|---|---|
| LMArena Non-English | 1303 | — |
| LMArena Chinese | 1337 | — |
| LMArena Russian | 1298 | — |
| LMArena Spanish | 1336 | — |
Instruction Following Not comparable
GLM-4.5V: 69.2 (#175), Qwen3 14B: —
| Benchmark | GLM-4.5V | Qwen3 14B |
|---|---|---|
| LMArena Instruction Following | 1311 | — |
Long Context GLM-4.5V leads
GLM-4.5V: 39.6 (#171), Qwen3 14B: 38.1 (#204)
| Benchmark | GLM-4.5V | Qwen3 14B |
|---|---|---|
| Fiction.LiveBench | — | 62.5% |
| LMArena Longer Query | 1304 | — |
Writing & Preference Not comparable
GLM-4.5V: 52.5 (#170), Qwen3 14B: —
| Benchmark | GLM-4.5V | Qwen3 14B |
|---|---|---|
| LMArena Text | 1333 | — |
| LMArena Creative Writing | 1295 | — |
| LMArena Multi-Turn | 1332 | — |
Frequently asked questions
Is GLM-4.5V better than Qwen3 14B?
GLM-4.5V is the stronger model overall, scoring 39.8 to 35.5 on the Noometry Index.
Which is cheaper, GLM-4.5V or Qwen3 14B?
Qwen3 14B is cheaper. It lists at $0.35 per million input tokens and $1.40 per million output tokens; GLM-4.5V lists at $0.60 and $1.80.
Is GLM-4.5V or Qwen3 14B better for coding?
GLM-4.5V scores higher on coding benchmarks: 39.5 versus 37.3 in the Noometry coding category.
Which has the bigger context window?
Qwen3 14B does, with 131K tokens against 64K.
How many benchmarks do GLM-4.5V and Qwen3 14B share?
1 benchmark has published results for both models. GLM-4.5V has 15 scored results on Noometry and Qwen3 14B has 12.