Model comparison
DeepSeek-V3.2-Speciale vs Qwen-14B
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 30.8 on the Noometry Index.
Last verified . 0 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- The widest gap is in writing & preference, where DeepSeek-V3.2-Speciale leads 46.1 to 27.8.
Side by side
| DeepSeek-V3.2-Speciale | Qwen-14B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.1 | 30.8 |
| Released | 2025-12-01 | 2023-09-24 |
| Weights | Open | Open |
| Context window | 128K | — |
| Max output | 128K | — |
| Input $ / M tokens | $0.58 | — |
| Output $ / M tokens | $1.68 | — |
| Results tracked | 3 | 18 |
Sponsored placements are available on pages like this one. Advertise on Noometry
Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 39.9 (#136), Qwen-14B: 30.6 (#283)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen-14B |
|---|---|---|
| WeirdML | 46.7% | — |
| LMArena Coding | — | 1072 |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.1 (#65), Qwen-14B: 18.7 (#255)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen-14B |
|---|---|---|
| SimpleBench | 52.6% | — |
| LMArena Hard Prompts | — | 1027 |
| BIG-Bench Hard | — | 55% |
| Epoch Capabilities Index | — | 112.8 |
| LAMBADA | — | 71.1% |
| PIQA | — | 79.9% |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Qwen-14B: 28.7 (#224)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen-14B |
|---|---|---|
| LMArena Math | — | 1068 |
| GSM8K | — | 61.3% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Qwen-14B: —
| Benchmark | DeepSeek-V3.2-Speciale | Qwen-14B |
|---|---|---|
| ARC (AI2) Challenge | — | 84.4% |
| BoolQ | — | 86.2% |
| MMLU | — | 66.3% |
Multilingual Not comparable
DeepSeek-V3.2-Speciale: —, Qwen-14B: 27.7 (#270)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen-14B |
|---|---|---|
| LMArena Non-English | — | 1042 |
| LMArena Chinese | — | 1077 |
Instruction Following Not comparable
DeepSeek-V3.2-Speciale: —, Qwen-14B: 52.6 (#284)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen-14B |
|---|---|---|
| LMArena Instruction Following | — | 1031 |
Long Context Not comparable
DeepSeek-V3.2-Speciale: —, Qwen-14B: 31.3 (#276)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen-14B |
|---|---|---|
| LMArena Longer Query | — | 1029 |
Writing & Preference DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 46.1 (#217), Qwen-14B: 27.8 (#294)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen-14B |
|---|---|---|
| LMArena Text | — | 1051 |
| LMArena Creative Writing | — | 1028 |
| EQ-Bench Creative Writing | 1276 | — |
| LMArena Multi-Turn | — | 1022 |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Qwen-14B?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.1 to 30.8 on the Noometry Index.
Is DeepSeek-V3.2-Speciale or Qwen-14B better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 39.9 versus 30.6 in the Noometry coding category.
How many benchmarks do DeepSeek-V3.2-Speciale and Qwen-14B share?
0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Qwen-14B has 18.