Model comparison
DeepSeek-V3.2-Speciale vs Qwen3.5-9B
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 33.8 on the Noometry Index. Qwen3.5-9B costs 7.6× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.
Last verified . 0 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- The widest gap is in reasoning, where DeepSeek-V3.2-Speciale leads 32.9 to 23.1.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.58 / $1.68 for DeepSeek-V3.2-Speciale.
- Qwen3.5-9B accepts more context: 262K tokens versus 128K.
Side by side
| DeepSeek-V3.2-Speciale | Qwen3.5-9B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 39.7 | 33.8 |
| Released | 2025-12-01 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 128K | 262K |
| Max output | 128K | 66K |
| Input $ / M tokens | $0.58 | $0.10 |
| Output $ / M tokens | $1.68 | $0.15 |
| Results tracked | 3 | 10 |
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Category by category
Coding DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 40.4 (#140), Qwen3.5-9B: 35.9 (#217)
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Speciale: —, Qwen3.5-9B: 14.5 (#151)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | — | 9.2% |
Reasoning DeepSeek-V3.2-Speciale leads
DeepSeek-V3.2-Speciale: 32.9 (#73), Qwen3.5-9B: 23.1 (#182)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen3.5-9B |
|---|---|---|
| SimpleBench | 52.6% | — |
| CritPt | — | 0.3% |
| Chess Puzzles | — | 12% |
| DTBench | — | 71.2% |
| LMCA | — | 24.5% |
| Epoch Capabilities Index | — | 139.46 |
Math Not comparable
DeepSeek-V3.2-Speciale: —, Qwen3.5-9B: 34.8 (#192)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen3.5-9B |
|---|---|---|
| MathArena Final-Answer Competitions | — | 48.5% |
| OTIS Mock AIME 2024-2025 | — | 61.7% |
Knowledge Not comparable
DeepSeek-V3.2-Speciale: —, Qwen3.5-9B: 46.0 (#84)
| Benchmark | DeepSeek-V3.2-Speciale | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | — | 79% |
Writing & Preference Not comparable
DeepSeek-V3.2-Speciale: 46.0 (#222), Qwen3.5-9B: —
| Benchmark | DeepSeek-V3.2-Speciale | Qwen3.5-9B |
|---|---|---|
| EQ-Bench Creative Writing | 1276 | — |
Frequently asked questions
Is DeepSeek-V3.2-Speciale better than Qwen3.5-9B?
DeepSeek-V3.2-Speciale is the stronger model overall, scoring 39.7 to 33.8 on the Noometry Index. Qwen3.5-9B costs 7.6× less per token, which makes it the better buy when DeepSeek-V3.2-Speciale's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Speciale or Qwen3.5-9B?
Qwen3.5-9B is cheaper. It lists at $0.10 per million input tokens and $0.15 per million output tokens; DeepSeek-V3.2-Speciale lists at $0.58 and $1.68.
Is DeepSeek-V3.2-Speciale or Qwen3.5-9B better for coding?
DeepSeek-V3.2-Speciale scores higher on coding benchmarks: 40.4 versus 35.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-9B does, with 262K tokens against 128K.
How many benchmarks do DeepSeek-V3.2-Speciale and Qwen3.5-9B share?
0 benchmarks have published results for both models. DeepSeek-V3.2-Speciale has 3 scored results on Noometry and Qwen3.5-9B has 10.