Model comparison
DeepSeek-V3.2-Exp vs Qwen3.5-9B
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 33.8 on the Noometry Index. Qwen3.5-9B costs 2.6× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.
Last verified . 10 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 10 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 4 categories and Qwen3.5-9B in 1 category; 5 gaps are clear of the uncertainty.
- The widest gap is in agentic & tool use, where DeepSeek-V3.2-Exp leads 32.7 to 14.5.
- The biggest single-benchmark swing is Terminal-Bench: 39.6% for DeepSeek-V3.2-Exp and 9.2% for Qwen3.5-9B.
- Qwen3.5-9B is cheaper at $0.10 / $0.15 per million input/output tokens, against $0.26 / $0.38 for DeepSeek-V3.2-Exp.
- Qwen3.5-9B accepts more context: 262K tokens versus 164K.
Side by side
| DeepSeek-V3.2-Exp | Qwen3.5-9B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 44.3 | 33.8 |
| Released | 2025-09-29 | 2026-02-23 |
| Weights | Open | Open |
| Context window | 164K | 262K |
| Max output | 66K | 66K |
| Input $ / M tokens | $0.26 | $0.10 |
| Output $ / M tokens | $0.38 | $0.15 |
| Results tracked | 49 | 10 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.5 (#65), Qwen3.5-9B: 35.9 (#217)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.5-9B |
|---|---|---|
| SciCode | 38.9% | 27.5% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| LMArena WebDev | 1362 | — |
| SWE-bench Multilingual | 59% | — |
| WeirdML | 39.5% | — |
| LMArena Coding | 1454 | — |
Agentic & Tool Use DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 32.7 (#59), Qwen3.5-9B: 14.5 (#151)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.5-9B |
|---|---|---|
| Terminal-Bench | 39.6% | 9.2% |
| APEX-Agents | 21.3% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
| Vending-Bench 2 | 1,034 | — |
Reasoning Qwen3.5-9B leads
DeepSeek-V3.2-Exp: 22.1 (#208), Qwen3.5-9B: 23.1 (#182)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.5-9B |
|---|---|---|
| CritPt | 2.9% | 0.3% |
| Chess Puzzles | 14% | 12% |
| DTBench | 87.7% | 71.2% |
| LMCA | 29.1% | 24.5% |
| Epoch Capabilities Index | 146.27 | 139.46 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| NYT Connections (extended) | 36.7% | — |
| ARC-AGI-1 | 57% | — |
| Thematic Generalization | 65% | — |
| LMArena Hard Prompts | 1434 | — |
Math DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 41.7 (#87), Qwen3.5-9B: 34.8 (#192)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.5-9B |
|---|---|---|
| MathArena Final-Answer Competitions | 57.7% | 48.5% |
| OTIS Mock AIME 2024-2025 | 87.8% | 61.7% |
| ProofBench | 8% | — |
| LMArena Math | 1435 | — |
| FrontierMath (Feb 2025 set) | 22.1% | — |
| FrontierMath Tier 4 (v1) | 2.1% | — |
Knowledge DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 51.7 (#66), Qwen3.5-9B: 46.0 (#84)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.5-9B |
|---|---|---|
| GPQA Diamond | 83.4% | 79% |
| Vectara Hallucination Rate | 5.3% | — |
| LMArena Expert | 1436 | — |
Multilingual Not comparable
DeepSeek-V3.2-Exp: 52.2 (#90), Qwen3.5-9B: —
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.5-9B |
|---|---|---|
| LMArena Non-English | 1409 | — |
| LMArena Chinese | 1461 | — |
| LMArena French | 1433 | — |
| LMArena German | 1440 | — |
| LMArena Japanese | 1374 | — |
| LMArena Korean | 1371 | — |
| LMArena Russian | 1424 | — |
| LMArena Spanish | 1440 | — |
Instruction Following Not comparable
DeepSeek-V3.2-Exp: 74.5 (#93), Qwen3.5-9B: —
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.5-9B |
|---|---|---|
| LMArena Instruction Following | 1413 | — |
Long Context Not comparable
DeepSeek-V3.2-Exp: 47.6 (#16), Qwen3.5-9B: —
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.5-9B |
|---|---|---|
| Fiction.LiveBench | 83.3% | — |
| CL-bench | 13.2% | — |
| CL-bench Life | 9.5% | — |
| LMArena Longer Query | 1428 | — |
Writing & Preference Not comparable
DeepSeek-V3.2-Exp: 62.4 (#77), Qwen3.5-9B: —
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.5-9B |
|---|---|---|
| LMArena Text | 1425 | — |
| LMArena Creative Writing | 1403 | — |
| EQ-Bench Creative Writing | 1515 | — |
| LMArena Multi-Turn | 1427 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Qwen3.5-9B?
DeepSeek-V3.2-Exp is the stronger model overall, scoring 44.3 to 33.8 on the Noometry Index. Qwen3.5-9B costs 2.6× less per token, which makes it the better buy when DeepSeek-V3.2-Exp's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Exp 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-Exp lists at $0.26 and $0.38.
Is DeepSeek-V3.2-Exp or Qwen3.5-9B better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.5 versus 35.9 in the Noometry coding category.
Which has the bigger context window?
Qwen3.5-9B does, with 262K tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and Qwen3.5-9B share?
10 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 49 scored results on Noometry and Qwen3.5-9B has 10.