Model comparison
DeepSeek-V3.2-Exp vs Qwen3.6 Plus
Qwen3.6 Plus is the stronger model overall, scoring 47.3 to 44.9 on the Noometry Index. DeepSeek-V3.2-Exp costs 3.9× less per token, which makes it the better buy when Qwen3.6 Plus's lead doesn't matter for your workload.
Last verified . 32 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 32 benchmarks with published results for both. DeepSeek-V3.2-Exp scores higher in 3 categories and Qwen3.6 Plus in 5 categories; 6 gaps are clear of the uncertainty.
- The widest gap is in math, where Qwen3.6 Plus leads 50.6 to 40.8.
- The biggest single-benchmark swing is NYT Connections (extended): 36.7% for DeepSeek-V3.2-Exp and 60.3% for Qwen3.6 Plus.
- DeepSeek-V3.2-Exp is cheaper at $0.26 / $0.38 per million input/output tokens, against $0.50 / $3 for Qwen3.6 Plus.
- Qwen3.6 Plus accepts more context: 1M tokens versus 164K.
- DeepSeek-V3.2-Exp has downloadable open weights; the other is API-only.
Side by side
| DeepSeek-V3.2-Exp | Qwen3.6 Plus | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 44.9 | 47.3 |
| Released | 2025-09-29 | 2026-03-31 |
| Weights | Open | Proprietary |
| Context window | 164K | 1M |
| Max output | 66K | 66K |
| Input $ / M tokens | $0.26 | $0.50 |
| Output $ / M tokens | $0.38 | $3 |
| Results tracked | 48 | 37 |
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Category by category
Coding DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 46.4 (#61), Qwen3.6 Plus: 40.6 (#125)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.6 Plus |
|---|---|---|
| LMArena WebDev | 1362 | 1461 |
| SciCode | 38.9% | 40.7% |
| LMArena Coding | 1454 | 1467 |
| SWE-bench Verified | — | 57.9% |
| SWE-bench Verified (bash only) | 70% | — |
| Aider Polyglot | 74.2% | — |
| SWE-bench Multilingual | 59% | — |
| WeirdML | 39.5% | — |
| ALE-Bench | — | 670.15 |
Agentic & Tool Use Not comparable
DeepSeek-V3.2-Exp: 37.9 (#31), Qwen3.6 Plus: —
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.6 Plus |
|---|---|---|
| Vending-Bench 2 | 1,034 | 5,115 |
| Terminal-Bench | 39.6% | — |
| Berkeley Function Calling Leaderboard | 56.7% | — |
| TheAgentCompany | 42.9% | — |
Reasoning Qwen3.6 Plus leads
DeepSeek-V3.2-Exp: 22.0 (#185), Qwen3.6 Plus: 28.5 (#89)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.6 Plus |
|---|---|---|
| NYT Connections (extended) | 36.7% | 60.3% |
| CritPt | 2.9% | 2.9% |
| Chess Puzzles | 14% | 17% |
| Thematic Generalization | 65% | 59.5% |
| LMArena Hard Prompts | 1433 | 1449 |
| DTBench | 87.7% | 81.9% |
| LMCA | 29.1% | 33.1% |
| Epoch Capabilities Index | 146.28 | 147.67 |
| ARC-AGI-2 | 4% | — |
| Kagi LLM Benchmark | 52.2% | — |
| ARC-AGI-1 | 57% | — |
| Mystery Game Puzzles | — | 12% |
Math Qwen3.6 Plus leads
DeepSeek-V3.2-Exp: 40.8 (#75), Qwen3.6 Plus: 50.6 (#44)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.6 Plus |
|---|---|---|
| OTIS Mock AIME 2024-2025 | 87.8% | 93.3% |
| LMArena Math | 1435 | 1450 |
| FrontierMath (Feb 2025 set) | 22.1% | 26.2% |
| FrontierMath Tier 4 (v1) | 2.1% | 8.3% |
| FrontierMath (Tiers 1-3) | — | 38.2% |
| MathArena Final-Answer Competitions | 57.7% | — |
| ProofBench | 8% | — |
Knowledge Qwen3.6 Plus leads
DeepSeek-V3.2-Exp: 51.6 (#61), Qwen3.6 Plus: 56.1 (#40)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.6 Plus |
|---|---|---|
| GPQA Diamond | 83.4% | 88.4% |
| LMArena Expert | 1436 | 1456 |
| SimpleQA Verified | — | 44.1% |
| Vectara Hallucination Rate | 5.3% | — |
Multilingual Qwen3.6 Plus leads
DeepSeek-V3.2-Exp: 52.4 (#85), Qwen3.6 Plus: 53.5 (#66)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.6 Plus |
|---|---|---|
| LMArena Non-English | 1409 | 1424 |
| LMArena Chinese | 1461 | 1478 |
| LMArena French | 1435 | 1454 |
| LMArena German | 1440 | 1451 |
| LMArena Japanese | 1377 | 1389 |
| LMArena Korean | 1372 | 1381 |
| LMArena Russian | 1423 | 1434 |
| LMArena Spanish | 1441 | 1432 |
Instruction Following Too close to call
DeepSeek-V3.2-Exp: 74.7 (#89), Qwen3.6 Plus: 75.2 (#70)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.6 Plus |
|---|---|---|
| LMArena Instruction Following | 1413 | 1425 |
Long Context DeepSeek-V3.2-Exp leads
DeepSeek-V3.2-Exp: 47.6 (#15), Qwen3.6 Plus: 45.1 (#48)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.6 Plus |
|---|---|---|
| CL-bench | 13.2% | 20.3% |
| LMArena Longer Query | 1428 | 1440 |
| Fiction.LiveBench | 83.3% | — |
| CL-bench Life | 9.5% | — |
Writing & Preference Too close to call
DeepSeek-V3.2-Exp: 62.5 (#73), Qwen3.6 Plus: 62.4 (#78)
| Benchmark | DeepSeek-V3.2-Exp | Qwen3.6 Plus |
|---|---|---|
| LMArena Text | 1425 | 1437 |
| LMArena Creative Writing | 1403 | 1405 |
| LMArena Multi-Turn | 1427 | 1438 |
| EQ-Bench Creative Writing | 1515 | — |
Frequently asked questions
Is DeepSeek-V3.2-Exp better than Qwen3.6 Plus?
Qwen3.6 Plus is the stronger model overall, scoring 47.3 to 44.9 on the Noometry Index. DeepSeek-V3.2-Exp costs 3.9× less per token, which makes it the better buy when Qwen3.6 Plus's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.2-Exp or Qwen3.6 Plus?
DeepSeek-V3.2-Exp is cheaper. It lists at $0.26 per million input tokens and $0.38 per million output tokens; Qwen3.6 Plus lists at $0.50 and $3.
Is DeepSeek-V3.2-Exp or Qwen3.6 Plus better for coding?
DeepSeek-V3.2-Exp scores higher on coding benchmarks: 46.4 versus 40.6 in the Noometry coding category.
Which has the bigger context window?
Qwen3.6 Plus does, with 1M tokens against 164K.
How many benchmarks do DeepSeek-V3.2-Exp and Qwen3.6 Plus share?
32 benchmarks have published results for both models. DeepSeek-V3.2-Exp has 48 scored results on Noometry and Qwen3.6 Plus has 37.