Model comparison
DeepSeek V3.2 vs Qwen3.6-27B
Head-to-head evidence from 13 shared benchmark results across 5 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V3.2 #82 (Supported); Qwen3.6-27B #93 (Estimated). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V3.2 and Qwen3.6-27B share 13 comparable benchmark results. 2 of 8 categories are comparable. 6 results are unique to DeepSeek V3.2; 41 to Qwen3.6-27B.
Updated July 21, 2026- Shared results
- 13
- DeepSeek V3.2 only
- 6
- Qwen3.6-27B only
- 41
- Comparable categories
- 2 / 8
Pick DeepSeek V3.2 if you want the stronger benchmark profile. Qwen3.6-27B only becomes the better choice if mathematics is the priority or you want the cheaper token bill.
Confidence note. This is a partial-evidence comparison with 13 shared benchmark results across 5 evidence categories; 2 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
DeepSeek V3.2 has the cleaner BenchAlign overall profile here, landing at 55.4 versus 53.82. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
DeepSeek V3.2 is also the more expensive model on tokens at $0.28 input / $0.42 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.6-27B. That is roughly Infinityx on output cost alone. Qwen3.6-27B is the reasoning model in the pair, while DeepSeek V3.2 is not. That usually helps on harder chain-of-thought-heavy tests, but it can also mean more latency and more token spend in real use. Qwen3.6-27B gives you the larger context window at 262K, compared with 128K for DeepSeek V3.2.
Category breakdown
Exact category averages are shown below. Not measured means BenchLM does not have enough sourced public coverage for that model and category.
| Category | DeepSeek V3.2 | Δ | Qwen3.6-27B |
|---|---|---|---|
| Math | DeepSeek V3.217.1 | Margin→ 72.1 | Qwen3.6-27B89.2 |
| Coding | DeepSeek V3.260.9 | Margin→ 16.6 | Qwen3.6-27B77.5 |
| Agentic | DeepSeek V3.2Not measured | MarginNo overlap | Qwen3.6-27B59.3 |
| Knowledge | DeepSeek V3.2Not measured | MarginNo overlap | Qwen3.6-27B53.3 |
| Multimodal | DeepSeek V3.2Not measured | MarginNo overlap | Qwen3.6-27B76.7 |
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V3.2 | Qwen3.6-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V3.2$0.28 input / $0.42 output | Qwen3.6-27B$0 input / $0 output | Qwen3.6-27B has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V3.235 tok/s | Qwen3.6-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V3.23.75 s | Qwen3.6-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V3.2128K | Qwen3.6-27B262K | Qwen3.6-27B lists the larger context window. |
Benchmark Deep Dive
Agentic11 benchmarks
| Benchmark | DeepSeek V3.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| Claw-EvalSource | 40.2% | 72.4% | Qwen3.6-27B leads |
| VITA-BenchSource | 18.5% | — | Not comparable |
| τ²-bench resultsSource | 78.9% | 94.2% | Qwen3.6-27B leads |
| Gert LabsSource | 29.57% | 54.84% | Qwen3.6-27B leads |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| QwenClawBenchSource | — | 53.4% | Not comparable |
| QwenWebBenchSource | — | 1487 | Not comparable |
| AndroidWorldSource | — | 70.3% | Not comparable |
| AA Agentic IndexSource | — | 27.0% | Not comparable |
| GDPval-AASource | — | 32.0% | Not comparable |
| GDPval-AASource | — | 1140 | Not comparable |
CodingQwen3.6-27B wins10 benchmarks
| Benchmark | DeepSeek V3.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| SWE-RebenchSource | 60.9% | — | Not comparable |
| React Native EvalsSource | 71.5% | — | Not comparable |
| AA-SciCodeSource | 38.7% | 39.8% | Qwen3.6-27B leads |
| SWE-bench VerifiedSource | — | 77.2% | Not comparable |
| SWE MultilingualSource | — | 71.3% | Not comparable |
| SWE-bench ProSource | — | 53.5% | Not comparable |
| Terminal-Bench 2.0Source | — | 59.3% | Not comparable |
| LiveCodeBenchSource | — | 83.9% | Not comparable |
| NL2RepoSource | — | 36.2% | Not comparable |
| AA Coding IndexSource | — | 53.7% | Not comparable |
Reasoning2 benchmarks
Knowledge12 benchmarks
| Benchmark | DeepSeek V3.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| Artificial Analysis Intelligence IndexSource | 24.7% | 37.0% | Qwen3.6-27B leads |
| AA-GPQA DiamondSource | 75.1% | 84.2% | Qwen3.6-27B leads |
| AA-HLESource | 10.5% | 21.6% | Qwen3.6-27B leads |
| AA-Omniscience IndexSource | -46.7% | -19.8% | Qwen3.6-27B leads |
| AA-Omniscience AccuracySource | 24.2% | 19.2% | DeepSeek V3.2 leads |
| AA-Omniscience Hallucination RateSource | 93.5% | 48.3% | Qwen3.6-27B leads |
| MMLU-ProSource | — | 86.2% | Not comparable |
| MMLU-ReduxSource | — | 93.5% | Not comparable |
| SuperGPQASource | — | 66% | Not comparable |
| C-EvalSource | — | 91.4% | Not comparable |
| GPQASource | — | 87.8% | Not comparable |
| HLESource | — | 24% | Not comparable |
MathQwen3.6-27B wins7 benchmarks
| Benchmark | DeepSeek V3.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| FrontierMath v2 (Tiers 1-3)Source | 22.100% | — | Not comparable |
| FrontierMath v2 (Tier 4)Source | 2.100% | — | Not comparable |
| HMMT Feb 2025Source | — | 93.8% | Not comparable |
| HMMT Nov 2025Source | — | 90.7% | Not comparable |
| HMMT Feb 2026Source | — | 84.3% | Not comparable |
| MMAnswerBenchSource | — | 80.8% | Not comparable |
| AIME26Source | — | 94.1% | Not comparable |
Multimodal17 benchmarks
| Benchmark | DeepSeek V3.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| Design Arena WebsiteSource | 1204 | — | Not comparable |
| MMMUSource | — | 82.9% | Not comparable |
| MMMU-ProSource | — | 75.8% | Not comparable |
| RealWorldQASource | — | 84.1% | Not comparable |
| DynaMathSource | — | 85.6% | Not comparable |
| MStarSource | — | 81.4% | Not comparable |
| SimpleVQASource | — | 56.1% | Not comparable |
| CharXivSource | — | 78.4% | Not comparable |
| CC-OCRSource | — | 81.2% | Not comparable |
| CountBenchSource | — | 97.8% | Not comparable |
| RefCOCO (avg)Source | — | 92.5% | Not comparable |
| ERQASource | — | 62.5% | Not comparable |
| Video-MME (with subtitle)Source | — | 87.7% | Not comparable |
| VideoMMMUSource | — | 84.4% | Not comparable |
| MLVU (M-Avg)Source | — | 86.6% | Not comparable |
| V*Source | — | 94.7% | Not comparable |
| AA-MMMU-ProSource | — | 74.6% | Not comparable |
Inst. Following1 benchmarks
| Benchmark | DeepSeek V3.2 | Qwen3.6-27B | Result |
|---|---|---|---|
| AA-IFBenchSource | 49.0% | 67.6% | Qwen3.6-27B leads |
Frequently Asked Questions (3)
Which is better, DeepSeek V3.2 or Qwen3.6-27B?
DeepSeek V3.2 is ahead on BenchLM's BenchAlign leaderboard, 55.4 to 53.82.
Which is better for coding, DeepSeek V3.2 or Qwen3.6-27B?
Qwen3.6-27B has the edge for coding in this comparison, averaging 77.5 versus 60.9. Inside this category, AA-SciCode is the benchmark that creates the most daylight between them.
Which is better for math, DeepSeek V3.2 or Qwen3.6-27B?
Qwen3.6-27B has the edge for math in this comparison, averaging 89.2 versus 17.1. DeepSeek V3.2 stays close enough that the answer can still flip depending on your workload.
Self-host vs API cost
Estimates at 50,000 req/day · 1000 tokens/req average.
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