Model comparison
DeepSeek V4 Pro Base vs Qwen3.5-27B
Head-to-head evidence from 3 shared benchmark results across 2 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro Base unranked (Not scored); Qwen3.5-27B #45 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Pro Base and Qwen3.5-27B share 3 comparable benchmark results. 2 of 8 categories are comparable. 21 results are unique to DeepSeek V4 Pro Base; 25 to Qwen3.5-27B.
Updated July 23, 2026- Shared results
- 3
- DeepSeek V4 Pro Base only
- 21
- Qwen3.5-27B only
- 25
- Comparable categories
- 2 / 8
Treat this as a split decision. DeepSeek V4 Pro Base makes more sense if you need the larger 1M context window or you would rather avoid the extra latency and token burn of a reasoning model; Qwen3.5-27B is the better fit if knowledge is the priority or you want the stronger reasoning-first profile.
Confidence note. This is a partial-evidence comparison with 3 shared benchmark results across 2 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 V4 Pro Base and Qwen3.5-27B finish on the same BenchAlign overall score, so this is less about a single winner and more about where the edge shows up. The BenchAlign headline says tie; the benchmark table is where the real choice happens.
Qwen3.5-27B is the reasoning model in the pair, while DeepSeek V4 Pro Base 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. DeepSeek V4 Pro Base gives you the larger context window at 1M, compared with 262K for Qwen3.5-27B.
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 V4 Pro Base | Δ | Qwen3.5-27B |
|---|---|---|---|
| Knowledge | DeepSeek V4 Pro Base66.4 | Margin→ 16.3 | Qwen3.5-27B82.7 |
| Reasoning | DeepSeek V4 Pro Base51.5 | Margin→ 9.1 | Qwen3.5-27B60.6 |
| Agentic | DeepSeek V4 Pro BaseNot measured | MarginNo overlap | Qwen3.5-27B52.0 |
| Coding | DeepSeek V4 Pro BaseNot measured | MarginNo overlap | Qwen3.5-27B64.9 |
| Multilingual | DeepSeek V4 Pro BaseNot measured | MarginNo overlap | Qwen3.5-27B82.2 |
| Inst. Following | DeepSeek V4 Pro BaseNot measured | MarginNo overlap | Qwen3.5-27B95.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
MMLU-Pro
KnowledgeA 73.5%B 86.1%Winner: Qwen3.5-27BΔ 12.6MMLU-Pro: DeepSeek V4 Pro Base scored 73.5%; Qwen3.5-27B scored 86.1%. Qwen3.5-27B wins this benchmark. - Source ↗
SuperGPQA
KnowledgeA 53.9%B 65.6%Winner: Qwen3.5-27BΔ 11.7SuperGPQA: DeepSeek V4 Pro Base scored 53.9%; Qwen3.5-27B scored 65.6%. Qwen3.5-27B wins this benchmark. - Source ↗
LongBench v2
ReasoningA 51.5%B 60.6%Winner: Qwen3.5-27BΔ 9.1LongBench v2: DeepSeek V4 Pro Base scored 51.5%; Qwen3.5-27B scored 60.6%. Qwen3.5-27B wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Pro Base | Qwen3.5-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro BaseNot available | Qwen3.5-27B$0 input / $0 output | A complete price comparison is not available. |
| Generation speedtokens per second | DeepSeek V4 Pro BaseNot available | Qwen3.5-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 Pro BaseNot available | Qwen3.5-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro Base1M | Qwen3.5-27B262K | DeepSeek V4 Pro Base lists the larger context window. |
Benchmark Deep Dive
Agentic5 benchmarks
Coding5 benchmarks
ReasoningQwen3.5-27B wins8 benchmarks
| Benchmark | DeepSeek V4 Pro Base | Qwen3.5-27B | Result |
|---|---|---|---|
| BBHSource | 87.5% | — | Not comparable |
| DROPSource | 88.7% | — | Not comparable |
| HellaSwagSource | 88.0% | — | Not comparable |
| WinoGrandeSource | 81.5% | — | Not comparable |
| CLUEWSCSource | 85.2% | — | Not comparable |
| LongBench v2Source | 51.5% | 60.6% | Qwen3.5-27B leads |
| AA-LCRSource | — | 67.3% | Not comparable |
| CritPtSource | — | 0.9% | Not comparable |
KnowledgeQwen3.5-27B wins19 benchmarks
| Benchmark | DeepSeek V4 Pro Base | Qwen3.5-27B | Result |
|---|---|---|---|
| AGIEvalSource | 83.1% | — | Not comparable |
| MMLUSource | 90.1% | — | Not comparable |
| MMLU-ReduxSource | 90.8% | — | Not comparable |
| MMLU-ProSource | 73.5% | 86.1% | Qwen3.5-27B leads |
| MMMLUSource | 90.3% | — | Not comparable |
| C-EvalSource | 93.1% | — | Not comparable |
| CMMLUSource | 90.8% | — | Not comparable |
| MultiLoKoSource | 51.1% | — | Not comparable |
| SimpleQASource | 55.2% | — | Not comparable |
| SuperGPQASource | 53.9% | 65.6% | Qwen3.5-27B leads |
| FACTS ParametricSource | 62.6% | — | Not comparable |
| TriviaQASource | 85.6% | — | Not comparable |
| GPQASource | — | 85.5% | Not comparable |
| Artificial Analysis Intelligence IndexSource | — | 33.8% | Not comparable |
| AA-GPQA DiamondSource | — | 85.8% | Not comparable |
| AA-HLESource | — | 22.2% | Not comparable |
| AA-Omniscience IndexSource | — | -42.0% | Not comparable |
| AA-Omniscience AccuracySource | — | 21.0% | Not comparable |
| AA-Omniscience Hallucination RateSource | — | 79.7% | Not comparable |
Math3 benchmarks
Multilingual2 benchmarks
Multimodal5 benchmarks
Frequently Asked Questions (3)
Which is better, DeepSeek V4 Pro Base or Qwen3.5-27B?
DeepSeek V4 Pro Base and Qwen3.5-27B are tied on the BenchAlign overall score, so the right pick depends on which category matters most for your use case.
Which is better for knowledge tasks, DeepSeek V4 Pro Base or Qwen3.5-27B?
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 82.7 versus 66.4. Inside this category, MMLU-Pro is the benchmark that creates the most daylight between them.
Which is better for reasoning, DeepSeek V4 Pro Base or Qwen3.5-27B?
Qwen3.5-27B has the edge for reasoning in this comparison, averaging 60.6 versus 51.5. Inside this category, LongBench v2 is the benchmark that creates the most daylight between them.
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