Model comparison
DeepSeek V4 Pro vs Qwen3.5-27B
Head-to-head evidence from 5 shared benchmark results across 3 categories. Overall scores shown here use the public BenchAlign v5 ranking lane.
Public leaderboard positions: DeepSeek V4 Pro #46 (Supported); Qwen3.5-27B #45 (Supported). Intervals and evidence labels describe ranking uncertainty, not a guarantee for a specific workload.
Evidence parity. DeepSeek V4 Pro and Qwen3.5-27B share 5 comparable benchmark results. 3 of 8 categories are comparable. 18 results are unique to DeepSeek V4 Pro; 23 to Qwen3.5-27B.
Updated July 23, 2026- Shared results
- 5
- DeepSeek V4 Pro only
- 18
- Qwen3.5-27B only
- 23
- Comparable categories
- 3 / 8
Pick Qwen3.5-27B if you want the stronger benchmark profile. DeepSeek V4 Pro only becomes the better choice if agentic is the priority or you need the larger 1M context window.
Confidence note. This is a partial-evidence comparison with 5 shared benchmark results across 3 evidence categories; 3 of 8 categories currently have scoreable aggregates for both models. Treat the verdict as directional until coverage is more balanced.
Why this result
Qwen3.5-27B has the cleaner BenchAlign overall profile here, landing at 60.7 versus 60.66. It is a real lead, but still close enough that category-level strengths matter more than the headline number.
Qwen3.5-27B's sharpest advantage is in knowledge, where it averages 82.7 against 41.3. The single biggest benchmark swing on the page is Terminal-Bench 2.0, 59.1% to 41.6%. DeepSeek V4 Pro does hit back in agentic, so the answer changes if that is the part of the workload you care about most.
DeepSeek V4 Pro is also the more expensive model on tokens at $0.43 input / $0.87 output per 1M tokens, versus $0.00 input / $0.00 output per 1M tokens for Qwen3.5-27B. That is roughly Infinityx on output cost alone. Qwen3.5-27B is the reasoning model in the pair, while DeepSeek V4 Pro 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 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 | Δ | Qwen3.5-27B |
|---|---|---|---|
| Knowledge | DeepSeek V4 Pro41.3 | Margin→ 41.4 | Qwen3.5-27B82.7 |
| Agentic | DeepSeek V4 Pro59.1 | Margin← 7.1 | Qwen3.5-27B52.0 |
| Coding | DeepSeek V4 Pro65.3 | Margin← 0.4 | Qwen3.5-27B64.9 |
| Reasoning | DeepSeek V4 ProNot measured | MarginNo overlap | Qwen3.5-27B60.6 |
| Math | DeepSeek V4 Pro31.7 | MarginNo overlap | Qwen3.5-27BNot measured |
| Multilingual | DeepSeek V4 ProNot measured | MarginNo overlap | Qwen3.5-27B82.2 |
| Inst. Following | DeepSeek V4 ProNot measured | MarginNo overlap | Qwen3.5-27B95.0 |
Decisive benchmark drivers
The largest measured benchmark gaps in this matchup, with exact reported values.
More
- Source ↗
Terminal-Bench 2.0
AgenticA 59.1%B 41.6%Winner: DeepSeek V4 ProΔ 17.5Terminal-Bench 2.0: DeepSeek V4 Pro scored 59.1%; Qwen3.5-27B scored 41.6%. DeepSeek V4 Pro wins this benchmark. - Source ↗
GPQA
KnowledgeA 72.9%B 85.5%Winner: Qwen3.5-27BΔ 12.6GPQA: DeepSeek V4 Pro scored 72.9%; Qwen3.5-27B scored 85.5%. Qwen3.5-27B wins this benchmark. - Source ↗
MMLU-Pro
KnowledgeA 82.9%B 86.1%Winner: Qwen3.5-27BΔ 3.2MMLU-Pro: DeepSeek V4 Pro scored 82.9%; Qwen3.5-27B scored 86.1%. Qwen3.5-27B wins this benchmark. - Source ↗
SWE-bench Verified
CodingA 73.6%B 72.4%Winner: DeepSeek V4 ProΔ 1.2SWE-bench Verified: DeepSeek V4 Pro scored 73.6%; Qwen3.5-27B scored 72.4%. DeepSeek V4 Pro wins this benchmark.
Operational comparison
Runtime and commercial metrics are compared only when both models have a complete sourced value.
| Metric | DeepSeek V4 Pro | Qwen3.5-27B | Comparison |
|---|---|---|---|
| Input / output priceUSD per 1M tokens | DeepSeek V4 Pro$0.435 input / $0.87 output | Qwen3.5-27B$0 input / $0 output | Qwen3.5-27B has the lower combined listed price. |
| Generation speedtokens per second | DeepSeek V4 ProNot available | Qwen3.5-27BNot available | A complete speed comparison is not available. |
| First-answer latencyseconds to first token | DeepSeek V4 ProNot available | Qwen3.5-27BNot available | A complete latency comparison is not available. |
| Context windowmaximum listed tokens | DeepSeek V4 Pro1M | Qwen3.5-27B262K | DeepSeek V4 Pro lists the larger context window. |
Benchmark Deep Dive
AgenticDeepSeek V4 Pro wins9 benchmarks
| Benchmark | DeepSeek V4 Pro | Qwen3.5-27B | Result |
|---|---|---|---|
| Terminal-Bench 2.0Source | 59.1% | 41.6% | DeepSeek V4 Pro leads |
| MCP AtlasSource | 69.4% | — | Not comparable |
| ToolathlonSource | 46.3% | — | Not comparable |
| Claw-EvalSource | 59.8% | — | Not comparable |
| Gert LabsSource | 50.28% | 39.41% | DeepSeek V4 Pro leads |
| ResearchClawBenchSource | 17.1% | — | Not comparable |
| BrowseCompSource | — | 61% | Not comparable |
| OSWorld-VerifiedSource | — | 56.2% | Not comparable |
| τ²-bench resultsSource | — | 93.9% | Not comparable |
CodingDeepSeek V4 Pro wins6 benchmarks
| Benchmark | DeepSeek V4 Pro | Qwen3.5-27B | Result |
|---|---|---|---|
| SWE-bench VerifiedSource | 73.6% | 72.4% | DeepSeek V4 Pro leads |
| SWE-bench ProSource | 52.1% | — | Not comparable |
| SWE MultilingualSource | 69.8% | — | Not comparable |
| Terminal-Bench 2.0Source | 59.1% | — | Not comparable |
| SWE-RebenchSource | — | 58.9% | Not comparable |
| AA-SciCodeSource | — | 39.5% | Not comparable |
Reasoning5 benchmarks
KnowledgeQwen3.5-27B wins13 benchmarks
| Benchmark | DeepSeek V4 Pro | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ProSource | 82.9% | 86.1% | Qwen3.5-27B leads |
| SimpleQASource | 45% | — | Not comparable |
| Chinese-SimpleQASource | 75.8% | — | Not comparable |
| GPQASource | 72.9% | 85.5% | Qwen3.5-27B leads |
| GPQA-DSource | 72.9% | — | Not comparable |
| HLESource | 7.7% | — | Not comparable |
| SuperGPQASource | — | 65.6% | 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 |
Math4 benchmarks
Multilingual1 benchmarks
| Benchmark | DeepSeek V4 Pro | Qwen3.5-27B | Result |
|---|---|---|---|
| MMLU-ProXSource | — | 82.2% | Not comparable |
Multimodal6 benchmarks
Frequently Asked Questions (4)
Which is better, DeepSeek V4 Pro or Qwen3.5-27B?
Qwen3.5-27B is ahead on BenchLM's BenchAlign leaderboard, 60.7 to 60.66. The biggest single separator in this matchup is Terminal-Bench 2.0, where the scores are 59.1% and 41.6%.
Which is better for knowledge tasks, DeepSeek V4 Pro or Qwen3.5-27B?
Qwen3.5-27B has the edge for knowledge tasks in this comparison, averaging 82.7 versus 41.3. Inside this category, GPQA is the benchmark that creates the most daylight between them.
Which is better for coding, DeepSeek V4 Pro or Qwen3.5-27B?
DeepSeek V4 Pro has the edge for coding in this comparison, averaging 65.3 versus 64.9. Inside this category, SWE-bench Verified is the benchmark that creates the most daylight between them.
Which is better for agentic tasks, DeepSeek V4 Pro or Qwen3.5-27B?
DeepSeek V4 Pro has the edge for agentic tasks in this comparison, averaging 59.1 versus 52. Inside this category, Terminal-Bench 2.0 is the benchmark that creates the most daylight between them.
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