Model comparison
DeepSeek-V3.1 vs Qwen3.8 27B
Qwen3.8 27B is the stronger model overall, scoring 45.3 to 42.2 on the Noometry Index. DeepSeek-V3.1 costs 2.6× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Last verified . 21 shared benchmarks.
DeepSeek
Alibaba (Qwen)Summary
- They share 21 benchmarks with published results for both. DeepSeek-V3.1 scores higher in 2 categories and Qwen3.8 27B in 6 categories; 7 gaps are clear of the uncertainty.
- The widest gap is in coding, where Qwen3.8 27B leads 50.3 to 39.8.
- The biggest single-benchmark swing is LMCA: 24.3% for DeepSeek-V3.1 and 41.4% for Qwen3.8 27B.
- DeepSeek-V3.1 is cheaper at $0.25 / $0.95 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
- Qwen3.8 27B accepts more context: 262K tokens versus 164K.
Side by side
| DeepSeek-V3.1 | Qwen3.8 27B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 42.2 | 45.3 |
| Released | 2025-08-21 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 164K | 262K |
| Max output | 8K | 33K |
| Input $ / M tokens | $0.25 | $0.99 |
| Output $ / M tokens | $0.95 | $1.49 |
| Results tracked | 27 | 28 |
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Category by category
Coding Qwen3.8 27B leads
DeepSeek-V3.1: 39.8 (#140), Qwen3.8 27B: 50.3 (#42)
| Benchmark | DeepSeek-V3.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Coding | 1417 | 1487 |
| LMArena WebDev | — | 1592 |
| SciCode | — | 46.6% |
| WeirdML | 38.4% | — |
Reasoning Qwen3.8 27B leads
DeepSeek-V3.1: 27.3 (#109), Qwen3.8 27B: 31.5 (#71)
| Benchmark | DeepSeek-V3.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Hard Prompts | 1417 | 1459 |
| DTBench | 82.7% | 88% |
| LMCA | 24.3% | 41.4% |
| Epoch Capabilities Index | 139.92 | 149.38 |
| SimpleBench | 40% | — |
| Kagi LLM Benchmark | 53.2% | — |
| NYT Connections (extended) | — | 54.5% |
| CritPt | — | 5.4% |
| Surface Evolver Bench | — | 45% |
| ForecastBench | 58 | — |
Math Too close to call
DeepSeek-V3.1: 36.1 (#125), Qwen3.8 27B: 35.7 (#139)
| Benchmark | DeepSeek-V3.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Math | 1420 | 1453 |
| ProofBench | — | 16% |
Knowledge DeepSeek-V3.1 leads
DeepSeek-V3.1: 43.0 (#86), Qwen3.8 27B: 40.9 (#105)
| Benchmark | DeepSeek-V3.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1403 | 1482 |
| Vectara Hallucination Rate | 5.5% | — |
Multimodal Not comparable
DeepSeek-V3.1: —, Qwen3.8 27B: 40.5 (#36)
| Benchmark | DeepSeek-V3.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | — | 1270 |
Multilingual Qwen3.8 27B leads
DeepSeek-V3.1: 51.8 (#100), Qwen3.8 27B: 54.0 (#54)
| Benchmark | DeepSeek-V3.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1400 | 1431 |
| LMArena Chinese | 1470 | 1504 |
| LMArena French | 1447 | 1468 |
| LMArena German | 1412 | 1435 |
| LMArena Japanese | 1374 | 1392 |
| LMArena Korean | 1337 | 1389 |
| LMArena Russian | 1405 | 1421 |
| LMArena Spanish | 1429 | 1444 |
Instruction Following Qwen3.8 27B leads
DeepSeek-V3.1: 74.1 (#105), Qwen3.8 27B: 75.8 (#54)
| Benchmark | DeepSeek-V3.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1400 | 1436 |
Long Context Qwen3.8 27B leads
DeepSeek-V3.1: 36.3 (#227), Qwen3.8 27B: 44.3 (#66)
| Benchmark | DeepSeek-V3.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1422 | 1448 |
| Fiction.LiveBench | 52.8% | — |
Writing & Preference Qwen3.8 27B leads
DeepSeek-V3.1: 60.5 (#93), Qwen3.8 27B: 65.9 (#41)
| Benchmark | DeepSeek-V3.1 | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1420 | 1441 |
| LMArena Creative Writing | 1401 | 1382 |
| EQ-Bench Creative Writing | 1436 | 1671 |
| LMArena Multi-Turn | 1408 | 1443 |
Frequently asked questions
Is DeepSeek-V3.1 better than Qwen3.8 27B?
Qwen3.8 27B is the stronger model overall, scoring 45.3 to 42.2 on the Noometry Index. DeepSeek-V3.1 costs 2.6× less per token, which makes it the better buy when Qwen3.8 27B's lead doesn't matter for your workload.
Which is cheaper, DeepSeek-V3.1 or Qwen3.8 27B?
DeepSeek-V3.1 is cheaper. It lists at $0.25 per million input tokens and $0.95 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.
Is DeepSeek-V3.1 or Qwen3.8 27B better for coding?
Qwen3.8 27B scores higher on coding benchmarks: 50.3 versus 39.8 in the Noometry coding category.
Which has the bigger context window?
Qwen3.8 27B does, with 262K tokens against 164K.
How many benchmarks do DeepSeek-V3.1 and Qwen3.8 27B share?
21 benchmarks have published results for both models. DeepSeek-V3.1 has 27 scored results on Noometry and Qwen3.8 27B has 28.