Model comparison
DeepSeek V4.1 Flash vs Qwen3.8 27B
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 46.0 on the Noometry Index.
Last verified . 29 shared benchmarks.
Summary
- They share 29 benchmarks with published results for both. DeepSeek V4.1 Flash scores higher in 7 categories and Qwen3.8 27B in 3 categories; 8 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek V4.1 Flash leads 66.7 to 37.1.
- The biggest single-benchmark swing is ProofBench: 54% for DeepSeek V4.1 Flash and 16% for Qwen3.8 27B.
- DeepSeek V4.1 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
- DeepSeek V4.1 Flash accepts more context: 1M tokens versus 262K.
Side by side
| DeepSeek V4.1 Flash | Qwen3.8 27B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 52.8 | 46.0 |
| Released | 2026-09-09 | 2026-08-14 |
| Weights | Open | Open |
| Context window | 1M | 262K |
| Max output | 393K | 33K |
| Input $ / M tokens | $0.15 | $0.99 |
| Output $ / M tokens | $0.60 | $1.49 |
| Results tracked | 37 | 31 |
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Category by category
Coding DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 52.9 (#32), Qwen3.8 27B: 50.5 (#44)
| Benchmark | DeepSeek V4.1 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena WebDev | 1619 | 1593 |
| SciCode | 51.9% | 46.6% |
| LMArena Coding | 1506 | 1482 |
| ALE-Bench | 1,092 | — |
Agentic & Tool Use Qwen3.8 27B leads
DeepSeek V4.1 Flash: 31.2 (#69), Qwen3.8 27B: 32.9 (#57)
| Benchmark | DeepSeek V4.1 Flash | Qwen3.8 27B |
|---|---|---|
| APEX-Agents | 39.5% | 47.5% |
| GDP.pdf | 19.8% | — |
Reasoning DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 50.2 (#36), Qwen3.8 27B: 41.0 (#54)
| Benchmark | DeepSeek V4.1 Flash | Qwen3.8 27B |
|---|---|---|
| NYT Connections (extended) | 89.6% | 54.5% |
| CritPt | 14.3% | 5.4% |
| LMArena Hard Prompts | 1483 | 1460 |
| DTBench | 89.9% | 88% |
| LMCA | 47% | 41.4% |
| Surface Evolver Bench | 46.3% | 45% |
| Epoch Capabilities Index | 154.9 | 149.38 |
| ARC-AGI-2 | — | 42.4% |
| ARC-AGI-1 | — | 87.5% |
| Mystery Game Puzzles | 43% | — |
Math DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 66.7 (#25), Qwen3.8 27B: 37.1 (#161)
| Benchmark | DeepSeek V4.1 Flash | Qwen3.8 27B |
|---|---|---|
| ProofBench | 54% | 16% |
| LMArena Math | 1477 | 1456 |
| FrontierMath (Tiers 1-3) | 67.4% | — |
| FrontierMath Tier 4 | 26.8% | — |
| OTIS Mock AIME 2024-2025 | 98.3% | — |
Knowledge DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 57.9 (#38), Qwen3.8 27B: 41.6 (#109)
| Benchmark | DeepSeek V4.1 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1506 | 1482 |
| GPQA Diamond | 89.8% | — |
Multimodal Qwen3.8 27B leads
DeepSeek V4.1 Flash: 39.1 (#61), Qwen3.8 27B: 41.3 (#37)
| Benchmark | DeepSeek V4.1 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | 1277 | 1271 |
| Furniture Assembly | 34.2% | — |
Multilingual DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 55.0 (#35), Qwen3.8 27B: 53.7 (#60)
| Benchmark | DeepSeek V4.1 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1448 | 1430 |
| LMArena Chinese | 1497 | 1504 |
| LMArena French | 1452 | 1465 |
| LMArena German | 1484 | 1438 |
| LMArena Japanese | 1412 | 1384 |
| LMArena Korean | 1452 | 1393 |
| LMArena Russian | 1471 | 1415 |
| LMArena Spanish | 1459 | 1448 |
Instruction Following DeepSeek V4.1 Flash leads
DeepSeek V4.1 Flash: 77.3 (#26), Qwen3.8 27B: 75.8 (#53)
| Benchmark | DeepSeek V4.1 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1474 | 1439 |
Long Context Too close to call
DeepSeek V4.1 Flash: 45.2 (#47), Qwen3.8 27B: 44.3 (#70)
| Benchmark | DeepSeek V4.1 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1475 | 1450 |
Writing & Preference Too close to call
DeepSeek V4.1 Flash: 65.4 (#48), Qwen3.8 27B: 65.8 (#43)
| Benchmark | DeepSeek V4.1 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1462 | 1441 |
| LMArena Creative Writing | 1435 | 1384 |
| EQ-Bench Creative Writing | 1540 | 1671 |
| LMArena Multi-Turn | 1457 | 1441 |
Frequently asked questions
Is DeepSeek V4.1 Flash better than Qwen3.8 27B?
DeepSeek V4.1 Flash is the stronger model overall, scoring 52.8 to 46.0 on the Noometry Index.
Which is cheaper, DeepSeek V4.1 Flash or Qwen3.8 27B?
DeepSeek V4.1 Flash is cheaper. It lists at $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.8 27B lists at $0.99 and $1.49.
Is DeepSeek V4.1 Flash or Qwen3.8 27B better for coding?
DeepSeek V4.1 Flash scores higher on coding benchmarks: 52.9 versus 50.5 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4.1 Flash does, with 1M tokens against 262K.
How many benchmarks do DeepSeek V4.1 Flash and Qwen3.8 27B share?
29 benchmarks have published results for both models. DeepSeek V4.1 Flash has 37 scored results on Noometry and Qwen3.8 27B has 31.