Model comparison
DeepSeek V4 Flash vs Qwen3.8 27B
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 46.0 on the Noometry Index.
Last verified . 28 shared benchmarks.
Summary
- They share 28 benchmarks with published results for both. DeepSeek V4 Flash scores higher in 3 categories and Qwen3.8 27B in 5 categories; 5 gaps are clear of the uncertainty.
- The widest gap is in math, where DeepSeek V4 Flash leads 60.3 to 37.1.
- The biggest single-benchmark swing is ProofBench: 56% for DeepSeek V4 Flash and 16% for Qwen3.8 27B.
- DeepSeek V4 Flash is cheaper at $0.15 / $0.60 per million input/output tokens, against $0.99 / $1.49 for Qwen3.8 27B.
- DeepSeek V4 Flash accepts more context: 1M tokens versus 262K.
Side by side
| DeepSeek V4 Flash | Qwen3.8 27B | |
|---|---|---|
| Provider | DeepSeek | Alibaba (Qwen) |
| Noometry Index | 53.6 | 46.0 |
| Released | 2026-04-24 | 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 | 41 | 31 |
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Category by category
Coding Qwen3.8 27B leads
DeepSeek V4 Flash: 47.9 (#59), Qwen3.8 27B: 50.5 (#44)
| Benchmark | DeepSeek V4 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena WebDev | 1582 | 1593 |
| SciCode | 49.9% | 46.6% |
| LMArena Coding | 1457 | 1482 |
| FrontierCode | 18.8% | — |
| WeirdML | 63% | — |
| ALE-Bench | 1,306 | — |
Agentic & Tool Use Not comparable
DeepSeek V4 Flash: —, Qwen3.8 27B: 32.9 (#57)
| Benchmark | DeepSeek V4 Flash | Qwen3.8 27B |
|---|---|---|
| APEX-Agents | — | 47.5% |
Reasoning DeepSeek V4 Flash leads
DeepSeek V4 Flash: 53.7 (#30), Qwen3.8 27B: 41.0 (#54)
| Benchmark | DeepSeek V4 Flash | Qwen3.8 27B |
|---|---|---|
| ARC-AGI-2 | 61.4% | 42.4% |
| NYT Connections (extended) | 89.6% | 54.5% |
| ARC-AGI-1 | 89% | 87.5% |
| CritPt | 16.6% | 5.4% |
| LMArena Hard Prompts | 1444 | 1460 |
| DTBench | 90.9% | 88% |
| LMCA | 41.7% | 41.4% |
| Epoch Capabilities Index | 154.49 | 149.38 |
| SimpleBench | 61.1% | — |
| Kagi LLM Benchmark | 52.2% | — |
| Chess Puzzles | 33% | — |
| Mystery Game Puzzles | 34% | — |
| Surface Evolver Bench | — | 45% |
Math DeepSeek V4 Flash leads
DeepSeek V4 Flash: 60.3 (#37), Qwen3.8 27B: 37.1 (#161)
| Benchmark | DeepSeek V4 Flash | Qwen3.8 27B |
|---|---|---|
| ProofBench | 56% | 16% |
| LMArena Math | 1427 | 1456 |
| FrontierMath (Tiers 1-3) | 57.5% | — |
| FrontierMath Tier 4 | 24.4% | — |
| MathArena Final-Answer Competitions | 76.5% | — |
| OTIS Mock AIME 2024-2025 | 94.4% | — |
Knowledge DeepSeek V4 Flash leads
DeepSeek V4 Flash: 55.4 (#48), Qwen3.8 27B: 41.6 (#109)
| Benchmark | DeepSeek V4 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Expert | 1441 | 1482 |
| GPQA Diamond | 91% | — |
| SimpleQA Verified | 33.6% | — |
Multimodal Not comparable
DeepSeek V4 Flash: —, Qwen3.8 27B: 41.3 (#37)
| Benchmark | DeepSeek V4 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Vision | — | 1271 |
Multilingual Too close to call
DeepSeek V4 Flash: 53.0 (#72), Qwen3.8 27B: 53.7 (#60)
| Benchmark | DeepSeek V4 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Non-English | 1420 | 1430 |
| LMArena Chinese | 1468 | 1504 |
| LMArena French | 1439 | 1465 |
| LMArena German | 1418 | 1438 |
| LMArena Japanese | 1406 | 1384 |
| LMArena Korean | 1384 | 1393 |
| LMArena Russian | 1428 | 1415 |
| LMArena Spanish | 1436 | 1448 |
Instruction Following Too close to call
DeepSeek V4 Flash: 74.9 (#81), Qwen3.8 27B: 75.8 (#53)
| Benchmark | DeepSeek V4 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Instruction Following | 1421 | 1439 |
Long Context Too close to call
DeepSeek V4 Flash: 43.8 (#85), Qwen3.8 27B: 44.3 (#70)
| Benchmark | DeepSeek V4 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Longer Query | 1434 | 1450 |
Writing & Preference Qwen3.8 27B leads
DeepSeek V4 Flash: 63.8 (#61), Qwen3.8 27B: 65.8 (#43)
| Benchmark | DeepSeek V4 Flash | Qwen3.8 27B |
|---|---|---|
| LMArena Text | 1432 | 1441 |
| LMArena Creative Writing | 1403 | 1384 |
| EQ-Bench Creative Writing | 1559 | 1671 |
| LMArena Multi-Turn | 1449 | 1441 |
Frequently asked questions
Is DeepSeek V4 Flash better than Qwen3.8 27B?
DeepSeek V4 Flash is the stronger model overall, scoring 53.6 to 46.0 on the Noometry Index.
Which is cheaper, DeepSeek V4 Flash or Qwen3.8 27B?
DeepSeek V4 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 Flash or Qwen3.8 27B better for coding?
Qwen3.8 27B scores higher on coding benchmarks: 50.5 versus 47.9 in the Noometry coding category.
Which has the bigger context window?
DeepSeek V4 Flash does, with 1M tokens against 262K.
How many benchmarks do DeepSeek V4 Flash and Qwen3.8 27B share?
28 benchmarks have published results for both models. DeepSeek V4 Flash has 41 scored results on Noometry and Qwen3.8 27B has 31.