DeepSeek V4.1 Flash 1017 vs Qwen3.6 27B
DeepSeek V4.1 Flash 1017 leads Qwen3.6 27B on coding, 70.3 to 36.8 on the Vibe Coding Index, and DeepSeek V4.1 Flash 1017 costs 4.5x less per output token ($0.60 vs $2.70 per million). Updated October 22, 2026; 9 benchmarks have a result for both.
DeepSeek V4.1 Flash 1017 or Qwen3.6 27B: which to pick
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 36.8 for Qwen3.6 27B.
Qwen3.6 27B is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 9
- DeepSeek V4.1 Flash 1017 only
- 30
- Qwen3.6 27B only
- 15
- Like-for-like categories
- 5 of 9
Only benchmarks that tested both models count toward a pick; a result only one model has is shown but not compared.
Which one for your work
Each card is decided by one number both models carry. Where only one has it, the card says so and makes no call.
Coding
Vibe Coding Index, six public coding benchmarks
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 scores 70.3 against 36.8.
4 and 2 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 21.4.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only Qwen3.6 27B has a AA Agentic Index result, 18.5.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V4.1 Flash 1017 has a Design Arena: websites result, 1311.
Directional only
General
Vals Index
No call
Only DeepSeek V4.1 Flash 1017 has a Vals Index result, 61.3%.
Directional only
Long documents
Listed context window
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 $0.0004, Qwen3.6 27B $0.0018 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Qwen3.6 27B has the number, $1.30.
Directional only
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, Qwen3.6 27B 36.8 from 2 benchmarks.
- Widest category
- Coding, 44.4 points apart on average over 2 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V4.1 Flash 1017 87.7% against Qwen3.6 27B 11.9%, 75.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against Qwen3.6 27B $2.70 per million output tokens, 4.5x apart.
- Context window
- DeepSeek V4.1 Flash 1017 takes 1.05M tokens, Qwen3.6 27B 262K.
Benchmark by benchmark
First the score: boards each model leads, per category, where both have a result. Then every board, a category to a fold; bars are each result on a 0 to 100 scale, so compare the two in a row, not one row with another.
| Category | DeepSeek V4.1 Flash 1017 | Bars | Qwen3.6 27B | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 3 | 0 | ||
| Math | 2 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 9 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | DeepSeek V4.1 Flash 1017 | Qwen3.6 27B | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev | 1664 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| AA Coding Index | Not measured | 53.7 | Only Qwen3.6 27B measured |
| SWE-bench Verified (Vals AI) | Not measured | 70.0% | Only Qwen3.6 27B measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Vibe Code Bench (Vals AI) | 87.7% | 11.9% | DeepSeek V4.1 Flash 1017 |
| Code migration (Vals AI) | 44.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| SciCode | 55.9% | 42.8% | DeepSeek V4.1 Flash 1017 |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 34.8% | Only Qwen3.6 27B measured |
| ALE-Bench | 1622.0 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev: React | 1667 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LMArena WebDev: HTML | 1687 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 27 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 21.4 | DeepSeek V4.1 Flash 1017 |
| Epoch Capabilities Index | Not measured | 146.2 | Only Qwen3.6 27B measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Qwen3.6 27B | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 18.5 | Only Qwen3.6 27B measured |
| Design Arena: spreadsheets | 1260 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench browser | 76.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench long agentic | 50.9% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Finance Agent (Vals AI) | 57.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Tax Agent Bench (Vals AI) | 44.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Legal Research Bench (Vals AI) | 45.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 14.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Excel Modeling Benchmark (Vals AI) | 61.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| τ²-Bench Telecom | Not measured | 94.2% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4.1 Flash 1017 | Qwen3.6 27B | Leader |
|---|---|---|---|
| LiveBench reasoning | 89.1% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 84.2% | Only Qwen3.6 27B measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | 23.1% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | 93.2% | 85.9% | DeepSeek V4.1 Flash 1017 |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Mystery game puzzles | 49.0% | 7.0% | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 16 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash 1017 | Qwen3.6 27B | Leader |
|---|---|---|---|
| OTIS Mock AIME | 99.4% | 91.1% | DeepSeek V4.1 Flash 1017 |
| FrontierMath, tiers 1 to 3 | 71.2% | 35.1% | DeepSeek V4.1 Flash 1017 |
| FrontierMath, tier 4 | 46.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 10 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Qwen3.6 27B | Leader |
|---|---|---|---|
| CaseLaw (Vals AI) | Not measured | 53.2% | Only Qwen3.6 27B measured |
| CorpFin (Vals AI) | Not measured | 62.3% | Only Qwen3.6 27B measured |
| TaxEval (Vals AI) | Not measured | 71.3% | Only Qwen3.6 27B measured |
| MortgageTax (Vals AI) | Not measured | 68.3% | Only Qwen3.6 27B measured |
| SimpleQA Verified | 42.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index8 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Qwen3.6 27B | Leader |
|---|---|---|---|
| Design Arena: websites | 1311 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: UI components | 1306 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: game dev | 1353 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: data viz | 1312 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: 3D | 1358 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: all code categories | 1331 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: ASCII art | 1235 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench design | 83.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Qwen3.6 27B | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| SAGE (Vals AI) | Not measured | 45.6% | Only Qwen3.6 27B measured |
| IFBench | Not measured | 67.6% | Only Qwen3.6 27B measured |
| Long context reasoning (AA-LCR) | 82.7% | 77.3% | DeepSeek V4.1 Flash 1017 |
| DTBench | Not measured | 78.1% | Only Qwen3.6 27B measured |
| LMCA | Not measured | 34.5% | Only Qwen3.6 27B measured |
| Neither model has a result on the other 39 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Qwen3.6 27B | Leader |
|---|---|---|---|
| Neither model has a result on 5 boards here | |||
What each workload costs
Three fixed token mixes at each model's listed price, so per-token rates become a bill.
Chat turn
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017 costs less
Repository review
50K input + 3K output tokens
DeepSeek V4.1 Flash 1017 costs less
Agent loop
200K input + 10K output tokens
DeepSeek V4.1 Flash 1017 costs less
Listed OpenRouter rates, without cache or batch discounts. A workload that does not fit a model's context window would need splitting.
Specification differences
The leading value in a row is marked; a row with a missing value names no leader. Our two rows are marked in red.
| Specification | DeepSeek V4.1 Flash 1017 | Qwen3.6 27B |
|---|---|---|
| Maker | DeepSeek | Qwen |
| Weights | Open weights | Open weights |
| Released | October 17, 2026 | April 27, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.15 | $0.45 |
| Output $/Mtok | $0.60 | $2.70 |
| Coding index | 70.3 | 36.8 |
| Independent measurements behind it | 4 | 2 |
| Cost per solved task | Not computable | $1.30 |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than Qwen3.6 27B for coding?
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 70.3 on the Vibe Coding Index against 36.8 for Qwen3.6 27B. Qwen3.6 27B is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
Which is cheaper, DeepSeek V4.1 Flash 1017 or Qwen3.6 27B?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; Qwen3.6 27B costs $0.45 and $2.70. Cost per solved task is known for Qwen3.6 27B only.
Which has the bigger context window, DeepSeek V4.1 Flash 1017 or Qwen3.6 27B?
DeepSeek V4.1 Flash 1017, with 1.05M tokens against 262K.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs Qwen3.6 27B comparison based on?
9 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 4 independent benchmarks and Qwen3.6 27B's on 2. Where only one model has a result, the page says so instead of naming a winner.