DeepSeek V4.1 Flash 1017 vs DeepSeek V4 Flash Vision Exp
Updated October 21, 2026. 11 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 83.3 on the Vibe Coding Index against 37.6 for DeepSeek V4 Flash Vision Exp.
DeepSeek V4 Flash Vision Exp is neither cheaper nor larger in context, and leads no benchmark both models were measured on.
- Shared results
- 11
- DeepSeek V4.1 Flash 1017 only
- 21
- DeepSeek V4 Flash Vision Exp only
- 3
- Like-for-like categories
- 6 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 scores 83.3 against 37.6.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 34.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Flash Vision Exp has a AA Agentic Index result, 47.5.
Directional only
Web design
Design Arena: websites
No call
Only DeepSeek V4.1 Flash 1017 has a Design Arena: websites result, 1312.
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
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 $0.0004, DeepSeek V4 Flash Vision Exp $0.0005 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 83.3 from 2 benchmarks, DeepSeek V4 Flash Vision Exp 37.6 from 1 benchmark.
- Widest category
- Coding, 32.3 points apart on average over 2 shared benchmarks.
- Widest single gap
- LiveBench coding: DeepSeek V4.1 Flash 1017 96.0% against DeepSeek V4 Flash Vision Exp 37.6%, 58.4 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against DeepSeek V4 Flash Vision Exp $0.6468 per million output tokens, 1.1x apart.
- Context window
- Both take 1.05M tokens.
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 | DeepSeek V4 Flash Vision Exp | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 3 | 0 | ||
| Reasoning | 2 | 0 | ||
| Web design | 1 | 0 | ||
| General | 1 | 0 | 1 tie | |
| All shared boards | 10 | 0 | 1 tie |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index20
| Benchmark | DeepSeek V4.1 Flash 1017 | DeepSeek V4 Flash Vision Exp | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | 37.6% | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev | 1664 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| AA Coding Index | Not measured | 65.0 | Only DeepSeek V4 Flash Vision Exp measured |
| Terminal-Bench 4 (Vals AI) | 31.8% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Vibe Code Bench (Vals AI) | 87.7% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Code migration (Vals AI) | 44.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| SciCode | 55.9% | 49.7% | DeepSeek V4.1 Flash 1017 |
| 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 28 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | DeepSeek V4 Flash Vision Exp | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 34.8 | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 1 board here | |||
Agentic workLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | DeepSeek V4.1 Flash 1017 | DeepSeek V4 Flash Vision Exp | Leader |
|---|---|---|---|
| AA Agentic Index | Not measured | 47.5 | Only DeepSeek V4 Flash Vision Exp measured |
| Design Arena: spreadsheets | 1238 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | 69.2% | DeepSeek V4.1 Flash 1017 |
| LiveBench browser | 76.0% | 64.0% | DeepSeek V4.1 Flash 1017 |
| LiveBench long agentic | 50.9% | 39.0% | DeepSeek V4.1 Flash 1017 |
| 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 |
| Neither model has a result on the other 29 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash 1017 | DeepSeek V4 Flash Vision Exp | Leader |
|---|---|---|---|
| LiveBench reasoning | 89.1% | 85.4% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Artificial Analysis run) | Not measured | 91.3% | Only DeepSeek V4 Flash Vision Exp measured |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | 34.5% | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 18 boards here | |||
MathNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | DeepSeek V4 Flash Vision Exp | Leader |
|---|---|---|---|
| Neither model has a result on 13 boards here | |||
KnowledgeNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | DeepSeek V4 Flash Vision Exp | Leader |
|---|---|---|---|
| Neither model has a result on 17 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | DeepSeek V4 Flash Vision Exp | Leader |
|---|---|---|---|
| Design Arena: websites | 1312 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: UI components | 1305 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: game dev | 1365 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: data viz | 1330 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: 3D | 1368 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: all code categories | 1335 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: ASCII art | 1244 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench design | 83.4% | 70.7% | DeepSeek V4.1 Flash 1017 |
| 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 | DeepSeek V4 Flash Vision Exp | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | 79.5% | Tie |
| Long context reasoning (AA-LCR) | 82.7% | 81.3% | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | DeepSeek V4 Flash Vision Exp | 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 | DeepSeek V4 Flash Vision Exp |
|---|---|---|
| Maker | DeepSeek | DeepSeek |
| Weights | Open weights | Open weights |
| Released | October 17, 2026 | August 21, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.15 | $0.2156 |
| Output $/Mtok | $0.60 | $0.6468 |
| Coding index | 83.3 | 37.6 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than DeepSeek V4 Flash Vision Exp for coding?
Pick DeepSeek V4.1 Flash 1017 for coding: it scores 83.3 on the Vibe Coding Index against 37.6 for DeepSeek V4 Flash Vision Exp. DeepSeek V4 Flash Vision Exp 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 DeepSeek V4 Flash Vision Exp?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; DeepSeek V4 Flash Vision Exp costs $0.2156 and $0.6468. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash 1017 or DeepSeek V4 Flash Vision Exp?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs DeepSeek V4 Flash Vision Exp comparison based on?
11 benchmarks have a result for both models as of October 21, 2026. DeepSeek V4.1 Flash 1017's coding index rests on 2 independent benchmarks and DeepSeek V4 Flash Vision Exp's on 1. Where only one model has a result, the page says so instead of naming a winner.