DeepSeek V4.1 Flash 1017 vs Nemotron 3.5 Super VL
DeepSeek V4.1 Flash 1017 leads Nemotron 3.5 Super VL on the Artificial Analysis Intelligence Index, 39.5 to 26.5, and Nemotron 3.5 Super VL costs 1.2x less per output token ($0.50 vs $0.60 per million). Updated October 22, 2026; 16 benchmarks have a result for both.
DeepSeek V4.1 Flash 1017 or Nemotron 3.5 Super VL: which to pick
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; Nemotron 3.5 Super VL has none, so the two cannot be ranked on coding.
Pick Nemotron 3.5 Super VL for output tokens 1.2x cheaper ($0.50 vs $0.60 per million).
- Shared results
- 16
- DeepSeek V4.1 Flash 1017 only
- 23
- Nemotron 3.5 Super VL only
- 0
- Like-for-like categories
- 6 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
No call
Only DeepSeek V4.1 Flash 1017 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 39.5 against 26.5.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V4.1 Flash 1017
DeepSeek V4.1 Flash 1017 leads, 1310 against 1173.
Same board, both measured
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
Nemotron 3.5 Super VL
DeepSeek V4.1 Flash 1017 $0.0004, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
At a glance
- Coding index
- DeepSeek V4.1 Flash 1017 70.3 from 4 benchmarks, Nemotron 3.5 Super VL not benchmarked.
- Widest category
- Agentic work, 38.6 points apart on average over 3 shared benchmarks.
- Widest single gap
- LiveBench browser: DeepSeek V4.1 Flash 1017 76.0% against Nemotron 3.5 Super VL 4.0%, 72.0 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4.1 Flash 1017 $0.60 against Nemotron 3.5 Super VL $0.50 per million output tokens, 1.2x 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 | Nemotron 3.5 Super VL | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 3 | 0 | ||
| Reasoning | 2 | 0 | ||
| Web design | 6 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 16 | 0 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | DeepSeek V4.1 Flash 1017 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | 65.5% | DeepSeek V4.1 Flash 1017 |
| LMArena WebDev | 1664 | Not measured | Only DeepSeek V4.1 Flash 1017 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% | 47.8% | 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 34 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4.1 Flash 1017 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.5 | 26.5 | 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 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: spreadsheets | 1259 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench research | 79.5% | 70.9% | DeepSeek V4.1 Flash 1017 |
| LiveBench browser | 76.0% | 4.0% | DeepSeek V4.1 Flash 1017 |
| LiveBench long agentic | 50.9% | 15.8% | 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 33 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4.1 Flash 1017 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench reasoning | 89.1% | 78.8% | DeepSeek V4.1 Flash 1017 |
| Humanity's Last Exam (Artificial Analysis run) | 43.3% | 30.7% | DeepSeek V4.1 Flash 1017 |
| GPQA Diamond (Epoch AI run) | 93.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Chess puzzles | 46.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Mystery game puzzles | 49.0% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| OTIS Mock AIME | 99.4% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tiers 1 to 3 | 71.2% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| FrontierMath, tier 4 | 46.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| SimpleQA Verified | 42.5% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Neither model has a result on the other 19 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | DeepSeek V4.1 Flash 1017 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | 1310 | 1173 | DeepSeek V4.1 Flash 1017 |
| Design Arena: UI components | 1306 | 1149 | DeepSeek V4.1 Flash 1017 |
| Design Arena: game dev | 1352 | 1110 | DeepSeek V4.1 Flash 1017 |
| Design Arena: data viz | 1306 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| Design Arena: 3D | 1363 | 1128 | DeepSeek V4.1 Flash 1017 |
| Design Arena: all code categories | 1330 | 1154 | DeepSeek V4.1 Flash 1017 |
| Design Arena: ASCII art | 1233 | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench design | 83.4% | 60.7% | 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 index20
| Benchmark | DeepSeek V4.1 Flash 1017 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Vals Index | 61.3% | Not measured | Only DeepSeek V4.1 Flash 1017 measured |
| LiveBench data analysis | 79.5% | 64.6% | DeepSeek V4.1 Flash 1017 |
| Long context reasoning (AA-LCR) | 82.7% | 69.0% | DeepSeek V4.1 Flash 1017 |
| Neither model has a result on the other 45 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4.1 Flash 1017 | Nemotron 3.5 Super VL | 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
Nemotron 3.5 Super VL costs less
Repository review
50K input + 3K output tokens
Nemotron 3.5 Super VL costs less
Agent loop
200K input + 10K output tokens
Nemotron 3.5 Super VL 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 | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | DeepSeek | NVIDIA |
| Weights | Open weights | Open weights |
| Released | October 17, 2026 | October 15, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.15 | $0.15 |
| Output $/Mtok | $0.60 | $0.50 |
| Coding index | 70.3 | Not benchmarked |
| Independent measurements behind it | 4 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4.1 Flash 1017 better than Nemotron 3.5 Super VL for coding?
Only DeepSeek V4.1 Flash 1017 has a coding score, 70.3 on the Vibe Coding Index; Nemotron 3.5 Super VL has none, so the two cannot be ranked on coding. Pick Nemotron 3.5 Super VL for output tokens 1.2x cheaper ($0.50 vs $0.60 per million).
Which is cheaper, DeepSeek V4.1 Flash 1017 or Nemotron 3.5 Super VL?
DeepSeek V4.1 Flash 1017 costs $0.15 per million input tokens and $0.60 per million output tokens; Nemotron 3.5 Super VL costs $0.15 and $0.50. Cost per solved task is known for neither of them.
Which has the bigger context window, DeepSeek V4.1 Flash 1017 or Nemotron 3.5 Super VL?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4.1 Flash 1017 vs Nemotron 3.5 Super VL comparison based on?
16 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 Nemotron 3.5 Super VL's on 1. Where only one model has a result, the page says so instead of naming a winner.