DeepSeek V4 Pro 0423 vs Nemotron 3.5 Super VL
DeepSeek V4 Pro 0423 leads Nemotron 3.5 Super VL on the Artificial Analysis Intelligence Index, 30.4 to 26.5, and Nemotron 3.5 Super VL costs 6.0x less per output token ($0.50 vs $2.976 per million). Updated October 22, 2026; 6 benchmarks have a result for both.
DeepSeek V4 Pro 0423 or Nemotron 3.5 Super VL: which to pick
Only DeepSeek V4 Pro 0423 has a coding score, 38.7 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 6.0x cheaper ($0.50 vs $2.976 per million).
- Shared results
- 6
- DeepSeek V4 Pro 0423 only
- 78
- Nemotron 3.5 Super VL only
- 10
- Like-for-like categories
- 2 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 Pro 0423 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 30.4 against 26.5.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V4 Pro 0423 has a AA Agentic Index result, 26.3.
Directional only
Reasoning
GPQA Diamond
No call
Only DeepSeek V4 Pro 0423 has a GPQA Diamond result, 89.4%.
Directional only
Knowledge
MMLU Pro
No call
Only DeepSeek V4 Pro 0423 has a MMLU Pro result, 87.3%.
Directional only
Web design
Design Arena: websites
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 1234 against 1172.
Same board, both measured
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 Pro 0423 $0.0028, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only DeepSeek V4 Pro 0423 has the number, $0.57.
Directional only
At a glance
- Coding index
- DeepSeek V4 Pro 0423 38.7 from 5 benchmarks, Nemotron 3.5 Super VL not benchmarked.
- Widest category
- Web design, 11.4 points apart on average over 5 shared benchmarks.
- Widest single gap
- Design Arena: 3D: DeepSeek V4 Pro 0423 1253 against Nemotron 3.5 Super VL 1127, 15.8 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $2.976 against Nemotron 3.5 Super VL $0.50 per million output tokens, 6.0x 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 Pro 0423 | Bars | Nemotron 3.5 Super VL | Ties |
|---|---|---|---|---|
| Intelligence | 1 | 0 | ||
| Web design | 5 | 0 | ||
| All shared boards | 6 | 0 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index19 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 65.5% | Only Nemotron 3.5 Super VL measured |
| LMArena WebDev | 1447 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| AA Coding Index | 59.4 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveCodeBench (Vals AI) | 87.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SWE-bench Verified (Vals AI) | 77.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 4 (Vals AI) | 11.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Vibe Code Bench (Vals AI) | 49.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Code migration (Vals AI) | 26.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SkillsBench (Vals AI) | 51.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SciCode | Not measured | 47.8% | Only Nemotron 3.5 Super VL measured |
| FrontierCode | 17.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| WeirdML | 48.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ALE-Bench | 1006.1 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: coding | 1470 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: software and IT | 1469 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena WebDev: React | 1441 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena WebDev: HTML | 1435 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 26.5 | DeepSeek V4 Pro 0423 |
| Epoch Capabilities Index | 149.1 | Not measured | Only DeepSeek V4 Pro 0423 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building web apps | 1000 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building full-stack apps | 948 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: agents building Godot games | 1059 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveBench research | Not measured | 70.9% | Only Nemotron 3.5 Super VL measured |
| LiveBench browser | Not measured | 4.0% | Only Nemotron 3.5 Super VL measured |
| LiveBench long agentic | Not measured | 15.8% | Only Nemotron 3.5 Super VL measured |
| Finance Agent (Vals AI) | 44.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Tax Agent Bench (Vals AI) | 25.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Legal Research Bench (Vals AI) | 23.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Excel Modeling Benchmark (Vals AI) | 51.6% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Vending-Bench 2 | $3,285 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 29 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveBench reasoning | Not measured | 78.8% | Only Nemotron 3.5 Super VL measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 30.7% | Only Nemotron 3.5 Super VL measured |
| GPQA Diamond (Epoch AI run) | 90.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Chess puzzles | 19.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Mystery game puzzles | 17.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: hard prompts | 1461 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: expert | 1463 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: hard prompts in English | 1466 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Kagi LLM Benchmark | 53.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 12 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| OTIS Mock AIME | 96.7% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| FrontierMath, tiers 1 to 3 | 45.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| FrontierMath, tier 4 | 2.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| ProofBench | 16.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: math | 1443 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: mathematical work | 1455 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LegalBench (Vals AI) | 80.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CaseLaw (Vals AI) | 59.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CorpFin (Vals AI) | 61.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| TaxEval (Vals AI) | 72.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MedCode (Vals AI) | 40.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SimpleQA Verified | 47.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: science | 1470 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: medicine and healthcare | 1466 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: legal and government | 1464 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: business and finance | 1443 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | 1234 | 1172 | DeepSeek V4 Pro 0423 |
| Design Arena: UI components | 1219 | 1153 | DeepSeek V4 Pro 0423 |
| Design Arena: game dev | 1228 | 1110 | DeepSeek V4 Pro 0423 |
| Design Arena: data viz | 1203 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: 3D | 1253 | 1127 | DeepSeek V4 Pro 0423 |
| Design Arena: all code categories | 1240 | 1155 | DeepSeek V4 Pro 0423 |
| Design Arena: SVG | 1139 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Design Arena: ASCII art | 1149 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LiveBench design | Not measured | 60.7% | Only Nemotron 3.5 Super VL measured |
GeneralDirectional onlyPublic boards, shown as published, kept out of the index25 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 64.6% | Only Nemotron 3.5 Super VL measured |
| Public Benefits Bench (Vals AI) | 62.9% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MedScribe (Vals AI) | 75.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Long context reasoning (AA-LCR) | Not measured | 69.0% | Only Nemotron 3.5 Super VL measured |
| DTBench | 90.7% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMCA | 41.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Surface Evolver Bench | 40.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CL-bench Life | 13.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text | 1451 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: instruction following | 1445 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: creative writing | 1446 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: English | 1459 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: non-English | 1439 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Chinese | 1481 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: German | 1458 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Japanese | 1446 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Korean | 1409 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: French | 1473 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Spanish | 1457 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Russian | 1453 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Polish | 1461 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: multi-turn | 1468 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: longer queries | 1458 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: writing and language | 1447 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: entertainment and media | 1431 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 21 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | 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 Pro 0423 | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | DeepSeek | NVIDIA |
| Weights | Open weights | Open weights |
| Released | April 24, 2026 | October 15, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $1.358 | $0.15 |
| Output $/Mtok | $2.976 | $0.50 |
| Coding index | 38.7 | Not benchmarked |
| Independent measurements behind it | 5 | 1 |
| Cost per solved task | $0.57 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than Nemotron 3.5 Super VL for coding?
Only DeepSeek V4 Pro 0423 has a coding score, 38.7 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 6.0x cheaper ($0.50 vs $2.976 per million).
Which is cheaper, DeepSeek V4 Pro 0423 or Nemotron 3.5 Super VL?
DeepSeek V4 Pro 0423 costs $1.358 per million input tokens and $2.976 per million output tokens; Nemotron 3.5 Super VL costs $0.15 and $0.50. Cost per solved task is known for DeepSeek V4 Pro 0423 only.
Which has the bigger context window, DeepSeek V4 Pro 0423 or Nemotron 3.5 Super VL?
Neither: both take 1.05M tokens.
How many benchmarks is the DeepSeek V4 Pro 0423 vs Nemotron 3.5 Super VL comparison based on?
6 benchmarks have a result for both models as of October 22, 2026. DeepSeek V4 Pro 0423's coding index rests on 5 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.