DeepSeek V4 Pro 0423 vs Nemotron 3 Ultra
DeepSeek V4 Pro 0423 leads Nemotron 3 Ultra on coding, 38.7 to 30.0 on the Vibe Coding Index, at about the same output price ($2.60 vs $2.40 per million). Updated October 22, 2026; 62 benchmarks have a result for both.
DeepSeek V4 Pro 0423 or Nemotron 3 Ultra: which to pick
Pick DeepSeek V4 Pro 0423 for coding: it scores 38.7 on the Vibe Coding Index against 30.0 for Nemotron 3 Ultra.
Pick Nemotron 3 Ultra for output tokens 1.1x cheaper ($2.40 vs $2.60 per million) and work measured by Terminal-Bench 2.1 (Vals AI) or Mystery game puzzles, where it leads.
- Shared results
- 62
- DeepSeek V4 Pro 0423 only
- 27
- Nemotron 3 Ultra only
- 9
- Like-for-like categories
- 8 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 Pro 0423
DeepSeek V4 Pro 0423 scores 38.7 against 30.0.
5 and 3 benchmarks behind the scores
Intelligence
AA Intelligence Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 30.4 against 22.9.
Same board, both measured
Agentic work
AA Agentic Index
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 26.3 against 20.1.
Same board, both measured
Reasoning
GPQA Diamond
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 89.4% against 86.1%.
Same board, both measured
Knowledge
MMLU Pro
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 87.3% against 85.8%.
Same board, both measured
Web design
Design Arena: websites
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 leads, 1234 against 1145.
Same board, both measured
Long documents
Listed context window
DeepSeek V4 Pro 0423
DeepSeek V4 Pro 0423 takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3 Ultra
DeepSeek V4 Pro 0423 $0.0026, Nemotron 3 Ultra $0.0018 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 Ultra 30.0 from 3 benchmarks.
- Widest category
- Web design, 10.3 points apart on average over 8 shared benchmarks.
- Widest single gap
- Vibe Code Bench (Vals AI): DeepSeek V4 Pro 0423 49.9% against Nemotron 3 Ultra 7.6%, 42.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V4 Pro 0423 $2.60 against Nemotron 3 Ultra $2.40 per million output tokens, 1.1x apart.
- Context window
- DeepSeek V4 Pro 0423 takes 1.05M tokens, Nemotron 3 Ultra 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 Pro 0423 | Bars | Nemotron 3 Ultra | Ties |
|---|---|---|---|---|
| Coding | 9 | 1 | 1 tie | |
| Intelligence | 2 | 0 | ||
| Agentic work | 4 | 0 | ||
| Reasoning | 4 | 2 | ||
| Math | 2 | 2 | ||
| Knowledge | 6 | 3 | ||
| Web design | 8 | 0 | ||
| General | 16 | 2 | ||
| All shared boards | 51 | 10 | 1 tie |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index91
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3 Ultra | Leader |
|---|---|---|---|
| LMArena WebDev | 1447 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| AA Coding Index | 59.4 | 49.3 | DeepSeek V4 Pro 0423 |
| LiveCodeBench (Vals AI) | 87.5% | 86.0% | DeepSeek V4 Pro 0423 |
| SWE-bench Verified (Vals AI) | 77.4% | 69.0% | DeepSeek V4 Pro 0423 |
| Terminal-Bench 4 (Vals AI) | 11.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Vibe Code Bench (Vals AI) | 49.9% | 7.6% | DeepSeek V4 Pro 0423 |
| Code migration (Vals AI) | 26.2% | 4.9% | DeepSeek V4 Pro 0423 |
| ProgramBench (Vals AI) | 0.0% | 0.0% | Tie |
| Terminal-Bench 2.1 (Vals AI) | 50.2% | 50.9% | Nemotron 3 Ultra |
| SkillsBench (Vals AI) | 51.3% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SciCode | Not measured | 40.3% | Only Nemotron 3 Ultra measured |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 36.4% | Only Nemotron 3 Ultra measured |
| FrontierCode | 17.6% | 13.6% | DeepSeek V4 Pro 0423 |
| WeirdML | 48.9% | 43.5% | DeepSeek V4 Pro 0423 |
| ALE-Bench | 1006.1 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: coding | 1470 | 1468 | DeepSeek V4 Pro 0423 |
| LMArena Text: software and IT | 1469 | 1468 | DeepSeek V4 Pro 0423 |
| LMArena WebDev: React | 1441 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena WebDev: HTML | 1435 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SWE Atlas: Codebase QnA | 27.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| SWE Atlas: Test Writing | 27.1% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 22 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3 Ultra | Leader |
|---|---|---|---|
| AA Intelligence Index | 30.4 | 22.9 | DeepSeek V4 Pro 0423 |
| Epoch Capabilities Index | 149.1 | 146.0 | DeepSeek V4 Pro 0423 |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index40
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3 Ultra | Leader |
|---|---|---|---|
| AA Agentic Index | 26.3 | 20.1 | DeepSeek V4 Pro 0423 |
| 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 |
| Finance Agent (Vals AI) | 44.1% | 37.7% | DeepSeek V4 Pro 0423 |
| Tax Agent Bench (Vals AI) | 25.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Legal Research Bench (Vals AI) | 23.1% | 15.4% | DeepSeek V4 Pro 0423 |
| Excel Modeling Benchmark (Vals AI) | 51.6% | 31.7% | DeepSeek V4 Pro 0423 |
| τ²-Bench Telecom | Not measured | 83.3% | Only Nemotron 3 Ultra measured |
| APEX-Agents | Not measured | 22.7% | Only Nemotron 3 Ultra measured |
| Vending-Bench 2 | $3,285 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| MCP Atlas | Not measured | 63.1% | Only Nemotron 3 Ultra measured |
| Neither model has a result on the other 30 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index42
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3 Ultra | Leader |
|---|---|---|---|
| GPQA Diamond | 89.4% | 86.1% | DeepSeek V4 Pro 0423 |
| GPQA Diamond (Artificial Analysis run) | Not measured | 86.7% | Only Nemotron 3 Ultra measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 28.4% | Only Nemotron 3 Ultra measured |
| GPQA Diamond (Epoch AI run) | 90.9% | 85.4% | DeepSeek V4 Pro 0423 |
| Chess puzzles | 19.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Mystery game puzzles | 17.0% | 20.0% | Nemotron 3 Ultra |
| LMArena Text: hard prompts | 1461 | 1452 | DeepSeek V4 Pro 0423 |
| LMArena Text: expert | 1463 | 1472 | Nemotron 3 Ultra |
| LMArena Text: hard prompts in English | 1466 | 1453 | DeepSeek V4 Pro 0423 |
| Kagi LLM Benchmark | 53.5% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Humanity's Last Exam (Scale run) | 13.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 12 boards here | |||
MathLike-for-likePublic boards, shown as published, kept out of the index22
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3 Ultra | Leader |
|---|---|---|---|
| OTIS Mock AIME | 96.7% | 86.7% | DeepSeek V4 Pro 0423 |
| 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% | 2.0% | DeepSeek V4 Pro 0423 |
| LMArena Text: math | 1443 | 1457 | Nemotron 3 Ultra |
| LMArena Text: mathematical work | 1455 | 1461 | Nemotron 3 Ultra |
| MathArena | 26.2% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeLike-for-likePublic boards, shown as published, kept out of the index63
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3 Ultra | Leader |
|---|---|---|---|
| MMLU Pro | 87.3% | 85.8% | DeepSeek V4 Pro 0423 |
| LegalBench (Vals AI) | 80.3% | 82.1% | Nemotron 3 Ultra |
| CaseLaw (Vals AI) | 59.4% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| CorpFin (Vals AI) | 61.4% | 65.5% | Nemotron 3 Ultra |
| TaxEval (Vals AI) | 72.1% | 73.1% | Nemotron 3 Ultra |
| MedCode (Vals AI) | 40.5% | 38.6% | DeepSeek V4 Pro 0423 |
| SimpleQA Verified | 47.0% | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: science | 1470 | 1465 | DeepSeek V4 Pro 0423 |
| LMArena Text: medicine and healthcare | 1466 | 1456 | DeepSeek V4 Pro 0423 |
| LMArena Text: legal and government | 1464 | 1452 | DeepSeek V4 Pro 0423 |
| LMArena Text: business and finance | 1443 | 1434 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 9 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index80
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3 Ultra | Leader |
|---|---|---|---|
| Design Arena: websites | 1234 | 1145 | DeepSeek V4 Pro 0423 |
| Design Arena: UI components | 1219 | 1135 | DeepSeek V4 Pro 0423 |
| Design Arena: game dev | 1228 | 1138 | DeepSeek V4 Pro 0423 |
| Design Arena: data viz | 1203 | 1141 | DeepSeek V4 Pro 0423 |
| Design Arena: 3D | 1253 | 1131 | DeepSeek V4 Pro 0423 |
| Design Arena: all code categories | 1240 | 1146 | DeepSeek V4 Pro 0423 |
| Design Arena: SVG | 1139 | 1069 | DeepSeek V4 Pro 0423 |
| Design Arena: ASCII art | 1149 | 1100 | DeepSeek V4 Pro 0423 |
| Neither model has a result on the other 1 board here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index162
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3 Ultra | Leader |
|---|---|---|---|
| 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 |
| IFBench | Not measured | 81.4% | Only Nemotron 3 Ultra measured |
| Long context reasoning (AA-LCR) | Not measured | 79.3% | Only Nemotron 3 Ultra measured |
| DTBench | 90.7% | 90.1% | DeepSeek V4 Pro 0423 |
| LMCA | 41.2% | 36.9% | DeepSeek V4 Pro 0423 |
| 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 | 1445 | DeepSeek V4 Pro 0423 |
| LMArena Text: instruction following | 1445 | 1418 | DeepSeek V4 Pro 0423 |
| LMArena Text: creative writing | 1446 | 1399 | DeepSeek V4 Pro 0423 |
| LMArena Text: English | 1459 | 1455 | DeepSeek V4 Pro 0423 |
| LMArena Text: non-English | 1439 | 1427 | DeepSeek V4 Pro 0423 |
| LMArena Text: Chinese | 1481 | 1497 | Nemotron 3 Ultra |
| LMArena Text: German | 1458 | 1472 | Nemotron 3 Ultra |
| LMArena Text: Japanese | 1446 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| LMArena Text: Korean | 1409 | 1384 | DeepSeek V4 Pro 0423 |
| LMArena Text: French | 1473 | 1472 | DeepSeek V4 Pro 0423 |
| LMArena Text: Spanish | 1457 | 1455 | DeepSeek V4 Pro 0423 |
| LMArena Text: Russian | 1453 | 1417 | DeepSeek V4 Pro 0423 |
| LMArena Text: Polish | 1461 | 1385 | DeepSeek V4 Pro 0423 |
| LMArena Text: multi-turn | 1468 | 1411 | DeepSeek V4 Pro 0423 |
| LMArena Text: longer queries | 1458 | 1437 | DeepSeek V4 Pro 0423 |
| LMArena Text: writing and language | 1447 | 1406 | DeepSeek V4 Pro 0423 |
| LMArena Text: entertainment and media | 1431 | 1397 | DeepSeek V4 Pro 0423 |
| EQ-Bench 4 | 1166 | Not measured | Only DeepSeek V4 Pro 0423 measured |
| Neither model has a result on the other 22 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V4 Pro 0423 | Nemotron 3 Ultra | 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 Ultra costs less
Repository review
50K input + 3K output tokens
Nemotron 3 Ultra costs less
Agent loop
200K input + 10K output tokens
Nemotron 3 Ultra 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 Ultra |
|---|---|---|
| Maker | DeepSeek | NVIDIA |
| Weights | Open weights | Open weights |
| Released | April 24, 2026 | June 4, 2026 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $1.30 | $0.60 |
| Output $/Mtok | $2.60 | $2.40 |
| Coding index | 38.7 | 30.0 |
| Independent measurements behind it | 5 | 3 |
| Cost per solved task | $0.57 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V4 Pro 0423 better than Nemotron 3 Ultra for coding?
Pick DeepSeek V4 Pro 0423 for coding: it scores 38.7 on the Vibe Coding Index against 30.0 for Nemotron 3 Ultra. Pick Nemotron 3 Ultra for output tokens 1.1x cheaper ($2.40 vs $2.60 per million) and work measured by Terminal-Bench 2.1 (Vals AI) or Mystery game puzzles, where it leads.
Which is cheaper, DeepSeek V4 Pro 0423 or Nemotron 3 Ultra?
DeepSeek V4 Pro 0423 costs $1.30 per million input tokens and $2.60 per million output tokens; Nemotron 3 Ultra costs $0.60 and $2.40. 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 Ultra?
DeepSeek V4 Pro 0423, with 1.05M tokens against 262K.
How many benchmarks is the DeepSeek V4 Pro 0423 vs Nemotron 3 Ultra comparison based on?
62 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 Ultra's on 3. Where only one model has a result, the page says so instead of naming a winner.