DeepSeek V3.1 Terminus vs Nemotron 3.5 Super VL
Nemotron 3.5 Super VL leads DeepSeek V3.1 Terminus on the Artificial Analysis Intelligence Index, 26.5 to 14.8, and Nemotron 3.5 Super VL costs 2.0x less per output token ($0.50 vs $1.0142 per million). Updated October 22, 2026; 9 benchmarks have a result for both.
DeepSeek V3.1 Terminus or Nemotron 3.5 Super VL: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick DeepSeek V3.1 Terminus for work measured by SciCode or AA Intelligence Index, where it leads.
- Shared results
- 9
- DeepSeek V3.1 Terminus only
- 36
- Nemotron 3.5 Super VL only
- 7
- 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
No call
Neither has a coding score yet.
One side not measured
Intelligence
AA Intelligence Index
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL leads, 26.5 against 14.8.
Same board, both measured
Agentic work
AA Agentic Index
No call
Only DeepSeek V3.1 Terminus has a AA Agentic Index result, 6.8.
Directional only
Web design
Design Arena: websites
DeepSeek V3.1 Terminus
DeepSeek V3.1 Terminus leads, 1187 against 1173.
Same board, both measured
Long documents
Listed context window
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL takes 1.05M tokens against 164K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Super VL
DeepSeek V3.1 Terminus $0.0008, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
At a glance
- Coding index
- DeepSeek V3.1 Terminus not benchmarked, Nemotron 3.5 Super VL not benchmarked.
- Widest category
- Reasoning, 14.3 points apart on average over 1 shared benchmark.
- Widest single gap
- Humanity's Last Exam (Artificial Analysis run): DeepSeek V3.1 Terminus 16.4% against Nemotron 3.5 Super VL 30.7%, 14.3 points apart on a 0 to 100 scale.
- Output price
- DeepSeek V3.1 Terminus $1.0142 against Nemotron 3.5 Super VL $0.50 per million output tokens, 2.0x apart.
- Context window
- DeepSeek V3.1 Terminus takes 164K tokens, Nemotron 3.5 Super VL 1.05M.
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 V3.1 Terminus | Bars | Nemotron 3.5 Super VL | Ties |
|---|---|---|---|---|
| Coding | 0 | 1 | ||
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Web design | 5 | 0 | ||
| General | 1 | 0 | ||
| All shared boards | 6 | 3 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index01
| Benchmark | DeepSeek V3.1 Terminus | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench coding | Not measured | 65.5% | Only Nemotron 3.5 Super VL measured |
| AA Coding Index | 43.5 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LiveCodeBench (Artificial Analysis run) | 79.8% | Not measured | Only DeepSeek V3.1 Terminus measured |
| SciCode | 38.0% | 47.8% | Nemotron 3.5 Super VL |
| Terminal-Bench Hard (Artificial Analysis run) | 31.8% | Not measured | Only DeepSeek V3.1 Terminus measured |
| ALE-Bench | 745.2 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: coding | 1425 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: software and IT | 1426 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 35 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 Terminus | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 14.8 | 26.5 | Nemotron 3.5 Super VL |
| Neither model has a result on the other 1 board here | |||
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Agentic Index | 6.8 | Not measured | Only DeepSeek V3.1 Terminus 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 |
| τ²-Bench Telecom | 37.1% | Not measured | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | DeepSeek V3.1 Terminus | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 78.8% | Only Nemotron 3.5 Super VL measured |
| GPQA Diamond (Artificial Analysis run) | 79.2% | Not measured | Only DeepSeek V3.1 Terminus measured |
| Humanity's Last Exam (Artificial Analysis run) | 16.4% | 30.7% | Nemotron 3.5 Super VL |
| LMArena Text: hard prompts | 1426 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: hard prompts in English | 1432 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Kagi LLM Benchmark | 57.4% | Not measured | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Math Index | 89.7 | Not measured | Only DeepSeek V3.1 Terminus measured |
| AIME 2025 (Artificial Analysis run) | 89.7% | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: math | 1402 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 85.1% | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: science | 1451 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: medicine and healthcare | 1468 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: legal and government | 1457 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: business and finance | 1414 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 15 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | DeepSeek V3.1 Terminus | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | 1187 | 1173 | DeepSeek V3.1 Terminus |
| Design Arena: UI components | 1180 | 1149 | DeepSeek V3.1 Terminus |
| Design Arena: game dev | 1131 | 1110 | DeepSeek V3.1 Terminus |
| Design Arena: data viz | 1168 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Design Arena: 3D | 1141 | 1128 | DeepSeek V3.1 Terminus |
| Design Arena: all code categories | 1175 | 1154 | DeepSeek V3.1 Terminus |
| Design Arena: SVG | 1065 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LiveBench design | Not measured | 60.7% | Only Nemotron 3.5 Super VL 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 V3.1 Terminus | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 64.6% | Only Nemotron 3.5 Super VL measured |
| IFBench | 57.0% | Not measured | Only DeepSeek V3.1 Terminus measured |
| Long context reasoning (AA-LCR) | 69.3% | 69.0% | DeepSeek V3.1 Terminus |
| DTBench | 81.3% | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMCA | 28.6% | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text | 1419 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: instruction following | 1404 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: creative writing | 1403 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: English | 1429 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: non-English | 1408 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: Russian | 1435 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: Polish | 1427 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: multi-turn | 1411 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: longer queries | 1422 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: writing and language | 1409 | Not measured | Only DeepSeek V3.1 Terminus measured |
| LMArena Text: entertainment and media | 1391 | Not measured | Only DeepSeek V3.1 Terminus measured |
| Neither model has a result on the other 32 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | DeepSeek V3.1 Terminus | 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 V3.1 Terminus | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | DeepSeek | NVIDIA |
| Weights | Open weights | Open weights |
| Released | September 22, 2025 | October 15, 2026 |
| Context window | 164K | 1.05M |
| Input $/Mtok | $0.3063 | $0.15 |
| Output $/Mtok | $1.0142 | $0.50 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 0 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is DeepSeek V3.1 Terminus better than Nemotron 3.5 Super VL for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick DeepSeek V3.1 Terminus for work measured by SciCode or AA Intelligence Index, where it leads.
Which is cheaper, DeepSeek V3.1 Terminus or Nemotron 3.5 Super VL?
DeepSeek V3.1 Terminus costs $0.3063 per million input tokens and $1.0142 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 V3.1 Terminus or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL, with 1.05M tokens against 164K.
How many benchmarks is the DeepSeek V3.1 Terminus vs Nemotron 3.5 Super VL comparison based on?
9 benchmarks have a result for both models as of October 22, 2026. DeepSeek V3.1 Terminus's coding index rests on 0 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.