GPT-4o (2024-11-20) vs Nemotron 3.5 Super VL
Nemotron 3.5 Super VL leads GPT-4o (2024-11-20) on the Artificial Analysis Intelligence Index, 26.5 to 8.4, and Nemotron 3.5 Super VL costs 20.0x less per output token ($0.50 vs $10.00 per million). Updated October 23, 2026; 1 benchmarks have a result for both.
GPT-4o (2024-11-20) 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 GPT-4o (2024-11-20) for work measured by AA Intelligence Index, where it leads.
- Shared results
- 1
- GPT-4o (2024-11-20) only
- 23
- Nemotron 3.5 Super VL only
- 15
- Like-for-like categories
- 1 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 8.4.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-4o (2024-11-20) has a GPQA Diamond result, 53.8%.
Directional only
Math
AIME
No call
Only GPT-4o (2024-11-20) has a AIME result, 11.9%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-4o (2024-11-20) has a MMLU Pro result, 72.6%.
Directional only
Web design
Design Arena: websites
No call
Only Nemotron 3.5 Super VL has a Design Arena: websites result, 1172.
Directional only
Long documents
Listed context window
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL takes 1.05M tokens against 128K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Super VL
GPT-4o (2024-11-20) $0.0075, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-4o (2024-11-20) not benchmarked, Nemotron 3.5 Super VL not benchmarked.
- Widest category
- Intelligence, 18.1 points apart on average over 1 shared benchmark.
- Widest single gap
- AA Intelligence Index: GPT-4o (2024-11-20) 8.4 against Nemotron 3.5 Super VL 26.5, 18.1 points apart on a 0 to 100 scale.
- Output price
- GPT-4o (2024-11-20) $10.00 against Nemotron 3.5 Super VL $0.50 per million output tokens, 20.0x apart.
- Context window
- GPT-4o (2024-11-20) takes 128K 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 | GPT-4o (2024-11-20) | Bars | Nemotron 3.5 Super VL | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| All shared boards | 0 | 1 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index7 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Aider polyglot | 18.2% | Not measured | Only GPT-4o (2024-11-20) measured |
| LiveBench coding | Not measured | 65.5% | Only Nemotron 3.5 Super VL measured |
| LiveCodeBench (Vals AI) | 43.4% | Not measured | Only GPT-4o (2024-11-20) measured |
| SciCode | Not measured | 47.8% | Only Nemotron 3.5 Super VL measured |
| GSO-Bench | 0.0% | Not measured | Only GPT-4o (2024-11-20) measured |
| WeirdML | 25.1% | Not measured | Only GPT-4o (2024-11-20) measured |
| Cybench | 12.5% | Not measured | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 36 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o (2024-11-20) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 8.4 | 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 index4 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| 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 |
| TheAgentCompany | 8.6% | Not measured | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| GPQA Diamond | 53.8% | Not measured | Only GPT-4o (2024-11-20) measured |
| LiveBench reasoning | Not measured | 78.8% | Only Nemotron 3.5 Super VL measured |
| MMMU Pro (Vals AI) | 62.2% | Not measured | Only GPT-4o (2024-11-20) measured |
| Humanity's Last Exam (Artificial Analysis run) | Not measured | 30.7% | Only Nemotron 3.5 Super VL measured |
| GPQA Diamond (Epoch AI run) | 47.9% | Not measured | Only GPT-4o (2024-11-20) measured |
| VPCT | 40.0% | Not measured | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 17 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index5 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AIME | 11.9% | Not measured | Only GPT-4o (2024-11-20) measured |
| MATH 500 (Vals AI) | 74.0% | Not measured | Only GPT-4o (2024-11-20) measured |
| MGSM (Vals AI) | 90.4% | Not measured | Only GPT-4o (2024-11-20) measured |
| OTIS Mock AIME | 6.3% | Not measured | Only GPT-4o (2024-11-20) measured |
| MATH level 5 | 49.8% | Not measured | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 9 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index7 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| MMLU Pro | 72.6% | Not measured | Only GPT-4o (2024-11-20) measured |
| LegalBench (Vals AI) | 82.2% | Not measured | Only GPT-4o (2024-11-20) measured |
| CaseLaw (Vals AI) | 59.7% | Not measured | Only GPT-4o (2024-11-20) measured |
| CorpFin (Vals AI) | 45.9% | Not measured | Only GPT-4o (2024-11-20) measured |
| TaxEval (Vals AI) | 74.5% | Not measured | Only GPT-4o (2024-11-20) measured |
| MortgageTax (Vals AI) | 57.4% | Not measured | Only GPT-4o (2024-11-20) measured |
| GeoBench | 71.0% | Not measured | Only GPT-4o (2024-11-20) measured |
| Neither model has a result on the other 13 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | Not measured | 1172 | Only Nemotron 3.5 Super VL measured |
| Design Arena: UI components | Not measured | 1148 | Only Nemotron 3.5 Super VL measured |
| Design Arena: game dev | Not measured | 1110 | Only Nemotron 3.5 Super VL measured |
| Design Arena: 3D | Not measured | 1127 | Only Nemotron 3.5 Super VL measured |
| Design Arena: all code categories | Not measured | 1154 | Only Nemotron 3.5 Super VL measured |
| LiveBench design | Not measured | 60.7% | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index3 boards, none shared
| Benchmark | GPT-4o (2024-11-20) | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 64.6% | Only Nemotron 3.5 Super VL measured |
| Long context reasoning (AA-LCR) | Not measured | 69.0% | Only Nemotron 3.5 Super VL measured |
| Creative writing (Lech Mazur) | 81.8% | Not measured | Only GPT-4o (2024-11-20) measured |
| 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 | GPT-4o (2024-11-20) | 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 | OpenAI GPT-4o (2024-11-20) | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | OpenAI | NVIDIA |
| Weights | No public weights listed | Open weights |
| Released | November 20, 2024 | October 15, 2026 |
| Context window | 128K | 1.05M |
| Input $/Mtok | $2.50 | $0.15 |
| Output $/Mtok | $10.00 | $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 GPT-4o (2024-11-20) 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 GPT-4o (2024-11-20) for work measured by AA Intelligence Index, where it leads.
Which is cheaper, GPT-4o (2024-11-20) or Nemotron 3.5 Super VL?
GPT-4o (2024-11-20) costs $2.50 per million input tokens and $10.00 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, GPT-4o (2024-11-20) or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL, with 1.05M tokens against 128K.
How many benchmarks is the GPT-4o (2024-11-20) vs Nemotron 3.5 Super VL comparison based on?
1 benchmarks have a result for both models as of October 23, 2026. GPT-4o (2024-11-20)'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.