GPT-4o vs Nemotron 3.5 Super VL
Updated October 21, 2026. 7 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
GPT-4o scores 38.8 and Nemotron 3.5 Super VL 65.5 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking.
Pick Nemotron 3.5 Super VL when output tokens cost 20.0x less, or when the context window is larger, 1.05M against 128K tokens.
- Shared results
- 7
- GPT-4o only
- 19
- Nemotron 3.5 Super VL only
- 9
- Like-for-like categories
- 3 of 9
Shared results can support a head-to-head reading. Results only one model has describe coverage, not superiority.
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, four public coding benchmarks
No call
38.8 against 65.5, from different benchmarks with none in common.
Directional only
Intelligence
AA Intelligence Index
No call
Only Nemotron 3.5 Super VL has a AA Intelligence Index result, 26.5.
Directional only
Web design
Design Arena: websites
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL leads, 1171 against 831.
Same board, both measured
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 $0.0075, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-4o 38.8 from 1 benchmark, Nemotron 3.5 Super VL 65.5 from 1 benchmark.
- Widest category
- Web design, 34.0 points apart on average over 5 shared benchmarks.
- Widest single gap
- Design Arena: websites: GPT-4o 831 against Nemotron 3.5 Super VL 1171, 42.5 points apart on a 0 to 100 scale.
- Output price
- GPT-4o $10.00 against Nemotron 3.5 Super VL $0.50 per million output tokens, 20.0x apart.
- Context window
- GPT-4o 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 | Bars | Nemotron 3.5 Super VL | Ties |
|---|---|---|---|---|
| Reasoning | 0 | 1 | ||
| Web design | 0 | 5 | ||
| General | 0 | 1 | ||
| All shared boards | 0 | 7 |
CodingDirectional onlyFour of these boards feed the Vibe Coding Index8 boards, none shared
| Benchmark | GPT-4o | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| SWE-bench Verified | 38.8% | Not measured | Only GPT-4o measured |
| SWE-bench Lite | 38.0% | Not measured | Only GPT-4o measured |
| SWE-bench (full test set) | 18.8% | Not measured | Only GPT-4o measured |
| SWE-bench Multimodal | 30.4% | Not measured | Only GPT-4o measured |
| LiveBench coding | Not measured | 65.5% | Only Nemotron 3.5 Super VL measured |
| LiveCodeBench (Artificial Analysis run) | 30.9% | Not measured | Only GPT-4o measured |
| SciCode | Not measured | 47.8% | Only Nemotron 3.5 Super VL measured |
| Terminal-Bench Hard (Artificial Analysis run) | 8.3% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 30 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | GPT-4o | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | Not measured | 26.5 | Only Nemotron 3.5 Super VL measured |
| 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 | GPT-4o | 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 |
| τ²-Bench Telecom | 25.1% | Not measured | Only GPT-4o measured |
| GDPval | 9.9% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 34 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench reasoning | Not measured | 78.8% | Only Nemotron 3.5 Super VL measured |
| GPQA Diamond (Artificial Analysis run) | 54.3% | Not measured | Only GPT-4o measured |
| Humanity's Last Exam (Artificial Analysis run) | 2.4% | 30.7% | Nemotron 3.5 Super VL |
| ARC-AGI | 4.5% | Not measured | Only GPT-4o measured |
| ARC-AGI-2 | 0.0% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 16 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Math Index | 6.0 | Not measured | Only GPT-4o measured |
| AIME 2025 (Artificial Analysis run) | 6.0% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 11 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | GPT-4o | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | 74.8% | Not measured | Only GPT-4o measured |
| MMLU | 88.1% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 15 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index05
| Benchmark | GPT-4o | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | 831 | 1171 | Nemotron 3.5 Super VL |
| Design Arena: UI components | 885 | 1148 | Nemotron 3.5 Super VL |
| Design Arena: game dev | 906 | 1108 | Nemotron 3.5 Super VL |
| Design Arena: data viz | 858 | Not measured | Only GPT-4o measured |
| Design Arena: 3D | 869 | 1132 | Nemotron 3.5 Super VL |
| Design Arena: all code categories | 861 | 1155 | Nemotron 3.5 Super VL |
| LiveBench design | Not measured | 60.7% | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-4o | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 64.6% | Only Nemotron 3.5 Super VL measured |
| IFBench | 34.3% | Not measured | Only GPT-4o measured |
| Long context reasoning (AA-LCR) | 49.3% | 69.0% | Nemotron 3.5 Super VL |
| DTBench | 64.5% | Not measured | Only GPT-4o measured |
| LMCA | 16.6% | Not measured | Only GPT-4o measured |
| Neither model has a result on the other 41 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-4o | 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 | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | OpenAI | NVIDIA |
| Weights | No public weights listed | Open weights |
| Released | May 13, 2024 | October 15, 2026 |
| Context window | 128K | 1.05M |
| Input $/Mtok | $2.50 | $0.15 |
| Output $/Mtok | $10.00 | $0.50 |
| Coding index | 38.8 | 65.5 |
| Independent measurements behind it | 1 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-4o better than Nemotron 3.5 Super VL for coding?
GPT-4o scores 38.8 and Nemotron 3.5 Super VL 65.5 on the Vibe Coding Index, but no coding benchmark measured both, so the gap is directional, not a ranking. Pick Nemotron 3.5 Super VL when output tokens cost 20.0x less, or when the context window is larger, 1.05M against 128K tokens.
Which is cheaper, GPT-4o or Nemotron 3.5 Super VL?
GPT-4o 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 or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL, with 1.05M tokens against 128K.
How many benchmarks is the GPT-4o vs Nemotron 3.5 Super VL comparison based on?
7 benchmarks have a result for both models as of October 21, 2026. GPT-4o's coding index rests on 1 independent benchmark 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.