GPT-5 vs Nemotron 3.5 Super VL
Nemotron 3.5 Super VL leads GPT-5 on the Artificial Analysis Intelligence Index, 26.5 to 23.0, and Nemotron 3.5 Super VL costs 20.0x less per output token ($0.50 vs $10.00 per million). Updated October 22, 2026; 8 benchmarks have a result for both.
GPT-5 or Nemotron 3.5 Super VL: which to pick
Only GPT-5 has a coding score, 38.8 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 20.0x cheaper ($0.50 vs $10.00 per million) and a larger context window (1.05M against 400K tokens).
- Shared results
- 8
- GPT-5 only
- 105
- Nemotron 3.5 Super VL only
- 8
- Like-for-like categories
- 4 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 GPT-5 has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL leads, 26.5 against 23.0.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only GPT-5 has a GPQA Diamond result, 85.6%.
Directional only
Math
AIME
No call
Only GPT-5 has a AIME result, 93.4%.
Directional only
Knowledge
MMLU Pro
No call
Only GPT-5 has a MMLU Pro result, 86.5%.
Directional only
Web design
Design Arena: websites
GPT-5
GPT-5 leads, 1184 against 1172.
Same board, both measured
Long documents
Listed context window
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL takes 1.05M tokens against 400K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Super VL
GPT-5 $0.0063, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only GPT-5 has the number, $0.39.
Directional only
At a glance
- Coding index
- GPT-5 38.8 from 3 benchmarks, Nemotron 3.5 Super VL not benchmarked.
- Widest category
- General, 9.2 points apart on average over 1 shared benchmark.
- Widest single gap
- Design Arena: game dev: GPT-5 1185 against Nemotron 3.5 Super VL 1110, 9.4 points apart on a 0 to 100 scale.
- Output price
- GPT-5 $10.00 against Nemotron 3.5 Super VL $0.50 per million output tokens, 20.0x apart.
- Context window
- GPT-5 takes 400K 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-5 | Bars | Nemotron 3.5 Super VL | Ties |
|---|---|---|---|---|
| Intelligence | 0 | 1 | ||
| Reasoning | 0 | 1 | ||
| Web design | 4 | 1 | ||
| General | 1 | 0 | ||
| All shared boards | 5 | 3 |
CodingDirectional onlySix of these boards feed the Vibe Coding Index19 boards, none shared
| Benchmark | GPT-5 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Aider polyglot | 88.0% | Not measured | Only GPT-5 measured |
| SWE-bench Verified | 74.4% | Not measured | Only GPT-5 measured |
| LiveBench coding | Not measured | 65.5% | Only Nemotron 3.5 Super VL measured |
| LMArena WebDev | 1418 | Not measured | Only GPT-5 measured |
| AA Coding Index | 37.8 | Not measured | Only GPT-5 measured |
| LiveCodeBench (Vals AI) | 85.9% | Not measured | Only GPT-5 measured |
| SWE-bench Verified (Vals AI) | 69.0% | Not measured | Only GPT-5 measured |
| Vibe Code Bench (Vals AI) | 20.1% | Not measured | Only GPT-5 measured |
| LiveCodeBench (Artificial Analysis run) | 84.6% | Not measured | Only GPT-5 measured |
| SciCode | Not measured | 47.8% | Only Nemotron 3.5 Super VL measured |
| Terminal-Bench Hard (Artificial Analysis run) | 37.9% | Not measured | Only GPT-5 measured |
| Terminal-Bench | 49.6% | Not measured | Only GPT-5 measured |
| GSO-Bench | 6.9% | Not measured | Only GPT-5 measured |
| WeirdML | 60.7% | Not measured | Only GPT-5 measured |
| ALE-Bench | 807.6 | Not measured | Only GPT-5 measured |
| LMArena Text: coding | 1436 | Not measured | Only GPT-5 measured |
| LMArena Text: software and IT | 1420 | Not measured | Only GPT-5 measured |
| LMArena WebDev: HTML | 1427 | Not measured | Only GPT-5 measured |
| SciCode | 0.0% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 20 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 23.0 | 26.5 | Nemotron 3.5 Super VL |
| Epoch Capabilities Index | 150.0 | Not measured | Only GPT-5 measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index10 boards, none shared
| Benchmark | GPT-5 | 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 | 86.5% | Not measured | Only GPT-5 measured |
| METR time horizons | 69.6% | Not measured | Only GPT-5 measured |
| Remote Labor Index | 1.7% | Not measured | Only GPT-5 measured |
| GDPval | 34.8% | Not measured | Only GPT-5 measured |
| BALROG | 32.8% | Not measured | Only GPT-5 measured |
| DeepResearch Bench | 49.6% | Not measured | Only GPT-5 measured |
| BrowseComp-Plus | 80.0% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 31 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index01
| Benchmark | GPT-5 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| GPQA Diamond | 85.6% | Not measured | Only GPT-5 measured |
| LiveBench reasoning | Not measured | 78.8% | Only Nemotron 3.5 Super VL measured |
| MMMU Pro (Vals AI) | 81.5% | Not measured | Only GPT-5 measured |
| GPQA Diamond (Artificial Analysis run) | 85.4% | Not measured | Only GPT-5 measured |
| Humanity's Last Exam (Artificial Analysis run) | 28.5% | 30.7% | Nemotron 3.5 Super VL |
| GPQA Diamond (Epoch AI run) | 86.2% | Not measured | Only GPT-5 measured |
| ARC-AGI | 65.7% | Not measured | Only GPT-5 measured |
| ARC-AGI-2 | 9.9% | Not measured | Only GPT-5 measured |
| SimpleBench | 56.7% | Not measured | Only GPT-5 measured |
| Chess puzzles | 36.0% | Not measured | Only GPT-5 measured |
| Mystery game puzzles | 23.0% | Not measured | Only GPT-5 measured |
| EBR-bench | 12.7% | Not measured | Only GPT-5 measured |
| VPCT | 66.0% | Not measured | Only GPT-5 measured |
| LMArena Text: hard prompts | 1415 | Not measured | Only GPT-5 measured |
| LMArena Text: expert | 1419 | Not measured | Only GPT-5 measured |
| LMArena Text: hard prompts in English | 1419 | Not measured | Only GPT-5 measured |
| LMArena Vision: diagrams | 1252 | Not measured | Only GPT-5 measured |
| LMArena Vision: homework | 1278 | Not measured | Only GPT-5 measured |
| Kagi LLM Benchmark | 72.7% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 3 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index12 boards, none shared
| Benchmark | GPT-5 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AIME | 93.4% | Not measured | Only GPT-5 measured |
| MATH 500 (Vals AI) | 96.0% | Not measured | Only GPT-5 measured |
| MGSM (Vals AI) | 92.8% | Not measured | Only GPT-5 measured |
| AA Math Index | 94.3 | Not measured | Only GPT-5 measured |
| AIME 2025 (Artificial Analysis run) | 94.3% | Not measured | Only GPT-5 measured |
| OTIS Mock AIME | 91.4% | Not measured | Only GPT-5 measured |
| FrontierMath, tiers 1 to 3 | 55.4% | Not measured | Only GPT-5 measured |
| FrontierMath, tier 4 | 22.0% | Not measured | Only GPT-5 measured |
| ProofBench | 18.0% | Not measured | Only GPT-5 measured |
| MATH level 5 | 98.1% | Not measured | Only GPT-5 measured |
| LMArena Text: math | 1407 | Not measured | Only GPT-5 measured |
| LMArena Text: mathematical work | 1405 | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 1 board here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index16 boards, none shared
| Benchmark | GPT-5 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| MMLU Pro | 86.5% | Not measured | Only GPT-5 measured |
| LegalBench (Vals AI) | 86.0% | Not measured | Only GPT-5 measured |
| CaseLaw (Vals AI) | 66.5% | Not measured | Only GPT-5 measured |
| CorpFin (Vals AI) | 61.1% | Not measured | Only GPT-5 measured |
| TaxEval (Vals AI) | 73.4% | Not measured | Only GPT-5 measured |
| MortgageTax (Vals AI) | 65.5% | Not measured | Only GPT-5 measured |
| MedQA (Vals AI) | 96.3% | Not measured | Only GPT-5 measured |
| MedCode (Vals AI) | 49.6% | Not measured | Only GPT-5 measured |
| MMLU Pro (Artificial Analysis run) | 87.1% | Not measured | Only GPT-5 measured |
| SimpleQA Verified | 50.1% | Not measured | Only GPT-5 measured |
| GeoBench | 81.0% | Not measured | Only GPT-5 measured |
| LMArena Text: science | 1416 | Not measured | Only GPT-5 measured |
| LMArena Text: medicine and healthcare | 1413 | Not measured | Only GPT-5 measured |
| LMArena Text: legal and government | 1431 | Not measured | Only GPT-5 measured |
| LMArena Text: business and finance | 1400 | Not measured | Only GPT-5 measured |
| FACTS Benchmark Suite | 56.5% | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 2 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index41
| Benchmark | GPT-5 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | 1184 | 1172 | GPT-5 |
| Design Arena: UI components | 1177 | 1153 | GPT-5 |
| Design Arena: game dev | 1185 | 1110 | GPT-5 |
| Design Arena: data viz | 1221 | Not measured | Only GPT-5 measured |
| Design Arena: 3D | 1055 | 1127 | Nemotron 3.5 Super VL |
| Design Arena: all code categories | 1167 | 1155 | GPT-5 |
| Design Arena: SVG | 1188 | Not measured | Only GPT-5 measured |
| Design Arena: ASCII art | 1141 | Not measured | Only GPT-5 measured |
| LiveBench design | Not measured | 60.7% | Only Nemotron 3.5 Super VL measured |
GeneralLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-5 | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | Not measured | 64.6% | Only Nemotron 3.5 Super VL measured |
| SAGE (Vals AI) | 43.7% | Not measured | Only GPT-5 measured |
| MedScribe (Vals AI) | 83.7% | Not measured | Only GPT-5 measured |
| IFBench | 73.1% | Not measured | Only GPT-5 measured |
| Long context reasoning (AA-LCR) | 78.2% | 69.0% | GPT-5 |
| DTBench | 90.7% | Not measured | Only GPT-5 measured |
| LMCA | 40.0% | Not measured | Only GPT-5 measured |
| Fiction.LiveBench | 97.2% | Not measured | Only GPT-5 measured |
| Creative writing (Lech Mazur) | 86.0% | Not measured | Only GPT-5 measured |
| LMArena Text | 1406 | Not measured | Only GPT-5 measured |
| LMArena Text: instruction following | 1388 | Not measured | Only GPT-5 measured |
| LMArena Text: creative writing | 1365 | Not measured | Only GPT-5 measured |
| LMArena Vision | 1232 | Not measured | Only GPT-5 measured |
| LMArena Text: English | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: non-English | 1397 | Not measured | Only GPT-5 measured |
| LMArena Text: Chinese | 1422 | Not measured | Only GPT-5 measured |
| LMArena Text: German | 1416 | Not measured | Only GPT-5 measured |
| LMArena Text: Japanese | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: Korean | 1360 | Not measured | Only GPT-5 measured |
| LMArena Text: French | 1409 | Not measured | Only GPT-5 measured |
| LMArena Text: Spanish | 1399 | Not measured | Only GPT-5 measured |
| LMArena Text: Russian | 1407 | Not measured | Only GPT-5 measured |
| LMArena Text: Polish | 1431 | Not measured | Only GPT-5 measured |
| LMArena Text: multi-turn | 1426 | Not measured | Only GPT-5 measured |
| LMArena Text: longer queries | 1399 | Not measured | Only GPT-5 measured |
| LMArena Text: writing and language | 1384 | Not measured | Only GPT-5 measured |
| LMArena Text: entertainment and media | 1379 | Not measured | Only GPT-5 measured |
| LMArena Vision: English | 1231 | Not measured | Only GPT-5 measured |
| LMArena Vision: Chinese | 1267 | Not measured | Only GPT-5 measured |
| LMArena Vision: reading text | 1246 | Not measured | Only GPT-5 measured |
| LMArena Vision: humor | 1219 | Not measured | Only GPT-5 measured |
| LMArena Vision: captioning | 1212 | Not measured | Only GPT-5 measured |
| LMArena Vision: recognizing things | 1263 | Not measured | Only GPT-5 measured |
| LMArena Vision: creative writing | 1210 | Not measured | Only GPT-5 measured |
| Neither model has a result on the other 12 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-5 | 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-5 | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | OpenAI | NVIDIA |
| Weights | No public weights listed | Open weights |
| Released | August 7, 2025 | October 15, 2026 |
| Context window | 400K | 1.05M |
| Input $/Mtok | $1.25 | $0.15 |
| Output $/Mtok | $10.00 | $0.50 |
| Coding index | 38.8 | Not benchmarked |
| Independent measurements behind it | 3 | 1 |
| Cost per solved task | $0.39 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-5 better than Nemotron 3.5 Super VL for coding?
Only GPT-5 has a coding score, 38.8 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 20.0x cheaper ($0.50 vs $10.00 per million) and a larger context window (1.05M against 400K tokens).
Which is cheaper, GPT-5 or Nemotron 3.5 Super VL?
GPT-5 costs $1.25 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 GPT-5 only.
Which has the bigger context window, GPT-5 or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL, with 1.05M tokens against 400K.
How many benchmarks is the GPT-5 vs Nemotron 3.5 Super VL comparison based on?
8 benchmarks have a result for both models as of October 22, 2026. GPT-5's coding index rests on 3 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.