GPT-6.1 Sol vs Nemotron 3.5 Super VL
Updated October 21, 2026. 16 benchmarks have a result for both. Coding is ranked on the Vibe Coding Index; every other board is shown as published.
Decision reading
Pick GPT-6.1 Sol for coding: it scores 88.7 on the Vibe Coding Index against 65.5 for Nemotron 3.5 Super VL.
Pick Nemotron 3.5 Super VL when output tokens cost 20.0x less.
- Shared results
- 16
- GPT-6.1 Sol only
- 79
- Nemotron 3.5 Super VL only
- 0
- Like-for-like categories
- 6 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
GPT-6.1 Sol
GPT-6.1 Sol scores 88.7 against 65.5.
2 and 1 benchmarks behind the scores
Intelligence
AA Intelligence Index
GPT-6.1 Sol
GPT-6.1 Sol leads, 51.8 against 26.5.
Same board, both measured
Web design
Design Arena: websites
GPT-6.1 Sol
GPT-6.1 Sol leads, 1321 against 1171.
Same board, both measured
General
Vals Index
No call
Only GPT-6.1 Sol has a Vals Index result, 63.9%.
Directional only
Long documents
Listed context window
GPT-6.1 Sol
GPT-6.1 Sol takes 1.05M tokens against 1.05M.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Super VL
GPT-6.1 Sol $0.0070, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
At a glance
- Coding index
- GPT-6.1 Sol 88.7 from 2 benchmarks, Nemotron 3.5 Super VL 65.5 from 1 benchmark.
- Widest category
- Agentic work, 51.3 points apart on average over 3 shared benchmarks.
- Widest single gap
- LiveBench browser: GPT-6.1 Sol 88.0% against Nemotron 3.5 Super VL 4.0%, 84.0 points apart on a 0 to 100 scale.
- Output price
- GPT-6.1 Sol $10.00 against Nemotron 3.5 Super VL $0.50 per million output tokens, 20.0x apart.
- Context window
- GPT-6.1 Sol takes 1.05M 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-6.1 Sol | Bars | Nemotron 3.5 Super VL | Ties |
|---|---|---|---|---|
| Coding | 2 | 0 | ||
| Intelligence | 1 | 0 | ||
| Agentic work | 3 | 0 | ||
| Reasoning | 2 | 0 | ||
| Web design | 6 | 0 | ||
| General | 2 | 0 | ||
| All shared boards | 16 | 0 |
CodingLike-for-likeFour of these boards feed the Vibe Coding Index20
| Benchmark | GPT-6.1 Sol | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench coding | 96.0% | 65.5% | GPT-6.1 Sol |
| LMArena WebDev | 1753 | Not measured | Only GPT-6.1 Sol measured |
| Terminal-Bench 4 (Vals AI) | 55.0% | Not measured | Only GPT-6.1 Sol measured |
| Vibe Code Bench (Vals AI) | 88.9% | Not measured | Only GPT-6.1 Sol measured |
| Code migration (Vals AI) | 65.1% | Not measured | Only GPT-6.1 Sol measured |
| ProgramBench (Vals AI) | 3.0% | Not measured | Only GPT-6.1 Sol measured |
| IOI (Vals AI) | 96.9% | Not measured | Only GPT-6.1 Sol measured |
| SWE-bench Multimodal (Vals AI) | 43.5% | Not measured | Only GPT-6.1 Sol measured |
| CyberBench (Vals AI) | 39.3% | Not measured | Only GPT-6.1 Sol measured |
| SciCode | 55.8% | 47.8% | GPT-6.1 Sol |
| FrontierCode | 50.2% | Not measured | Only GPT-6.1 Sol measured |
| ALE-Bench | 2743.8 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: coding | 1478 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: software and IT | 1476 | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev: React | 1754 | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev: HTML | 1800 | Not measured | Only GPT-6.1 Sol measured |
| LMArena WebDev: from an image | 1702 | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 21 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | GPT-6.1 Sol | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 51.8 | 26.5 | GPT-6.1 Sol |
| Epoch Capabilities Index | 168.4 | Not measured | Only GPT-6.1 Sol measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | GPT-6.1 Sol | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: agents building mobile apps | 1190 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: spreadsheets | 1250 | Not measured | Only GPT-6.1 Sol measured |
| LiveBench research | 83.5% | 70.9% | GPT-6.1 Sol |
| LiveBench browser | 88.0% | 4.0% | GPT-6.1 Sol |
| LiveBench long agentic | 73.0% | 15.8% | GPT-6.1 Sol |
| SREBench (Vals AI) | 50.8% | Not measured | Only GPT-6.1 Sol measured |
| Finance Agent (Vals AI) | 52.0% | Not measured | Only GPT-6.1 Sol measured |
| Tax Agent Bench (Vals AI) | 21.8% | Not measured | Only GPT-6.1 Sol measured |
| Legal Research Bench (Vals AI) | 38.5% | Not measured | Only GPT-6.1 Sol measured |
| Harvey's Legal Agent Benchmark (Vals AI) | 6.3% | Not measured | Only GPT-6.1 Sol measured |
| Excel Modeling Benchmark (Vals AI) | 70.8% | Not measured | Only GPT-6.1 Sol measured |
| BioMysteryBench (Vals AI) | 79.6% | Not measured | Only GPT-6.1 Sol measured |
| Terminal-Bench-Science (Vals AI) | 52.9% | Not measured | Only GPT-6.1 Sol measured |
| Vals Science Index | 59.6% | Not measured | Only GPT-6.1 Sol measured |
| Robo-bench (Vals AI) | 28.3% | Not measured | Only GPT-6.1 Sol measured |
| APEX-Agents | 60.0% | Not measured | Only GPT-6.1 Sol measured |
| SpireClimb | 24.7% | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 22 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6.1 Sol | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench reasoning | 92.6% | 78.8% | GPT-6.1 Sol |
| MysteryMechanism (Vals AI) | 46.4% | Not measured | Only GPT-6.1 Sol measured |
| Humanity's Last Exam (Artificial Analysis run) | 52.9% | 30.7% | GPT-6.1 Sol |
| GPQA Diamond (Epoch AI run) | 95.4% | Not measured | Only GPT-6.1 Sol measured |
| ARC-AGI | 98.5% | Not measured | Only GPT-6.1 Sol measured |
| ARC-AGI-2 | 94.2% | Not measured | Only GPT-6.1 Sol measured |
| Chess puzzles | 68.0% | Not measured | Only GPT-6.1 Sol measured |
| Mystery game puzzles | 80.0% | Not measured | Only GPT-6.1 Sol measured |
| EBR-bench | 54.3% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: hard prompts | 1463 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: expert | 1489 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: hard prompts in English | 1465 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: diagrams | 1306 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: homework | 1356 | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 7 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-6.1 Sol | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| OTIS Mock AIME | 100.0% | Not measured | Only GPT-6.1 Sol measured |
| FrontierMath, tiers 1 to 3 | 93.7% | Not measured | Only GPT-6.1 Sol measured |
| FrontierMath, tier 4 | 100.0% | Not measured | Only GPT-6.1 Sol measured |
| ProofBench | 99.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: math | 1489 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: mathematical work | 1513 | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index6 boards, none shared
| Benchmark | GPT-6.1 Sol | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| MedCode (Vals AI) | 48.8% | Not measured | Only GPT-6.1 Sol measured |
| SimpleQA Verified | 73.9% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: science | 1444 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: medicine and healthcare | 1433 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: legal and government | 1471 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: business and finance | 1445 | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 11 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | GPT-6.1 Sol | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | 1321 | 1171 | GPT-6.1 Sol |
| Design Arena: UI components | 1321 | 1148 | GPT-6.1 Sol |
| Design Arena: game dev | 1389 | 1108 | GPT-6.1 Sol |
| Design Arena: data viz | 1278 | Not measured | Only GPT-6.1 Sol measured |
| Design Arena: 3D | 1461 | 1132 | GPT-6.1 Sol |
| Design Arena: all code categories | 1345 | 1155 | GPT-6.1 Sol |
| LiveBench design | 82.1% | 60.7% | GPT-6.1 Sol |
| Neither model has a result on the other 2 boards here | |||
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | GPT-6.1 Sol | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Vals Index | 63.9% | Not measured | Only GPT-6.1 Sol measured |
| LiveBench data analysis | 82.7% | 64.6% | GPT-6.1 Sol |
| Public Benefits Bench (Vals AI) | 59.3% | Not measured | Only GPT-6.1 Sol measured |
| SAGE (Vals AI) | 46.5% | Not measured | Only GPT-6.1 Sol measured |
| MedScribe (Vals AI) | 86.5% | Not measured | Only GPT-6.1 Sol measured |
| Long context reasoning (AA-LCR) | 84.0% | 69.0% | GPT-6.1 Sol |
| DTBench | 96.5% | Not measured | Only GPT-6.1 Sol measured |
| LMCA | 62.8% | Not measured | Only GPT-6.1 Sol measured |
| Furniture assembly | 80.0% | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text | 1443 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: instruction following | 1466 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: creative writing | 1425 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision | 1288 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: English | 1450 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: non-English | 1433 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: Chinese | 1487 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: French | 1460 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: Spanish | 1439 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: Russian | 1457 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: multi-turn | 1442 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: longer queries | 1458 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: writing and language | 1444 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Text: entertainment and media | 1426 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: English | 1274 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: reading text | 1289 | Not measured | Only GPT-6.1 Sol measured |
| LMArena Vision: creative writing | 1301 | Not measured | Only GPT-6.1 Sol measured |
| Neither model has a result on the other 20 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | GPT-6.1 Sol | 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-6.1 Sol | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | OpenAI | NVIDIA |
| Weights | No public weights listed | Open weights |
| Released | September 29, 2026 | October 15, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $2.00 | $0.15 |
| Output $/Mtok | $10.00 | $0.50 |
| Coding index | 88.7 | 65.5 |
| Independent measurements behind it | 2 | 1 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is GPT-6.1 Sol better than Nemotron 3.5 Super VL for coding?
Pick GPT-6.1 Sol for coding: it scores 88.7 on the Vibe Coding Index against 65.5 for Nemotron 3.5 Super VL. Pick Nemotron 3.5 Super VL when output tokens cost 20.0x less.
Which is cheaper, GPT-6.1 Sol or Nemotron 3.5 Super VL?
GPT-6.1 Sol costs $2.00 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-6.1 Sol or Nemotron 3.5 Super VL?
GPT-6.1 Sol, with 1.05M tokens against 1.05M.
How many benchmarks is the GPT-6.1 Sol vs Nemotron 3.5 Super VL comparison based on?
16 benchmarks have a result for both models as of October 21, 2026. GPT-6.1 Sol's coding index rests on 2 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.