Gemini 3.7 Flash vs Nemotron 3.5 Super VL
Gemini 3.7 Flash leads Nemotron 3.5 Super VL on the Artificial Analysis Intelligence Index, 39.6 to 26.5, and Nemotron 3.5 Super VL costs 7.5x less per output token ($0.50 vs $3.75 per million). Updated October 22, 2026; 16 benchmarks have a result for both.
vs boards won of 16 shared
Gemini 3.7 Flash or Nemotron 3.5 Super VL: which to pick
Only Gemini 3.7 Flash has a coding score, 58.0 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 7.5x cheaper ($0.50 vs $3.75 per million).
Model AGemini 3.7 FlashGoogle58.0/1006 independent benchmarks$1.78 per solved taskModel BNemotron 3.5 Super VLNVIDIANot benchmarked1 independent benchmarkNo cost per solved task
- Shared results
- 16
- Gemini 3.7 Flash only
- 98
- Nemotron 3.5 Super VL only
- 0
- Like-for-like categories
- 6 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 Gemini 3.7 Flash has a coding score, so no call is made.
One side not measured
Intelligence
AA Intelligence Index
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 39.6 against 26.5.
Same board, both measured
Reasoning
GPQA Diamond
No call
Only Gemini 3.7 Flash has a GPQA Diamond result, 93.9%.
Directional only
Knowledge
MMLU Pro
No call
Only Gemini 3.7 Flash has a MMLU Pro result, 90.1%.
Directional only
Web design
Design Arena: websites
Gemini 3.7 Flash
Gemini 3.7 Flash leads, 1303 against 1172.
Same board, both measured
Long documents
Listed context window
No call
Both take 1.05M tokens.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Super VL
Gemini 3.7 Flash $0.0026, Nemotron 3.5 Super VL $0.0004 per turn.
List prices
Cost per solved task
One SWE-bench Verified task, solved
No call
Only Gemini 3.7 Flash has the number, $1.78.
Directional only
At a glance
- Coding index
- Gemini 3.7 Flash 58.0 from 6 benchmarks, Nemotron 3.5 Super VL not benchmarked.
- Widest category
- Agentic work, 23.4 points apart on average over 3 shared benchmarks.
- Widest single gap
- LiveBench browser: Gemini 3.7 Flash 56.0% against Nemotron 3.5 Super VL 4.0%, 52.0 points apart on a 0 to 100 scale.
- Output price
- Gemini 3.7 Flash $3.75 against Nemotron 3.5 Super VL $0.50 per million output tokens, 7.5x apart.
- Context window
- Both take 1.05M tokens.
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 | Gemini 3.7 Flash | 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-likeSix of these boards feed the Vibe Coding Index20
| Benchmark | Gemini 3.7 Flash | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench coding | 80.5% | 65.5% | Gemini 3.7 Flash |
| LMArena WebDev | 1591 | Not measured | Only Gemini 3.7 Flash measured |
| AA Coding Index | 71.5 | Not measured | Only Gemini 3.7 Flash measured |
| LiveCodeBench (Vals AI) | 88.7% | Not measured | Only Gemini 3.7 Flash measured |
| SWE-bench Verified (Vals AI) | 80.8% | Not measured | Only Gemini 3.7 Flash measured |
| Terminal-Bench 4 (Vals AI) | 12.1% | Not measured | Only Gemini 3.7 Flash measured |
| Vibe Code Bench (Vals AI) | 70.4% | Not measured | Only Gemini 3.7 Flash measured |
| Code migration (Vals AI) | 34.8% | Not measured | Only Gemini 3.7 Flash measured |
| ProgramBench (Vals AI) | 0.0% | Not measured | Only Gemini 3.7 Flash measured |
| IOI (Vals AI) | 67.8% | Not measured | Only Gemini 3.7 Flash measured |
| Terminal-Bench 2.1 (Vals AI) | 77.5% | Not measured | Only Gemini 3.7 Flash measured |
| SkillsBench (Vals AI) | 65.9% | Not measured | Only Gemini 3.7 Flash measured |
| CyberBench (Vals AI) | 43.8% | Not measured | Only Gemini 3.7 Flash measured |
| SciCode | 59.8% | 47.8% | Gemini 3.7 Flash |
| FrontierCode | 43.6% | Not measured | Only Gemini 3.7 Flash measured |
| FrontierSWE | 20.3% | Not measured | Only Gemini 3.7 Flash measured |
| ALE-Bench | 904.3 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: coding | 1499 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: software and IT | 1498 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena WebDev: React | 1587 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena WebDev: HTML | 1593 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena WebDev: from an image | 1599 | Not measured | Only Gemini 3.7 Flash measured |
| SciCode | 13.8% | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 16 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Gemini 3.7 Flash | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| AA Intelligence Index | 39.6 | 26.5 | Gemini 3.7 Flash |
| Epoch Capabilities Index | 158.0 | Not measured | Only Gemini 3.7 Flash measured |
Agentic workLike-for-likePublic boards, shown as published, kept out of the index30
| Benchmark | Gemini 3.7 Flash | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: agents building web apps | 1223 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building full-stack apps | 1208 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building mobile apps | 1229 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building Android apps | 1243 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building Godot games | 1264 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: agents building games | 1235 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: spreadsheets | 1058 | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench research | 76.5% | 70.9% | Gemini 3.7 Flash |
| LiveBench browser | 56.0% | 4.0% | Gemini 3.7 Flash |
| LiveBench long agentic | 28.3% | 15.8% | Gemini 3.7 Flash |
| SREBench (Vals AI) | 4.6% | Not measured | Only Gemini 3.7 Flash measured |
| Finance Agent (Vals AI) | 59.0% | Not measured | Only Gemini 3.7 Flash measured |
| Tax Agent Bench (Vals AI) | 22.8% | Not measured | Only Gemini 3.7 Flash measured |
| Legal Research Bench (Vals AI) | 34.6% | Not measured | Only Gemini 3.7 Flash measured |
| Excel Modeling Benchmark (Vals AI) | 71.3% | Not measured | Only Gemini 3.7 Flash measured |
| Terminal-Bench-Science (Vals AI) | 7.1% | Not measured | Only Gemini 3.7 Flash measured |
| APEX-Agents | 67.8% | Not measured | Only Gemini 3.7 Flash measured |
| Remote Labor Index | 5.0% | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 23 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.7 Flash | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| GPQA Diamond | 93.9% | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench reasoning | 87.8% | 78.8% | Gemini 3.7 Flash |
| MMMU Pro (Vals AI) | 89.0% | Not measured | Only Gemini 3.7 Flash measured |
| GPQA Diamond (Artificial Analysis run) | 94.5% | Not measured | Only Gemini 3.7 Flash measured |
| Humanity's Last Exam (Artificial Analysis run) | 47.9% | 30.7% | Gemini 3.7 Flash |
| GPQA Diamond (Epoch AI run) | 94.8% | Not measured | Only Gemini 3.7 Flash measured |
| ARC-AGI | 95.5% | Not measured | Only Gemini 3.7 Flash measured |
| ARC-AGI-2 | 84.6% | Not measured | Only Gemini 3.7 Flash measured |
| Chess puzzles | 66.0% | Not measured | Only Gemini 3.7 Flash measured |
| Mystery game puzzles | 37.0% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: hard prompts | 1495 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: expert | 1512 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: hard prompts in English | 1487 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: diagrams | 1329 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: homework | 1320 | Not measured | Only Gemini 3.7 Flash 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 | Gemini 3.7 Flash | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| OTIS Mock AIME | 97.2% | Not measured | Only Gemini 3.7 Flash measured |
| FrontierMath, tiers 1 to 3 | 71.6% | Not measured | Only Gemini 3.7 Flash measured |
| FrontierMath, tier 4 | 36.6% | Not measured | Only Gemini 3.7 Flash measured |
| ProofBench | 58.0% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: math | 1511 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: mathematical work | 1502 | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 7 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index11 boards, none shared
| Benchmark | Gemini 3.7 Flash | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| MMLU Pro | 90.1% | Not measured | Only Gemini 3.7 Flash measured |
| LegalBench (Vals AI) | 87.3% | Not measured | Only Gemini 3.7 Flash measured |
| TaxEval (Vals AI) | 74.7% | Not measured | Only Gemini 3.7 Flash measured |
| MortgageTax (Vals AI) | 66.7% | Not measured | Only Gemini 3.7 Flash measured |
| MedCode (Vals AI) | 53.4% | Not measured | Only Gemini 3.7 Flash measured |
| SimpleQA Verified | 69.2% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: science | 1511 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: medicine and healthcare | 1494 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: legal and government | 1496 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: business and finance | 1479 | Not measured | Only Gemini 3.7 Flash measured |
| FACTS Benchmark Suite | 71.2% | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 7 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index60
| Benchmark | Gemini 3.7 Flash | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| Design Arena: websites | 1303 | 1172 | Gemini 3.7 Flash |
| Design Arena: UI components | 1296 | 1153 | Gemini 3.7 Flash |
| Design Arena: game dev | 1317 | 1110 | Gemini 3.7 Flash |
| Design Arena: data viz | 1315 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: 3D | 1307 | 1127 | Gemini 3.7 Flash |
| Design Arena: all code categories | 1308 | 1155 | Gemini 3.7 Flash |
| Design Arena: SVG | 1283 | Not measured | Only Gemini 3.7 Flash measured |
| Design Arena: ASCII art | 1276 | Not measured | Only Gemini 3.7 Flash measured |
| LiveBench design | 73.2% | 60.7% | Gemini 3.7 Flash |
GeneralLike-for-likePublic boards, shown as published, kept out of the index20
| Benchmark | Gemini 3.7 Flash | Nemotron 3.5 Super VL | Leader |
|---|---|---|---|
| LiveBench data analysis | 68.0% | 64.6% | Gemini 3.7 Flash |
| SAGE (Vals AI) | 49.2% | Not measured | Only Gemini 3.7 Flash measured |
| MedScribe (Vals AI) | 83.9% | Not measured | Only Gemini 3.7 Flash measured |
| Long context reasoning (AA-LCR) | 83.0% | 69.0% | Gemini 3.7 Flash |
| DTBench | 96.8% | Not measured | Only Gemini 3.7 Flash measured |
| LMCA | 50.4% | Not measured | Only Gemini 3.7 Flash measured |
| Furniture assembly | 26.7% | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text | 1489 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: instruction following | 1484 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: creative writing | 1488 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision | 1314 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: English | 1483 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: non-English | 1487 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Chinese | 1552 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: German | 1506 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Japanese | 1511 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Korean | 1481 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: French | 1500 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Spanish | 1495 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Russian | 1515 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: Polish | 1503 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: multi-turn | 1485 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: longer queries | 1492 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: writing and language | 1491 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Text: entertainment and media | 1477 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: English | 1309 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: Chinese | 1394 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: reading text | 1322 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: humor | 1299 | Not measured | Only Gemini 3.7 Flash measured |
| LMArena Vision: creative writing | 1312 | Not measured | Only Gemini 3.7 Flash measured |
| Neither model has a result on the other 16 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Gemini 3.7 Flash | 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
Gemini 3.7 Flash$0.0026Fits in one request
Nemotron 3.5 Super VL$0.0004Fits in one request
Nemotron 3.5 Super VL costs less
Repository review
50K input + 3K output tokens
Gemini 3.7 Flash$0.049Fits in one request
Nemotron 3.5 Super VL$0.0090Fits in one request
Nemotron 3.5 Super VL costs less
Agent loop
200K input + 10K output tokens
Gemini 3.7 Flash$0.188Fits in one request
Nemotron 3.5 Super VL$0.035Fits in one request
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 | Google Gemini 3.7 Flash | NVIDIA Nemotron 3.5 Super VL |
|---|---|---|
| Maker | NVIDIA | |
| Weights | No public weights listed | Open weights |
| Released | August 13, 2026 | October 15, 2026 |
| Context window | 1.05M | 1.05M |
| Input $/Mtok | $0.75 | $0.15 |
| Output $/Mtok | $3.75 | $0.50 |
| Coding index | 58.0 | Not benchmarked |
| Independent measurements behind it | 6 | 1 |
| Cost per solved task | $1.78 | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Gemini 3.7 Flash better than Nemotron 3.5 Super VL for coding?
Only Gemini 3.7 Flash has a coding score, 58.0 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 7.5x cheaper ($0.50 vs $3.75 per million).
Which is cheaper, Gemini 3.7 Flash or Nemotron 3.5 Super VL?
Gemini 3.7 Flash costs $0.75 per million input tokens and $3.75 per million output tokens; Nemotron 3.5 Super VL costs $0.15 and $0.50. Cost per solved task is known for Gemini 3.7 Flash only.
Which has the bigger context window, Gemini 3.7 Flash or Nemotron 3.5 Super VL?
Neither: both take 1.05M tokens.
How many benchmarks is the Gemini 3.7 Flash vs Nemotron 3.5 Super VL comparison based on?
16 benchmarks have a result for both models as of October 22, 2026. Gemini 3.7 Flash's coding index rests on 6 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.