Nemotron 3.5 Super VL vs Qwen3 235B A22B Instruct 2507
Nemotron 3.5 Super VL leads Qwen3 235B A22B Instruct 2507 on the Artificial Analysis Intelligence Index, 26.5 to 12.0, and Nemotron 3.5 Super VL costs 1.6x less per output token ($0.50 vs $0.775 per million). Updated October 22, 2026; 9 benchmarks have a result for both.
Nemotron 3.5 Super VL or Qwen3 235B A22B Instruct 2507: which to pick
Neither model has a coding score yet, so the two cannot be ranked on coding.
Pick Nemotron 3.5 Super VL for output tokens 1.6x cheaper ($0.50 vs $0.775 per million) and a larger context window (1.05M against 262K tokens).
- Shared results
- 9
- Nemotron 3.5 Super VL only
- 7
- Qwen3 235B A22B Instruct 2507 only
- 15
- Like-for-like categories
- 5 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 12.0.
Same board, both measured
Web design
Design Arena: websites
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL leads, 1173 against 1058.
Same board, both measured
Long documents
Listed context window
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL takes 1.05M tokens against 262K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL $0.0004, Qwen3 235B A22B Instruct 2507 $0.0005 per turn.
List prices
At a glance
- Coding index
- Nemotron 3.5 Super VL not benchmarked, Qwen3 235B A22B Instruct 2507 not benchmarked.
- Widest category
- Web design, 17.8 points apart on average over 5 shared benchmarks.
- Widest single gap
- Design Arena: UI components: Nemotron 3.5 Super VL 1149 against Qwen3 235B A22B Instruct 2507 963, 23.3 points apart on a 0 to 100 scale.
- Output price
- Nemotron 3.5 Super VL $0.50 against Qwen3 235B A22B Instruct 2507 $0.775 per million output tokens, 1.6x apart.
- Context window
- Nemotron 3.5 Super VL takes 1.05M tokens, Qwen3 235B A22B Instruct 2507 262K.
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 | Nemotron 3.5 Super VL | Bars | Qwen3 235B A22B Instruct 2507 | Ties |
|---|---|---|---|---|
| Coding | 1 | 0 | ||
| Intelligence | 1 | 0 | ||
| Reasoning | 1 | 0 | ||
| Web design | 5 | 0 | ||
| General | 0 | 1 | ||
| All shared boards | 8 | 1 |
CodingLike-for-likeSix of these boards feed the Vibe Coding Index10
| Benchmark | Nemotron 3.5 Super VL | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| LiveBench coding | 65.5% | Not measured | Only Nemotron 3.5 Super VL measured |
| LiveCodeBench (Artificial Analysis run) | Not measured | 78.8% | Only Qwen3 235B A22B Instruct 2507 measured |
| SciCode | 47.8% | 41.4% | Nemotron 3.5 Super VL |
| Terminal-Bench Hard (Artificial Analysis run) | Not measured | 15.2% | Only Qwen3 235B A22B Instruct 2507 measured |
| WeirdML | Not measured | 38.7% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 38 boards here | |||
IntelligenceLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Nemotron 3.5 Super VL | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Intelligence Index | 26.5 | 12.0 | 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 index5 boards, none shared
| Benchmark | Nemotron 3.5 Super VL | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| LiveBench research | 70.9% | Not measured | Only Nemotron 3.5 Super VL measured |
| LiveBench browser | 4.0% | Not measured | Only Nemotron 3.5 Super VL measured |
| LiveBench long agentic | 15.8% | Not measured | Only Nemotron 3.5 Super VL measured |
| τ²-Bench Telecom | Not measured | 53.2% | Only Qwen3 235B A22B Instruct 2507 measured |
| Berkeley Function Calling Leaderboard (BFCL) | Not measured | 52.1% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 37 boards here | |||
ReasoningLike-for-likePublic boards, shown as published, kept out of the index10
| Benchmark | Nemotron 3.5 Super VL | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| LiveBench reasoning | 78.8% | Not measured | Only Nemotron 3.5 Super VL measured |
| GPQA Diamond (Artificial Analysis run) | Not measured | 79.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| Humanity's Last Exam (Artificial Analysis run) | 30.7% | 15.9% | Nemotron 3.5 Super VL |
| ARC-AGI | Not measured | 11.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| ARC-AGI-2 | Not measured | 1.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 18 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index2 boards, none shared
| Benchmark | Nemotron 3.5 Super VL | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| AA Math Index | Not measured | 91.0 | Only Qwen3 235B A22B Instruct 2507 measured |
| AIME 2025 (Artificial Analysis run) | Not measured | 91.0% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 12 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board, none shared
| Benchmark | Nemotron 3.5 Super VL | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| MMLU Pro (Artificial Analysis run) | Not measured | 84.3% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 19 boards here | |||
Web designLike-for-likePublic boards, shown as published, kept out of the index50
| Benchmark | Nemotron 3.5 Super VL | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| Design Arena: websites | 1173 | 1058 | Nemotron 3.5 Super VL |
| Design Arena: UI components | 1149 | 963 | Nemotron 3.5 Super VL |
| Design Arena: game dev | 1110 | 951 | Nemotron 3.5 Super VL |
| Design Arena: data viz | Not measured | 1070 | Only Qwen3 235B A22B Instruct 2507 measured |
| Design Arena: 3D | 1128 | 994 | Nemotron 3.5 Super VL |
| Design Arena: all code categories | 1154 | 1038 | Nemotron 3.5 Super VL |
| LiveBench design | 60.7% | Not measured | 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 | Nemotron 3.5 Super VL | Qwen3 235B A22B Instruct 2507 | Leader |
|---|---|---|---|
| LiveBench data analysis | 64.6% | Not measured | Only Nemotron 3.5 Super VL measured |
| IFBench | Not measured | 51.2% | Only Qwen3 235B A22B Instruct 2507 measured |
| Long context reasoning (AA-LCR) | 69.0% | 72.0% | Qwen3 235B A22B Instruct 2507 |
| DTBench | Not measured | 78.4% | Only Qwen3 235B A22B Instruct 2507 measured |
| LMCA | Not measured | 23.5% | Only Qwen3 235B A22B Instruct 2507 measured |
| Neither model has a result on the other 43 boards here | |||
Image generationNot comparablePublic boards, shown as published, kept out of the index0 boards, none shared
| Benchmark | Nemotron 3.5 Super VL | Qwen3 235B A22B Instruct 2507 | 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 | NVIDIA Nemotron 3.5 Super VL | Qwen3 235B A22B Instruct 2507 |
|---|---|---|
| Maker | NVIDIA | Qwen |
| Weights | Open weights | Open weights |
| Released | October 15, 2026 | July 21, 2025 |
| Context window | 1.05M | 262K |
| Input $/Mtok | $0.15 | $0.145 |
| Output $/Mtok | $0.50 | $0.775 |
| Coding index | Not benchmarked | Not benchmarked |
| Independent measurements behind it | 1 | 0 |
| Cost per solved task | Not computable | Not computable |
| Source | OpenRouter listing | OpenRouter listing |
Questions
Is Nemotron 3.5 Super VL better than Qwen3 235B A22B Instruct 2507 for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Nemotron 3.5 Super VL for output tokens 1.6x cheaper ($0.50 vs $0.775 per million) and a larger context window (1.05M against 262K tokens).
Which is cheaper, Nemotron 3.5 Super VL or Qwen3 235B A22B Instruct 2507?
Nemotron 3.5 Super VL costs $0.15 per million input tokens and $0.50 per million output tokens; Qwen3 235B A22B Instruct 2507 costs $0.145 and $0.775. Cost per solved task is known for neither of them.
Which has the bigger context window, Nemotron 3.5 Super VL or Qwen3 235B A22B Instruct 2507?
Nemotron 3.5 Super VL, with 1.05M tokens against 262K.
How many benchmarks is the Nemotron 3.5 Super VL vs Qwen3 235B A22B Instruct 2507 comparison based on?
9 benchmarks have a result for both models as of October 22, 2026. Nemotron 3.5 Super VL's coding index rests on 1 independent benchmark and Qwen3 235B A22B Instruct 2507's on 0. Where only one model has a result, the page says so instead of naming a winner.