Nemotron 3.5 Super VL vs Qwen2.5 7B Instruct
Qwen2.5 7B Instruct costs 2.5x less per output token than Nemotron 3.5 Super VL ($0.20 vs $0.50 per million). Updated October 22, 2026; no benchmark has tested both models yet.
- Coding
- No data
- Overall
- 35.6
- Output $/M
- $0.50
- Coding
- No data
- Overall
- No data
- Output $/M
- $0.20
Nemotron 3.5 Super VL or Qwen2.5 7B Instruct: 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 a larger context window (1.05M against 33K tokens).
No benchmark has tested both yet. Nemotron 3.5 Super VL has results on LiveBench coding, SciCode and AA Intelligence Index; Qwen2.5 7B Instruct on Epoch Capabilities Index, BALROG and GPQA Diamond (Epoch AI run). Compare them on price and context instead.
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
No call
Only Nemotron 3.5 Super VL has a AA Intelligence Index result, 26.5.
Directional only
Web design
Design Arena: websites
No call
Only Nemotron 3.5 Super VL has a Design Arena: websites result, 1173.
Directional only
Long documents
Listed context window
Nemotron 3.5 Super VL
Nemotron 3.5 Super VL takes 1.05M tokens against 33K.
Listed by OpenRouter
Everyday cost
1K input + 500 output tokens
Qwen2.5 7B Instruct
Nemotron 3.5 Super VL $0.0004, Qwen2.5 7B Instruct $0.0002 per turn.
List prices
At a glance
- Coding index
- Nemotron 3.5 Super VL not benchmarked, Qwen2.5 7B Instruct not benchmarked.
- Widest category
- None yet: no category has been tested on both models.
- Widest single gap
- None yet: no benchmark has tested both models.
- Output price
- Nemotron 3.5 Super VL $0.50 against Qwen2.5 7B Instruct $0.20 per million output tokens, 2.5x apart.
- Context window
- Nemotron 3.5 Super VL takes 1.05M tokens, Qwen2.5 7B Instruct 33K.
Benchmark by benchmark
Every board either model has a result on, a category to a fold; bars are each result on a 0 to 100 scale. Only one model is measured on each, so no row names a leader.
CodingDirectional onlySix of these boards feed the Vibe Coding Index2 boards measured
| Benchmark | Nemotron 3.5 Super VL | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| LiveBench coding | 65.5% | Not measured | Only Nemotron 3.5 Super VL measured |
| SciCode | 47.8% | Not measured | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 41 boards here | |||
IntelligenceDirectional onlyPublic boards, shown as published, kept out of the index2 boards measured
| Benchmark | Nemotron 3.5 Super VL | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| AA Intelligence Index | 26.5 | Not measured | Only Nemotron 3.5 Super VL measured |
| Epoch Capabilities Index | Not measured | 118.5 | Only Qwen2.5 7B Instruct measured |
Agentic workDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Nemotron 3.5 Super VL | Qwen2.5 7B Instruct | 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 |
| BALROG | Not measured | 7.8% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 38 boards here | |||
ReasoningDirectional onlyPublic boards, shown as published, kept out of the index3 boards measured
| Benchmark | Nemotron 3.5 Super VL | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| LiveBench reasoning | 78.8% | Not measured | Only Nemotron 3.5 Super VL measured |
| Humanity's Last Exam (Artificial Analysis run) | 30.7% | Not measured | Only Nemotron 3.5 Super VL measured |
| GPQA Diamond (Epoch AI run) | Not measured | 35.5% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 20 boards here | |||
MathDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Nemotron 3.5 Super VL | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| OTIS Mock AIME | Not measured | 2.5% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 13 boards here | |||
KnowledgeDirectional onlyPublic boards, shown as published, kept out of the index1 board measured
| Benchmark | Nemotron 3.5 Super VL | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| MMLU | Not measured | 72.9% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 19 boards here | |||
Web designDirectional onlyPublic boards, shown as published, kept out of the index6 boards measured
| Benchmark | Nemotron 3.5 Super VL | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| Design Arena: websites | 1173 | Not measured | Only Nemotron 3.5 Super VL measured |
| Design Arena: UI components | 1149 | Not measured | Only Nemotron 3.5 Super VL measured |
| Design Arena: game dev | 1110 | Not measured | Only Nemotron 3.5 Super VL measured |
| Design Arena: 3D | 1128 | Not measured | Only Nemotron 3.5 Super VL measured |
| Design Arena: all code categories | 1154 | Not measured | Only Nemotron 3.5 Super VL measured |
| LiveBench design | 60.7% | Not measured | Only Nemotron 3.5 Super VL measured |
| Neither model has a result on the other 3 boards here | |||
GeneralDirectional onlyPublic boards, shown as published, kept out of the index4 boards measured
| Benchmark | Nemotron 3.5 Super VL | Qwen2.5 7B Instruct | Leader |
|---|---|---|---|
| LiveBench data analysis | 64.6% | Not measured | Only Nemotron 3.5 Super VL measured |
| Long context reasoning (AA-LCR) | 69.0% | Not measured | Only Nemotron 3.5 Super VL measured |
| DTBench | Not measured | 47.7% | Only Qwen2.5 7B Instruct measured |
| LMCA | Not measured | 6.4% | Only Qwen2.5 7B Instruct measured |
| Neither model has a result on the other 44 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
Qwen2.5 7B Instruct costs less
Repository review
50K input + 3K output tokens
Qwen2.5 7B Instruct costs less
Agent loop
200K input + 10K output tokens
Qwen2.5 7B Instruct 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 | Qwen2.5 7B Instruct |
|---|---|---|
| Maker | NVIDIA | Qwen |
| Weights | Open weights | Open weights |
| Released | October 15, 2026 | October 16, 2024 |
| Context window | 1.05M | 33K |
| Input $/Mtok | $0.15 | $0.10 |
| Output $/Mtok | $0.50 | $0.20 |
| 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 Qwen2.5 7B Instruct for coding?
Neither model has a coding score yet, so the two cannot be ranked on coding. Pick Nemotron 3.5 Super VL for a larger context window (1.05M against 33K tokens).
Which is cheaper, Nemotron 3.5 Super VL or Qwen2.5 7B Instruct?
Nemotron 3.5 Super VL costs $0.15 per million input tokens and $0.50 per million output tokens; Qwen2.5 7B Instruct costs $0.10 and $0.20. Cost per solved task is known for neither of them.
Which has the bigger context window, Nemotron 3.5 Super VL or Qwen2.5 7B Instruct?
Nemotron 3.5 Super VL, with 1.05M tokens against 33K.
Has any benchmark tested both Nemotron 3.5 Super VL and Qwen2.5 7B Instruct?
No benchmark has tested both yet. Nemotron 3.5 Super VL has results on LiveBench coding, SciCode and AA Intelligence Index; Qwen2.5 7B Instruct on Epoch Capabilities Index, BALROG and GPQA Diamond (Epoch AI run). Compare them on price and context instead. Data as of October 22, 2026.