// head_to_head
Schematron V2 Small vs Nemotron 3.5 Super VL
Published LiveBench scores across all seven categories, live list pricing, context windows, and the measured cost of a point of capability — for both models, side by side.
Schematron V2 Small is the cheaper of the two; neither can be ranked on quality here.
Schematron V2 Small does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
inference-net
Schematron V2 Small
- Blended / 1M
- $0.095
- Context
- 128K
- Released
- Sep 12, 2026
- Overall score
- Not evaluated
nvidia
Nemotron 3.5 Super VL
- Blended / 1M
- $0.237
- Context
- 1.0M
- Released
- Oct 15, 2026
- Overall score
- 50.2
Specs and pricing
| Metric | Schematron V2 Small | Nemotron 3.5 Super VL |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | — | 50.2 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | — | $0.0030 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.095win | $0.237 |
| Input price / 1M | $0.050win | $0.150 |
| Output price / 1M | $0.230win | $0.500 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.050 | $0.020win |
| Context window | 128K | 1.0Mwin |
| Max output tokens | 8K | 944Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Schematron V2 Small on top, Nemotron 3.5 Super VL below, both out of 100.
What each one costs to run
Per-token prices are hard to feel. These are monthly list costs for both models across five workload shapes, using each provider's published cached-input rate where there is one.
| Workload | Schematron V2 Small | Nemotron 3.5 Super VL |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $30.40/mo | $66.64/mo |
| RAG assistant 8K in / 600 out × 100K requests | $53.80/mo | $98.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $58.40/mo | $87.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $67.25/mo | $181.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $148.00/mo | $360.00/mo |
Which should you pick?
You need to fit large documents in one call
Nemotron 3.5 Super VL
Wider context window — 1.0M against 128K.
You are cost-constrained
Schematron V2 Small
Cheaper on blended list price at $0.095 per million tokens.
Schematron V2 Small vs Nemotron 3.5 Super VL FAQ
Which is better, Schematron V2 Small or Nemotron 3.5 Super VL?
Schematron V2 Small is the cheaper of the two; neither can be ranked on quality here. Schematron V2 Small does not have a published LiveBench run, so this comparison covers price, context and declared capabilities only. A missing score means "not evaluated", not "worse" — the right way to separate these two is an eval on your own workload.
Is Schematron V2 Small cheaper than Nemotron 3.5 Super VL?
Schematron V2 Small is cheaper. On a 3:1 input:output blend, Schematron V2 Small lists at $0.095 per million tokens and Nemotron 3.5 Super VL at $0.237 — Schematron V2 Small is 2.5× cheaper. Input and output are priced separately — Schematron V2 Small charges $0.050 in and $0.230 out, Nemotron 3.5 Super VL charges $0.150 and $0.500 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Does Schematron V2 Small or Nemotron 3.5 Super VL have a bigger context window?
Nemotron 3.5 Super VL has the larger context window: 128K for Schematron V2 Small against 1.0M for Nemotron 3.5 Super VL. Note that a window you can fill is not a window you should fill — retrieval quality usually degrades well before the limit, and you pay for every token you put in it.
Do Schematron V2 Small and Nemotron 3.5 Super VL support prompt caching?
Both publish a cached-input rate: $0.050 per million for Schematron V2 Small and $0.020 for Nemotron 3.5 Super VL, against full input rates of $0.050 and $0.150. On a workload with a long stable prefix — a system prompt, a tool schema, a retrieved corpus — that changes the economics more than the headline price does.
Related comparisons
How these numbers are produced
- Price — provider list price from OpenRouter, refreshed every 15 minutes. “Blended” is a 3:1 input:output mix.
- Scores — LiveBench release 2026-10-25, using their own category map. Each model shows its strongest published run. A blank means “not evaluated”, never “bad”.
- Cost per point — the measured dollars LiveBench spent on the run, divided by the score it earned.
- “Win” — awarded only past a threshold: one full point on a benchmark score, 10% on a price, 25% on a context window. Anything tighter reports as a tie, because effort settings alone move a LiveBench score by more than that.
Published benchmarks rank models on someone else's tasks. Before committing, see LLM & agent evaluation for building an eval on your own.