// head_to_head
Saba 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.
Nemotron 3.5 Super VL is the cheaper of the two; neither can be ranked on quality here.
Saba 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.
mistralai
Saba
- Blended / 1M
- $0.300
- Context
- 33K
- Released
- Feb 17, 2025
- 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 | Saba | 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. | $0.300 | $0.237win |
| Input price / 1M | $0.200 | $0.150win |
| Output price / 1M | $0.600 | $0.500win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.020 | $0.020 |
| Context window | 33K | 1.0Mwin |
| Max output tokens | 26K | 236Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Saba 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 | Saba | Nemotron 3.5 Super VL |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $83.04/mo | $66.64/mo |
| RAG assistant 8K in / 600 out × 100K requests | $124.00/mo | $98.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $107.20/mo | $87.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $236.00/mo | $181.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $470.00/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 33K.
Saba vs Nemotron 3.5 Super VL FAQ
Which is better, Saba or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL is the cheaper of the two; neither can be ranked on quality here. Saba 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 Saba cheaper than Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL is cheaper. On a 3:1 input:output blend, Saba lists at $0.300 per million tokens and Nemotron 3.5 Super VL at $0.237 — Nemotron 3.5 Super VL is 26% cheaper. Input and output are priced separately — Saba charges $0.200 in and $0.600 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 Saba or Nemotron 3.5 Super VL have a bigger context window?
Nemotron 3.5 Super VL has the larger context window: 33K for Saba 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 Saba and Nemotron 3.5 Super VL support prompt caching?
Both publish a cached-input rate: $0.020 per million for Saba and $0.020 for Nemotron 3.5 Super VL, against full input rates of $0.200 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.