// head_to_head
Gemma 4 31B 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 better model, at roughly the same price.
Nemotron 3.5 Super VL leads by 11.5 points overall and the two list within about 10% of each other, so the cheaper-but-weaker trade-off does not apply. Price parity plus a score gap usually makes this an easy call. Gemma 4 31B also leads on measured cost per point of capability, at $0.0017 per point.
Gemma 4 31B
- Blended / 1M
- $0.250
- Context
- 262K
- Released
- Apr 2, 2026
- Overall score
- 38.7
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 | Gemma 4 31B | Nemotron 3.5 Super VL |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 38.7 | 50.2win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0017win | $0.0030 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.250 | $0.237 |
| Input price / 1M | $0.200 | $0.150win |
| Output price / 1M | $0.400win | $0.500 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | — | $0.020 |
| Context window | 262K | 1.0Mwin |
| Max output tokens | 16K | 236Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemma 4 31B 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 | Gemma 4 31B | Nemotron 3.5 Super VL |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $80.00/mo | $66.64/mo |
| RAG assistant 8K in / 600 out × 100K requests | $184.00/mo | $98.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $192.00/mo | $87.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $230.00/mo | $181.00/mo |
| Bulk classification 500 in / 20 out × 5M requests | $540.00/mo | $360.00/mo |
Which should you pick?
You are running this at volume
Gemma 4 31B
Lowest measured cost per point of capability at $0.0017 per point — the gap compounds with every request.
Quality matters more than the bill
Nemotron 3.5 Super VL
Highest overall LiveBench score of the two at 50.2.
You need to fit large documents in one call
Nemotron 3.5 Super VL
Wider context window — 1.0M against 262K.
Gemma 4 31B vs Nemotron 3.5 Super VL FAQ
Which is better, Gemma 4 31B or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL is the better model, at roughly the same price. Nemotron 3.5 Super VL leads by 11.5 points overall and the two list within about 10% of each other, so the cheaper-but-weaker trade-off does not apply. Price parity plus a score gap usually makes this an easy call. Gemma 4 31B also leads on measured cost per point of capability, at $0.0017 per point.
Is Gemma 4 31B cheaper than Nemotron 3.5 Super VL?
They cost about the same. Both land near $0.250 per million tokens on a 3:1 input:output blend, so price is unlikely to be the deciding factor between them.
Gemma 4 31B vs Nemotron 3.5 Super VL: which scores higher on benchmarks?
Gemma 4 31B scores 38.7 and Nemotron 3.5 Super VL scores 50.2 overall on LiveBench, the weighted mean of its seven categories. That is a 11.5-point lead for Nemotron 3.5 Super VL. Category scores differ from the overall figure — a model can lead on reasoning and trail on coding, which the per-category table above breaks out.
Which gives better value for money, Gemma 4 31B or Nemotron 3.5 Super VL?
Gemma 4 31B. Cost per point divides the measured dollars LiveBench spent running the benchmark by the score it earned, so it captures something token pricing misses: a reasoning model can emit many times more tokens than its per-token rate suggests. Gemma 4 31B works out at $0.0017 per point and Nemotron 3.5 Super VL at $0.0030.
Does Gemma 4 31B or Nemotron 3.5 Super VL have a bigger context window?
Nemotron 3.5 Super VL has the larger context window: 262K for Gemma 4 31B 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 Gemma 4 31B and Nemotron 3.5 Super VL support prompt caching?
Nemotron 3.5 Super VL publishes a cached-input rate of $0.020 per million tokens against a full input rate of $0.150. The catalogue lists no separate cached rate for Gemma 4 31B, which means the provider does not price it separately here — not that caching is unavailable.
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.