// head_to_head
Gemini 4 Carbon 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.
Gemini 4 Carbon scores higher, Nemotron 3.5 Super VL costs less — it depends on your workload.
Gemini 4 Carbon is ahead by 36.5 points overall, and Nemotron 3.5 Super VL lists 17× cheaper per blended million tokens. Whether 36.5 points is worth that depends on how much a wrong answer costs you. Nemotron 3.5 Super VL also leads on measured cost per point of capability, at $0.0031 per point.
Gemini 4 Carbon
- Blended / 1M
- $4.00
- Context
- 1.0M
- Released
- Nov 10, 2026
- Overall score
- 83.5
nvidia
Nemotron 3.5 Super VL
- Blended / 1M
- $0.230
- Context
- 1.0M
- Released
- Oct 15, 2026
- Overall score
- 47.0
Specs and pricing
| Metric | Gemini 4 Carbon | Nemotron 3.5 Super VL |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 83.5win | 47.0 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0202 | $0.0031win |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $4.00 | $0.230win |
| Input price / 1M | $2.00 | $0.150win |
| Output price / 1M | $10.00 | $0.470win |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.100 | $0.020win |
| Context window | 1.0M | 1.0M |
| Max output tokens | 944K | 944K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Gemini 4 Carbon 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 | Gemini 4 Carbon | Nemotron 3.5 Super VL |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $1,143/mo | $64.24/mo |
| RAG assistant 8K in / 600 out × 100K requests | $1,440/mo | $96.20/mo |
| Coding agent 40K in / 4K out × 20K requests | $1,336/mo | $84.80/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $2,655/mo | $178.75/mo |
| Bulk classification 500 in / 20 out × 5M requests | $5,050/mo | $357.00/mo |
Which should you pick?
You are running this at volume
Nemotron 3.5 Super VL
Lowest measured cost per point of capability at $0.0031 per point — the gap compounds with every request.
Quality matters more than the bill
Gemini 4 Carbon
Highest overall LiveBench score of the two at 83.5.
The workload is coding or agentic work
Gemini 4 Carbon
Leads on long-horizon agentic — 72.7 against 12.0.
Gemini 4 Carbon vs Nemotron 3.5 Super VL FAQ
Which is better, Gemini 4 Carbon or Nemotron 3.5 Super VL?
Gemini 4 Carbon scores higher, Nemotron 3.5 Super VL costs less — it depends on your workload. Gemini 4 Carbon is ahead by 36.5 points overall, and Nemotron 3.5 Super VL lists 17× cheaper per blended million tokens. Whether 36.5 points is worth that depends on how much a wrong answer costs you. Nemotron 3.5 Super VL also leads on measured cost per point of capability, at $0.0031 per point.
Is Gemini 4 Carbon cheaper than Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL is cheaper. On a 3:1 input:output blend, Gemini 4 Carbon lists at $4.00 per million tokens and Nemotron 3.5 Super VL at $0.230 — Nemotron 3.5 Super VL is 17× cheaper. Input and output are priced separately — Gemini 4 Carbon charges $2.00 in and $10.00 out, Nemotron 3.5 Super VL charges $0.150 and $0.470 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Gemini 4 Carbon vs Nemotron 3.5 Super VL: which scores higher on benchmarks?
Gemini 4 Carbon scores 83.5 and Nemotron 3.5 Super VL scores 47.0 overall on LiveBench, the weighted mean of its seven categories. That is a 36.5-point lead for Gemini 4 Carbon. 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, Gemini 4 Carbon or Nemotron 3.5 Super VL?
Nemotron 3.5 Super VL. 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. Gemini 4 Carbon works out at $0.0202 per point and Nemotron 3.5 Super VL at $0.0031.
Does Gemini 4 Carbon or Nemotron 3.5 Super VL have a bigger context window?
They are effectively the same — 1.0M for Gemini 4 Carbon and 1.0M for Nemotron 3.5 Super VL.
Do Gemini 4 Carbon and Nemotron 3.5 Super VL support prompt caching?
Both publish a cached-input rate: $0.100 per million for Gemini 4 Carbon and $0.020 for Nemotron 3.5 Super VL, against full input rates of $2.00 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 2026-11-06, 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.