// head_to_head
Nemotron 3.5 Super VL vs SpaceXAI: Grok 4.7
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.
SpaceXAI: Grok 4.7 scores higher, Nemotron 3.5 Super VL costs less — it depends on your workload.
SpaceXAI: Grok 4.7 is ahead by 19.9 points overall, and Nemotron 3.5 Super VL lists 13× cheaper per blended million tokens. Whether 19.9 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 $0.0030 per point.
nvidia
Nemotron 3.5 Super VL
- Blended / 1M
- $0.237
- Context
- 1.0M
- Released
- Oct 15, 2026
- Overall score
- 50.2
x-ai
SpaceXAI: Grok 4.7
- Blended / 1M
- $3.00
- Context
- 500K
- Released
- Sep 21, 2026
- Overall score
- 70.1
Specs and pricing
| Metric | Nemotron 3.5 Super VL | SpaceXAI: Grok 4.7 |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 50.2 | 70.1win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0030win | $0.0291 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.237win | $3.00 |
| Input price / 1M | $0.150win | $2.00 |
| Output price / 1M | $0.500win | $6.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.020win | $0.500 |
| Context window | 1.0Mwin | 500K |
| Max output tokens | 236K | 450Kwin |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Nemotron 3.5 Super VL on top, SpaceXAI: Grok 4.7 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 | Nemotron 3.5 Super VL | SpaceXAI: Grok 4.7 |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $66.64/mo | $852.00/mo |
| RAG assistant 8K in / 600 out × 100K requests | $98.00/mo | $1,360/mo |
| Coding agent 40K in / 4K out × 20K requests | $87.20/mo | $1,240/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $181.00/mo | $2,375/mo |
| Bulk classification 500 in / 20 out × 5M requests | $360.00/mo | $4,850/mo |
Which should you pick?
You are running this at volume
Nemotron 3.5 Super VL
Lowest measured cost per point of capability at $0.0030 per point — the gap compounds with every request.
Quality matters more than the bill
SpaceXAI: Grok 4.7
Highest overall LiveBench score of the two at 70.1.
The workload is coding or agentic work
SpaceXAI: Grok 4.7
Leads on long-horizon agentic — 46.1 against 11.7.
You need to fit large documents in one call
Nemotron 3.5 Super VL
Wider context window — 1.0M against 500K.
Nemotron 3.5 Super VL vs SpaceXAI: Grok 4.7 FAQ
Which is better, Nemotron 3.5 Super VL or SpaceXAI: Grok 4.7?
SpaceXAI: Grok 4.7 scores higher, Nemotron 3.5 Super VL costs less — it depends on your workload. SpaceXAI: Grok 4.7 is ahead by 19.9 points overall, and Nemotron 3.5 Super VL lists 13× cheaper per blended million tokens. Whether 19.9 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 $0.0030 per point.
Is Nemotron 3.5 Super VL cheaper than SpaceXAI: Grok 4.7?
Nemotron 3.5 Super VL is cheaper. On a 3:1 input:output blend, Nemotron 3.5 Super VL lists at $0.237 per million tokens and SpaceXAI: Grok 4.7 at $3.00 — Nemotron 3.5 Super VL is 13× cheaper. Input and output are priced separately — Nemotron 3.5 Super VL charges $0.150 in and $0.500 out, SpaceXAI: Grok 4.7 charges $2.00 and $6.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Nemotron 3.5 Super VL vs SpaceXAI: Grok 4.7: which scores higher on benchmarks?
Nemotron 3.5 Super VL scores 50.2 and SpaceXAI: Grok 4.7 scores 70.1 overall on LiveBench, the weighted mean of its seven categories. That is a 19.9-point lead for SpaceXAI: Grok 4.7. 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, Nemotron 3.5 Super VL or SpaceXAI: Grok 4.7?
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. Nemotron 3.5 Super VL works out at $0.0030 per point and SpaceXAI: Grok 4.7 at $0.0291.
Does Nemotron 3.5 Super VL or SpaceXAI: Grok 4.7 have a bigger context window?
Nemotron 3.5 Super VL has the larger context window: 1.0M for Nemotron 3.5 Super VL against 500K for SpaceXAI: Grok 4.7. 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 Nemotron 3.5 Super VL and SpaceXAI: Grok 4.7 support prompt caching?
Both publish a cached-input rate: $0.020 per million for Nemotron 3.5 Super VL and $0.500 for SpaceXAI: Grok 4.7, against full input rates of $0.150 and $2.00. 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.