// head_to_head
Nemotron 3.5 Super VL vs Inkling
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 wins outright — it scores higher and costs less.
Nemotron 3.5 Super VL leads by 3.2 points overall while listing 7.4× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. 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
thinkingmachines
Inkling
- Blended / 1M
- $1.76
- Context
- 524K
- Released
- Jul 17, 2026
- Overall score
- 47.0
Specs and pricing
| Metric | Nemotron 3.5 Super VL | Inkling |
|---|---|---|
| LiveBench overall Weighted mean of the seven LiveBench category scores, 0–100. Higher is better. | 50.2win | 47.0 |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.0030win | $0.0179 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $0.237win | $1.76 |
| Input price / 1M | $0.150win | $1.00 |
| Output price / 1M | $0.500win | $4.05 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.020win | $0.170 |
| Context window | 1.0Mwin | 524K |
| Max output tokens | 236K | 472Kwin |
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, Inkling 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 | Inkling |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $66.64/mo | $504.24/mo |
| RAG assistant 8K in / 600 out × 100K requests | $98.00/mo | $711.00/mo |
| Coding agent 40K in / 4K out × 20K requests | $87.20/mo | $659.20/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $181.00/mo | $1,262/mo |
| Bulk classification 500 in / 20 out × 5M requests | $360.00/mo | $2,490/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
Nemotron 3.5 Super VL
Highest overall LiveBench score of the two at 50.2.
The workload is coding or agentic work
Nemotron 3.5 Super VL
Leads on long-horizon agentic — 11.7 against 8.5.
You need to fit large documents in one call
Nemotron 3.5 Super VL
Wider context window — 1.0M against 524K.
Nemotron 3.5 Super VL vs Inkling FAQ
Which is better, Nemotron 3.5 Super VL or Inkling?
Nemotron 3.5 Super VL wins outright — it scores higher and costs less. Nemotron 3.5 Super VL leads by 3.2 points overall while listing 7.4× cheaper per blended million tokens. There is no trade-off to reason about here; the only reason to pick the other is a constraint this table does not show, like an existing contract, a region, or a provider you are already on. 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 Inkling?
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 Inkling at $1.76 — Nemotron 3.5 Super VL is 7.4× cheaper. Input and output are priced separately — Nemotron 3.5 Super VL charges $0.150 in and $0.500 out, Inkling charges $1.00 and $4.05 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Nemotron 3.5 Super VL vs Inkling: which scores higher on benchmarks?
Nemotron 3.5 Super VL scores 50.2 and Inkling scores 47.0 overall on LiveBench, the weighted mean of its seven categories. That is a 3.2-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, Nemotron 3.5 Super VL or Inkling?
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 Inkling at $0.0179.
Does Nemotron 3.5 Super VL or Inkling have a bigger context window?
Nemotron 3.5 Super VL has the larger context window: 1.0M for Nemotron 3.5 Super VL against 524K for Inkling. 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 Inkling support prompt caching?
Both publish a cached-input rate: $0.020 per million for Nemotron 3.5 Super VL and $0.170 for Inkling, against full input rates of $0.150 and $1.00. 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.