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Nemotron 3.5 Super VL vs Solar Pro 4

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.

OpenRouter + LiveBenchAll comparisonsFull leaderboard

Nemotron 3.5 Super VL is the cheaper of the two; neither can be ranked on quality here.

Solar Pro 4 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.

nvidia

Nemotron 3.5 Super VL

Blended / 1M
$0.237
Context
1.0M
Released
Oct 15, 2026
Overall score
50.2
reasoningtool callingimage inputvideo inputprompt caching

upstage

Solar Pro 4

Blended / 1M
$0.262
Context
524K
Released
Aug 10, 2026
Overall score
Not evaluated
reasoningtool callingprompt caching

Specs and pricing

MetricNemotron 3.5 Super VLSolar Pro 4
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.

$0.237win$0.262
Input price / 1M$0.150$0.150
Output price / 1M$0.500win$0.600
Cached input / 1M

Price of an input token served from the prompt cache, where the provider publishes one.

$0.020win$0.030
Context window1.0Mwin524K
Max output tokens236Kwin131K

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, Solar Pro 4 below, both out of 100.

Long-horizon agentic
11.7
—
Coding
71.3
—
Reasoning
74.6
—
Browser
21.6
—
Data analysis
64.6
—
Research
66.7
—
Documents & design
62.8
—

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.

WorkloadNemotron 3.5 Super VLSolar Pro 4
Support chatbot

1.2K in / 400 out × 200K requests

$66.64/mo$75.36/mo
RAG assistant

8K in / 600 out × 100K requests

$98.00/mo$108.00/mo
Coding agent

40K in / 4K out × 20K requests

$87.20/mo$100.80/mo
Document extraction

20K in / 1.5K out × 50K requests

$181.00/mo$189.00/mo
Bulk classification

500 in / 20 out × 5M requests

$360.00/mo$375.00/mo
Run these two through the cost calculator

Which should you pick?

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 Solar Pro 4 FAQ

Which is better, Nemotron 3.5 Super VL or Solar Pro 4?

Nemotron 3.5 Super VL is the cheaper of the two; neither can be ranked on quality here. Solar Pro 4 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 Nemotron 3.5 Super VL cheaper than Solar Pro 4?

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 Solar Pro 4 at $0.262 — Nemotron 3.5 Super VL is 11% cheaper. Input and output are priced separately — Nemotron 3.5 Super VL charges $0.150 in and $0.500 out, Solar Pro 4 charges $0.150 and $0.600 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Does Nemotron 3.5 Super VL or Solar Pro 4 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 Solar Pro 4. 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 Solar Pro 4 support prompt caching?

Both publish a cached-input rate: $0.020 per million for Nemotron 3.5 Super VL and $0.030 for Solar Pro 4, against full input rates of $0.150 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.