// head_to_head
Nemotron 3 Ultra vs GPT-5.6 Sol
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.
GPT-5.6 Sol scores higher, Nemotron 3 Ultra costs less — it depends on your workload.
GPT-5.6 Sol is ahead by 13.7 points overall, and Nemotron 3 Ultra lists 3.8× cheaper per blended million tokens. Whether 13.7 points is worth that depends on how much a wrong answer costs you. Nemotron 3 Ultra also leads on measured cost per point of capability, at $0.2118 per point.
nvidia
Nemotron 3 Ultra
- Blended / 1M
- $1.05
- Context
- 262K
- Released
- Jun 4, 2026
- Overall score
- 67.4
openai
GPT-5.6 Sol
- Blended / 1M
- $4.00
- Context
- 1.1M
- Released
- Jul 9, 2026
- Overall score
- 81.1
Specs and pricing
| Metric | Nemotron 3 Ultra | GPT-5.6 Sol |
|---|---|---|
| LiveBench overall Mean of the seven LiveBench category scores, 0–100. Higher is better. | 67.4 | 81.1win |
| Cost per point Measured benchmark spend divided by overall score — dollars per point of capability. | $0.2118win | $0.2870 |
| Blended price / 1M 3:1 input:output mix, the usual shape of production traffic. | $1.05win | $4.00 |
| Input price / 1M | $0.600win | $2.00 |
| Output price / 1M | $2.40win | $10.00 |
| Cached input / 1M Price of an input token served from the prompt cache, where the provider publishes one. | $0.120win | $0.200 |
| Context window | 262K | 1.1Mwin |
| Max output tokens | 183Kwin | 128K |
Benchmarks by category
An overall score averages away the thing you probably care about. These are the seven LiveBench categories scored separately — Nemotron 3 Ultra on top, GPT-5.6 Sol 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 Ultra | GPT-5.6 Sol |
|---|---|---|
| Support chatbot 1.2K in / 400 out × 200K requests | $301.44/mo | $1,150/mo |
| RAG assistant 8K in / 600 out × 100K requests | $432.00/mo | $1,480/mo |
| Coding agent 40K in / 4K out × 20K requests | $403.20/mo | $1,392/mo |
| Document extraction 20K in / 1.5K out × 50K requests | $756.00/mo | $2,660/mo |
| Bulk classification 500 in / 20 out × 5M requests | $1,500/mo | $5,100/mo |
Which should you pick?
You are running this at volume
Nemotron 3 Ultra
Lowest measured cost per point of capability at $0.2118 per point — the gap compounds with every request.
Quality matters more than the bill
GPT-5.6 Sol
Highest overall LiveBench score of the two at 81.1.
The workload is coding or agentic work
GPT-5.6 Sol
Leads on agentic coding — 56.2 against 38.7.
You need to fit large documents in one call
GPT-5.6 Sol
Wider context window — 1.1M against 262K.
Nemotron 3 Ultra vs GPT-5.6 Sol FAQ
Which is better, Nemotron 3 Ultra or GPT-5.6 Sol?
GPT-5.6 Sol scores higher, Nemotron 3 Ultra costs less — it depends on your workload. GPT-5.6 Sol is ahead by 13.7 points overall, and Nemotron 3 Ultra lists 3.8× cheaper per blended million tokens. Whether 13.7 points is worth that depends on how much a wrong answer costs you. Nemotron 3 Ultra also leads on measured cost per point of capability, at $0.2118 per point.
Is Nemotron 3 Ultra cheaper than GPT-5.6 Sol?
Nemotron 3 Ultra is cheaper. On a 3:1 input:output blend, Nemotron 3 Ultra lists at $1.05 per million tokens and GPT-5.6 Sol at $4.00 — Nemotron 3 Ultra is 3.8× cheaper. Input and output are priced separately — Nemotron 3 Ultra charges $0.600 in and $2.40 out, GPT-5.6 Sol charges $2.00 and $10.00 — so the model that looks cheaper flips depending on how output-heavy your workload is.
Nemotron 3 Ultra vs GPT-5.6 Sol: which scores higher on benchmarks?
Nemotron 3 Ultra scores 67.4 and GPT-5.6 Sol scores 81.1 overall on LiveBench, the mean of its seven categories. That is a 13.7-point lead for GPT-5.6 Sol. 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 Ultra or GPT-5.6 Sol?
Nemotron 3 Ultra. 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 Ultra works out at $0.2118 per point and GPT-5.6 Sol at $0.2870.
Does Nemotron 3 Ultra or GPT-5.6 Sol have a bigger context window?
GPT-5.6 Sol has the larger context window: 262K for Nemotron 3 Ultra against 1.1M for GPT-5.6 Sol. 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 Ultra and GPT-5.6 Sol support prompt caching?
Both publish a cached-input rate: $0.120 per million for Nemotron 3 Ultra and $0.200 for GPT-5.6 Sol, against full input rates of $0.600 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 2026-06-25, 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.