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Nemotron 3 Ultra vs Qwen3.6 Plus

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

Qwen3.6 Plus wins outright — it scores higher and costs less.

Qwen3.6 Plus leads by 1.5 points overall while listing 44% 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. Qwen3.6 Plus also leads on measured cost per point of capability, at $0.1262 per point.

nvidia

Nemotron 3 Ultra

Blended / 1M
$1.05
Context
262K
Released
Jun 4, 2026
Overall score
67.4
reasoningtool callingprompt caching

qwen

Qwen3.6 Plus

Blended / 1M
$0.731
Context
1M
Released
Apr 2, 2026
Overall score
68.9
reasoningtool callingimage inputvideo input

Specs and pricing

MetricNemotron 3 UltraQwen3.6 Plus
LiveBench overall

Mean of the seven LiveBench category scores, 0–100. Higher is better.

67.468.9win
Cost per point

Measured benchmark spend divided by overall score — dollars per point of capability.

$0.2118$0.1262win
Blended price / 1M

3:1 input:output mix, the usual shape of production traffic.

$1.05$0.731win
Input price / 1M$0.600$0.325win
Output price / 1M$2.40$1.95win
Cached input / 1M

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

$0.120
Context window262K1Mwin
Max output tokens183Kwin66K

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, Qwen3.6 Plus below, both out of 100.

Agentic coding
38.7
41.4
Coding
70.7
78.2
Reasoning
74.7
75.8
Mathematics
88.7
83.7
Data analysis
54.5
69.9
Language
70.8
75.0
Instruction following
73.4
58.3

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 UltraQwen3.6 Plus
Support chatbot

1.2K in / 400 out × 200K requests

$301.44/mo$234.00/mo
RAG assistant

8K in / 600 out × 100K requests

$432.00/mo$377.00/mo
Coding agent

40K in / 4K out × 20K requests

$403.20/mo$416.00/mo
Document extraction

20K in / 1.5K out × 50K requests

$756.00/mo$471.25/mo
Bulk classification

500 in / 20 out × 5M requests

$1,500/mo$1,008/mo
Run these two through the cost calculator

Which should you pick?

You are running this at volume

Qwen3.6 Plus

Lowest measured cost per point of capability at $0.1262 per point — the gap compounds with every request.

Quality matters more than the bill

Qwen3.6 Plus

Highest overall LiveBench score of the two at 68.9.

The workload is coding or agentic work

Qwen3.6 Plus

Leads on agentic coding — 41.4 against 38.7.

You need to fit large documents in one call

Qwen3.6 Plus

Wider context window — 1M against 262K.

Nemotron 3 Ultra vs Qwen3.6 Plus FAQ

Which is better, Nemotron 3 Ultra or Qwen3.6 Plus?

Qwen3.6 Plus wins outright — it scores higher and costs less. Qwen3.6 Plus leads by 1.5 points overall while listing 44% 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. Qwen3.6 Plus also leads on measured cost per point of capability, at $0.1262 per point.

Is Nemotron 3 Ultra cheaper than Qwen3.6 Plus?

Qwen3.6 Plus is cheaper. On a 3:1 input:output blend, Nemotron 3 Ultra lists at $1.05 per million tokens and Qwen3.6 Plus at $0.731 — Qwen3.6 Plus is 44% cheaper. Input and output are priced separately — Nemotron 3 Ultra charges $0.600 in and $2.40 out, Qwen3.6 Plus charges $0.325 and $1.95 — so the model that looks cheaper flips depending on how output-heavy your workload is.

Nemotron 3 Ultra vs Qwen3.6 Plus: which scores higher on benchmarks?

Nemotron 3 Ultra scores 67.4 and Qwen3.6 Plus scores 68.9 overall on LiveBench, the mean of its seven categories. That is a 1.5-point lead for Qwen3.6 Plus. 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 Qwen3.6 Plus?

Qwen3.6 Plus. 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 Qwen3.6 Plus at $0.1262.

Does Nemotron 3 Ultra or Qwen3.6 Plus have a bigger context window?

Qwen3.6 Plus has the larger context window: 262K for Nemotron 3 Ultra against 1M for Qwen3.6 Plus. 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 Qwen3.6 Plus support prompt caching?

Nemotron 3 Ultra publishes a cached-input rate of $0.120 per million tokens against a full input rate of $0.600. The catalogue lists no separate cached rate for Qwen3.6 Plus, which means the provider does not price it separately here — not that caching is unavailable.

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.
  • ScoresLiveBench 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.