LLMnvidia

NVIDIA: Nemotron 3 Ultra

NVIDIA Nemotron 3 Ultra is an open frontier-reasoning and orchestration model from NVIDIA, with 55B active parameters out of 550B total (MoE). Built on a hybrid Transformer-Mamba mixture-of-experts architecture, it...

Anyone in the Space can @-mention NVIDIA: Nemotron 3 Ultra with the team's shared context — pooled credits, one chat, one memory.

All models

Starter is free forever — 1 Space, 100 credits/month, 1 MCP. No card.

Verdict

Nemotron 3 Ultra offers a massive 262K context window at mid-tier pricing, making it a strong choice for document-heavy workflows where you need to process entire codebases or long transcripts in one pass. The $0.50/$2.50 per Mtok pricing sits between budget and premium tiers, though without public benchmarks it's harder to gauge where it stands on reasoning or instruction-following against Claude or GPT-4o. Reach for this when context length is your bottleneck and you're willing to trade proven benchmark scores for window size.

Best for

  • Processing entire codebases in context
  • Long-form document summarization tasks
  • Multi-document analysis and synthesis
  • Extended conversation threads with history
  • Large-scale data extraction workflows

Strengths

The 262K token context window is the headline feature — you can load roughly 200,000 words or 50+ typical documents without chunking. At $0.50 input per million tokens, it's cheaper than Claude Opus for ingesting large volumes of text. NVIDIA's GPU infrastructure typically delivers low latency, which matters when you're pushing hundreds of thousands of tokens through the model. The pricing structure favors read-heavy workloads where you load context once and generate concise outputs.

Trade-offs

No public benchmark data means you're flying blind on reasoning quality, instruction-following accuracy, and coding ability relative to GPT-4o or Claude Sonnet. The $2.50 output pricing is 5x the input rate, so verbose responses get expensive fast — this isn't the model for creative writing or long-form generation. NVIDIA's ecosystem is less mature than OpenAI or Anthropic for tooling and integrations. Without MMLU, HumanEval, or GSM8K scores, it's hard to predict performance on complex tasks.

Specifications

Provider
nvidia
Category
llm
Context length
262,144 tokens
Max output
16,384 tokens
Modalities
text
License
proprietary
Released
2026-06-04

Pricing

Input
$0.50/Mtok
Output
$2.50/Mtok
Model ID
nvidia/nemotron-3-ultra-550b-a55b

Per-token prices show what the model costs upstream. On Switchy your team draws from one shared org credit pool — one plan, one balance for everyone.

Team cost calculator

Estimated monthly spend
$19.36
17.6M tokens / month
5 seats · 80 msgs/day

Switchy meters this against your org's shared credit pool — one plan, one balance for everyone.

Providers

ProviderContextInputOutputP50 latencyThroughput30d uptime
nvidia262k$0.50/Mtok$2.50/Mtok

Performance

Performance snapshots are collected daily. Check back after the next ingestion run.

Benchmarks

Public benchmark scores are not available yet for this model. Check back after the next ingestion run.

Works well with

Top MCPs

Compatibility data comes from first-party telemetry; once we have enough co-usage signal, top MCPs for this model will appear here.

How Switchy teams use it

Not enough Spaces have used this model yet to share anonymised team stats. We wait for at least 50 distinct Spaces per week before publishing any aggregate.

Starter prompts

Codebase Architecture Analysis

Analyze this codebase for architectural patterns and anti-patterns. Identify tight coupling between modules, circular dependencies, and opportunities to extract shared logic. Provide a prioritized list of refactoring recommendations.
Open in a Space →

Multi-Document Synthesis

I've provided 30 research papers on the same topic. Synthesize the key findings, highlight areas of consensus and disagreement, and identify gaps in the current research. Structure your response as a literature review.
Open in a Space →

Long Transcript Extraction

Extract all action items, decisions, and open questions from this meeting transcript. For each action item, note who is responsible and any mentioned deadlines. Group by topic area.
Open in a Space →

Contract Comparison

Compare these five vendor contracts and highlight differences in liability clauses, termination terms, data handling requirements, and pricing structures. Flag any unusual or risky provisions.
Open in a Space →

Historical Context Retrieval

Based on everything we've discussed in this conversation, what are the three most important unresolved questions, and what additional information would help us make a decision?
Open in a Space →
Data last verified 2 hours ago.Sources aggregated hourly to weekly. See docs/architecture/model-directory.md.