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

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Starter is free forever - 1 Project, 100 credits/month, 1 MCP. No card.

Verdict

Nemotron 3 Ultra offers a massive 262K context window at $0.50/$2.20 per Mtok, making it competitive for long-document work where cost matters more than bleeding-edge reasoning. Without public benchmarks, it's hard to assess reasoning quality against peers like GPT-4o or Claude Sonnet, but the pricing and context size suggest NVIDIA is targeting bulk processing and enterprise document workflows. Reach for this when you need to ingest large codebases or contracts without hitting token limits, but expect to validate output quality on your own tasks before committing.

Best for

Long-document analysis under budget constraints
Large codebase context for refactoring
Multi-document synthesis in enterprise workflows
Cost-sensitive batch processing tasks

Strengths

The 262K context window matches Claude Sonnet 3.5's capacity while undercutting it on input cost by roughly 40%. At $0.50 per million input tokens, you can feed entire technical manuals or multi-file codebases without aggressive chunking. NVIDIA's GPU heritage suggests strong inference speed, though public latency data isn't available yet. The pricing structure favors read-heavy workloads where you send large prompts and expect concise outputs.

Trade-offs

No public benchmarks means you're flying blind on reasoning quality, math performance, and instruction-following compared to established models. The $2.20 output cost is higher than GPT-4o Mini ($0.60) and Gemini 1.5 Flash ($0.30), so verbose responses get expensive fast. Without published MMLU, HumanEval, or GPQA scores, you'll need to run your own evals before trusting it for mission-critical logic or code generation.

Specifications

Provider: nvidia
Category: llm
Context length: 256,000 tokens
Max output: 32,768 tokens
Modalities: text
License: proprietary
Released: 2026-06-04

Pricing

Input: $0.63/Mtok
Output: $3.13/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

Seats5 peopleMessages / seat / day80Avg turn size2 ktokOutput share30 %

Estimated monthly spend

$24.20

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

Provider	Context	Input	Output	P50 latency	Throughput	30d uptime
nvidia	262k	$0.50/Mtok	$2.20/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 Projects have used this model yet to share anonymised team stats. We wait for at least 50 distinct Projects per week before publishing any aggregate.

Starter prompts

Codebase Architecture Summary

I'm pasting the contents of 47 Python files from our backend service. Read through all of them and describe the overall architecture, identifying the main design patterns, potential bottlenecks, and any inconsistencies in how modules interact.

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Multi-Contract Comparison

Below are three SaaS vendor agreements (each 15-20 pages). Compare their data retention policies, liability caps, and termination clauses. Highlight any red flags or unusually favorable terms in a table format.

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Research Paper Synthesis

I'm providing five research papers on transformer attention mechanisms (total ~80 pages). Summarize the consensus findings, note where authors disagree, and list any novel techniques mentioned in only one paper.

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Log File Root Cause Analysis

Here's a 50,000-line application log from a production incident. Identify the root cause by tracing error propagation backward, then list the sequence of failures that led to the outage.

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Technical Spec Consolidation

I'm attaching six internal wiki pages describing our API authentication flow—some overlap, some contradict. Produce one authoritative spec document that reconciles the differences and flags any unresolved ambiguities.

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