NVIDIA Nemotron 3 Super: Pricing & Benchmarks (2026)

NVIDIA Is Now an LLM Vendor, Not Just a GPU Vendor

For most of the last three years, NVIDIA's role in the LLM economy has been simple: they sell the picks and shovels. Every dollar spent on GPT-5, Claude, or Gemini eventually pays for an H100 or B200. But in late 2025 and early 2026, NVIDIA started doing something different — shipping their own family of open-weight reasoning models, Nemotron, and offering them as a hosted API on build.nvidia.com at prices that undercut almost every closed-source competitor.

Three Nemotron models matter right now for cost-conscious teams:

• Nemotron 3 Super 120B — flagship reasoning model, 262K context
• Llama 3.3 Nemotron Super 49B — distilled, mid-tier, 128K context
• Nemotron Nano 9B v2 — small, ultra-cheap, 128K context

This post benchmarks all three against the two other big "open-ish" pricing tiers in 2026: Meta's Llama 4 family and OpenAI's GPT-OSS models — strictly on cost-per-1M-tokens for representative workloads.

Cost-Per-1M-Tokens: The Headline Numbers

All prices in USD per million tokens, current as of April 2026 on the providers' first-party hosted APIs (build.nvidia.com, api.openai.com for GPT-OSS, Together AI for Llama 4 reference pricing).

The headline: at every weight class, Nemotron is 15-40% cheaper than the equivalent Llama 4 or GPT-OSS tier on first-party pricing. Nano 9B is the cheapest serious reasoning-capable model on the market right now.

Realistic Workload: 1M Requests/Month

Headline rates are easy to misread. The number that matters is monthly cost for a real workload. Let's price out a typical RAG pipeline: 2,000 input tokens, 500 output tokens per request, 1M requests/month.

For pure cost, the picture is unambiguous — Nemotron wins every weight class. The 120B flagship is $150/month cheaper than its closest competitor (GPT-OSS 120B) and $750/month cheaper than Llama 4 Maverick at this volume.

You can run your own numbers in our calculator or compare any two of these models head-to-head.

What About Quality?

Cost only matters if quality holds up. Based on NVIDIA's published evals (MMLU-Pro, GPQA, MATH, HumanEval) and independent third-party benchmarks released in March-April 2026:

• Nemotron 3 Super 120B matches or beats GPT-OSS 120B on math (MATH-500: 88.4 vs 86.1) and reasoning (GPQA: 64.2 vs 62.8). Trails Llama 4 Maverick by ~3 points on broad-knowledge MMLU-Pro but wins on tool use.
• Nemotron Llama 3.3 Super 49B is essentially a distilled Llama 3.3 70B — same architecture family, ~2 points lower on most benchmarks, ~60% the cost of Llama 4 Scout.
• Nemotron Nano 9B v2 punches above its weight: HumanEval 76.8 (better than Llama 3.1 8B's 66.5) at the cheapest hosted price of any 2026 reasoning model.

The summary: for math, code, and tool-use heavy workloads, Nemotron is now the price-performance leader. For broad-knowledge chat and creative writing, Llama 4 Maverick still has an edge — but at 75% higher cost.

Throughput and Latency

NVIDIA hosts Nemotron on their own infrastructure, which means it runs on the latest Blackwell B200 silicon. Reported tokens/second on build.nvidia.com:

Nano 9B is the throughput champion of the group — useful for streaming UIs and real-time agents where time-to-first-token matters more than per-token quality.

Caveats Before You Migrate

A few things to know before you swap your production traffic:

• No batch API yet. OpenAI and Anthropic both offer 50% discounts via Batch API. NVIDIA does not (as of April 2026). For async-heavy workloads, GPT-OSS via OpenAI's Batch API can actually be cheaper than Nemotron — see our Batch API calculator.
• Rate limits are tighter than OpenAI/Anthropic on the free tier. Pay-as-you-go tier matches industry norms.
• No prompt caching discount — Anthropic and OpenAI both offer ~90% off cached input tokens. For long-system-prompt workloads, this can flip the cost equation.
• Open weights = self-hosting option. All three Nemotron models are released under permissive licenses. If your monthly bill exceeds ~$5,000, self-hosting on rented H100s starts to win.

Who Should Switch

• High-volume RAG and document-processing teams running on GPT-OSS 120B → Nemotron 3 Super 120B saves ~13% with similar quality.
• Cost-sensitive startups running cheap classification or extraction → Nemotron Nano 9B v2 is the cheapest serious model on the market.
• Llama 4 Maverick users who don't need broad-knowledge reasoning → Nemotron 3 Super 120B saves ~43% on the same workload.

Who Should Stay Put

• Heavy Batch API users on OpenAI or Anthropic — the 50% async discount still wins.
• Long-system-prompt workloads with prompt caching active on Anthropic — the 90% cache discount is hard to beat.
• Anything needing GPT-5-class broad reasoning — Nemotron isn't there yet.

Bottom Line

NVIDIA Nemotron is the most cost-effective hosted reasoning family on the market in April 2026, full stop. If you're paying for GPT-OSS or Llama 4 today and your workload is reasoning-heavy or budget-constrained, the migration math almost always favors switching.

Run your own numbers — Nemotron 3 Super 120B, Llama 3.3 Nemotron Super 49B, Nemotron Nano 9B v2 — or compare any pair on the model comparison tool.