LANGUAGE MODEL Nvidia Last updated:

Llama 3.3 Nemotron Super 49B v1 (Non-reasoning)

Llama 3.3 Nemotron Super 49B v1 (Non-reasoning) is an API model from Nvidia. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.

API Docs ↗
Intelligence
Below avg
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on Artificial Analysis. Cost buckets are fixed dollar thresholds keyed off output-token price ($/M out).
Intelligence
  • Top 1%≤ 1%
  • Top 5%≤ 5%
  • Top 10%≤ 10%
  • Good≤ 25%
  • Medium≤ 50%
  • Below avg> 50%
Speed
  • Top 1%≥ 345 tok/s
  • Top 5%≥ 237 tok/s
  • Top 10%≥ 196 tok/s
  • Good≥ 146 tok/s
  • Medium≥ 90 tok/s
  • Slow< 90 tok/s
Cost
  • Freeopen weights · self-host
  • Low< $1 / M out
  • Moderate$1–5 / M out
  • High≥ $5 / M out
Artificial Analysis ↗ Hugging Face ↗

Core Capabilities

Long Documents
Handles entire codebases, books, and multi-doc RAG.

Context Window

128k tokens
≈ 98 pages
4k Chat 聊天
32k Long docs 长文档
128k This model 本模型
400k Multi-doc 多文档
1M Codebase 整个代码库
10M

Availability

API
Available
Product / App
Not available
Open Source
Not released
Enterprise
Contact sales

Pricing Model

Pay per token
Input and output billed separately.
Pay-per-token

Capability / Performance

Where this model sits relative to the middle 60% of models in the tree. All scores are 0–10 (higher is better).

Lower 20% Upper 80% This model
Reasoning
AA Intelligence Index · scaled to 10
1.7
5.6
2.6
Coding
SciCode · scaled to 10
1.8
4.3
2.8
Agentic tasks
Terminal-Bench Hard · scaled to 10
0.2
3.6
0.0
Context / memory
Context window size · log-scaled
6.0
9.0
6.0
Lower 20% 20th percentile — 20% of models score below this This model Where the current model lands Upper 80% 80th percentile — only 20% of models score above this Percentile boundaries are computed across every model in the tree that reports the underlying benchmark for each capability.

What it feels like

  • Same architecture as 3.1 70B but post-training advances delivered notable reasoning + math gains
  • 92.1% on IFEval — beats Llama 3.1 405B (88.6) and GPT-4o (84.6) on instruction following
  • MATH 77.0% (vs 67.8% for 3.1 70B); HumanEval 88.4%; MMLU Chat 86.0%
Reviews: DataCamp — Llama 3.3 70B review ↗ · Artificial Analysis — Llama 3.3 70B ↗ · Analytics Vidhya — Llama 3.3 70B is here, 25x cheaper ↗

Best use cases

  • Open-weights production where 405B is too expensive but 8B is too weak
  • Self-hosted RAG and code-completion stacks needing strong instruction-following
  • Multilingual reasoning (MGSM 91.1%) at frontier-tier quality

Tools to try

NIM Microservices Build catalog

Not ideal for

  • Frontier reasoning by mid-2025 — Llama 4 family and competitors moved past it
  • Multimodal tasks (text-only; vision lives in Llama 3.2 Vision / Llama 4)