Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning)
Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) is an API model from Nvidia. It’s positioned for general text tasks—work that benefits from iteration, not just one-shot answers.
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
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
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
- Llama 3.1 405B was the world's largest openly available foundation model at release
- Trained on 15T tokens across 16K+ H100 GPUs — Meta opted for dense decoder over MoE for training stability
- MMLU 87.3% (Instruct) — matched or beat GPT-4-Turbo (86.5%) and Claude 3 Opus (86.8%)
Best use cases
- Self-hosted frontier-quality LLM deployments where you can spare ~8 H100s
- Distillation source for smaller open models (license-permissive synthetic data)
- Multilingual production work in the 8 supported languages
Tools to try
Not ideal for
- Single-GPU / edge deployments — 405B is too big without aggressive quantization
- Frontier reasoning by 2025 — Llama 3.3 70B and Llama 4 family supersede it