LANGUAGE MODEL Nvidia Last updated:

NVIDIA Nemotron Nano 12B v2 VL (Reasoning)

NVIDIA Nemotron Nano 12B v2 VL (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
Speed
Medium
135 tok/s output
Cost
Low
$0.20 in / $0.60 out
Context
128K
Up to 128,000 tokens
How are Intelligence, Speed & Cost bucketed?
Intelligence and Speed buckets are percentile ranks on Artificial Analysis. Cost buckets are fixed dollar thresholds keyed off output-token price ($/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
GitHub ↗ 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

Free / self-host
Open weights — pay only for compute.
Self-host

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
2.1
Coding
SciCode · scaled to 10
1.8
4.3
2.6
Agentic tasks
Terminal-Bench Hard · scaled to 10
0.2
3.6
0.5
Context / memory
Context window size · log-scaled
6.0
9.0
6.0
Cost efficiency
Input price ($/M tokens) · cheaper scores higher
6.2
10.0
10.0
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

  • Language model from Nvidia — see the linked sources below for benchmark and review coverage

Best use cases

  • General-purpose tasks within Nvidia's deployment footprint
  • See the model spec and sources block for benchmarked use cases

Tools to try

NIM Microservices Build catalog

Not ideal for

  • Tasks far outside the modalities listed in this model's spec
  • Workflows where a more recent successor in the same family scores higher