Model profile
NVIDIA

NVIDIA Nemotron Nano 12B v2 VL (Reasoning)

NVIDIA Nemotron Nano 12B v2 VL (Reasoning) is a budget-priced text-first model from NVIDIA with fast runtime profile, partial context coverage, and the clearest fit around agent workflows / reasoning.

Best for: Agent workflows / ReasoningFast latencyN/A contextBudget pricing
Intelligence
14.9

Benchmark blend

Coding
11.8

Dev workflow signal

Context
N/A

N/A

Input Price
$0.20

Budget tier

Decision snapshot
46

NVIDIA Nemotron Nano 12B v2 VL (Reasoning) currently reads as a budget text-first option with partially published context and a fast runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Reasoning
Latency tier
Fast
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis

Decision Strip

Decision rail before the raw tables

Core buy-side signals stay in one pass. The rest of the page expands only after intelligence, speed, context, and price are clear.

Intelligence
14.9
15

General reasoning and benchmark headroom.

Limited
Speed
137 tok/s
99

TTFT 0.21s

Above average
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$0.20
86

$0.60 output / 1M

Efficient

Editorial Profile

NVIDIA Nemotron Nano 12B v2 VL (Reasoning) in one narrative

Positioning, tradeoffs, and fit are consolidated into one read instead of repeating the same story across separate cards.

Use-case specificCoding score 12Math score 75

The NVIDIA Nemotron Nano 12B v2 VL (Reasoning) AI model by NVIDIA.

Identity

NVIDIA text-first profile

Positioning

Agent workflows / Reasoning with partially published context and fast runtime.

Cost posture

Efficient spend profile. More comfortable for sustained prompt volume if the capability fit is right.

Strengths
  • Latency and throughput look responsive enough for interactive loops.

Tradeoffs
  • Budget-friendly input pricing is a strength, but raw capability may vary by workload.

  • Current metadata points to a text-first profile rather than a broad multimodal one.

  • Context limits are only partially published, so long-session planning needs extra validation.

Best fit
  • Focused chat, retrieval-augmented flows, and narrower production tasks.

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Context
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Input Price
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Context
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Benchmarks

Grouped by job-to-be-done

Only benchmark categories with actual signal are shown. Secondary values stay as simple definitions instead of nested micro-cards.

General intelligence

Broad reasoning, knowledge depth, and flagship benchmark posture.

Intelligence Index
14.9
MMLU Pro
75.9%
GPQA
57.2%
HLE
5.3%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
11.8
LiveCodeBench
0.694
SciCode
26.2%
Math

Formal reasoning, structured problem solving, and competition-style math.

Math Index
75.0
AIME 2025
75.0%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
31.9%
TAU2
21.3%
TerminalBench Hard
4.5%
LCR
40.0%

Specs & Pricing

Technical snapshot and cost posture

Specs stay neutral, pricing gets emphasis through values rather than extra containers. Raw provider internals remain in metadata at the end.

Technical snapshot
Context Window
N/A
Vision
Text-first
Price architecture
Input
per 1M input tokens
$0.20
Output
per 1M output tokens
$0.60
Blended
AA 3:1 mix
$0.30

This model is relatively efficient on price. It is the easier fit when sustained prompt volume matters.

Metadata

Raw source tables at the end

Verification details remain available, but the page no longer forces them ahead of the editorial read.