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Model profile
Meta

Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning)

Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) is a budget text-first model from Meta with a heavy runtime profile, unknown context posture, and the clearest fit around coding / long-context research.

Best for: Coding / Long-context researchHeavy latencyUnknown contextBudget pricing
Intelligence
14.4

Benchmark blend

Coding
0.493

Dev workflow signal

Context
N/A

Unknown

Input Price
$0.00

Budget tier

Decision snapshot
40

Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) currently reads as a budget text-first option with unknown context and a heavy runtime profile.

Overall profile
Use-case specific
Best for
Coding / Long-context research
Latency tier
Heavy
Price tier
Budget
Source coverage
OpenRouterArtificial Analysis
Compare this modelSee leaderboard

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
14.4
14

General reasoning and benchmark headroom.

Limited
Speed
N/A
46

Latency data is partial.

Situational
Context
N/A
34

How much prompt and task state can stay in view.

Limited
Price
$0.00
86

$0.00 output / 1M

Efficient

Editorial Profile

Llama 3.1 Nemotron Nano 4B v1.1 (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 49Math score 50

The Llama 3.1 Nemotron Nano 4B v1.1 (Reasoning) AI model by Meta.

Identity

Meta text-first profile

Positioning

Coding / Long-context research with unknown context and heavy runtime.

Cost posture

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

Strengths

  • The available source data suggests a balanced profile rather than one dominant edge.

Tradeoffs

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

  • Latency profile is better for deliberate runs than rapid back-and-forth chat.

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

Best fit

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

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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
14.4
MMLU Pro
55.6%
GPQA
40.8%
HLE
5.1%
Coding

Software implementation, debugging quality, and coding benchmark signal.

LiveCodeBench
0.493
SciCode
10.1%
Math

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

Math Index
50.0
AIME
70.7%
AIME 2025
50.0%
Math 500
94.7%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
25.5%
TAU2
11.7%
LCR
0.0%

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
$0.00
Output
per 1M output tokens
$0.00
Blended
AA 3:1 mix
$0.00

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.