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Model profile
Meta

Llama 3.1 Nemotron Instruct 70B

Llama 3.1 Nemotron Instruct 70B is a budget text-first model from Meta with a balanced runtime profile, unknown context posture, and the clearest fit around long-context research / multimodal.

Best for: Long-context research / MultimodalBalanced latencyUnknown contextBudget pricing
Intelligence
13.4

Benchmark blend

Coding
10.8

Dev workflow signal

Context
N/A

Unknown

Input Price
$1.20

Budget tier

Decision snapshot
36

Llama 3.1 Nemotron Instruct 70B currently reads as a budget text-first option with unknown context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Long-context research / Multimodal
Latency tier
Balanced
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
13.4
13

General reasoning and benchmark headroom.

Limited
Speed
37 tok/s
60

TTFT 0.56s

Competitive
Context
N/A
34

How much prompt and task state can stay in view.

Limited
Price
$1.20
86

$1.20 output / 1M

Efficient

Editorial Profile

Llama 3.1 Nemotron Instruct 70B 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 11Math score 11

The Llama 3.1 Nemotron Instruct 70B AI model by Meta.

Identity

Meta text-first profile

Positioning

Long-context research / Multimodal with unknown context and balanced 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 is balanced rather than ultra-fast, which is fine for most workflows but not the snappiest tier.

  • 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
13.4
MMLU Pro
69.0%
GPQA
46.5%
HLE
4.6%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
10.8
LiveCodeBench
0.169
SciCode
23.3%
Math

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

Math Index
11.0
AIME
24.7%
AIME 2025
11.0%
Math 500
73.3%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
30.7%
TAU2
23.1%
TerminalBench Hard
4.5%
LCR
7.0%

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
$1.20
Output
per 1M output tokens
$1.20
Blended
AA 3:1 mix
$1.20

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.