Model profile
Meta

Llama 3.1 Instruct 405B

Llama 3.1 Instruct 405B is a mid-range-priced text-first model from Meta with balanced runtime profile, partial context coverage, and the clearest fit around agent workflows / reasoning.

Best for: Agent workflows / ReasoningBalanced latencyN/A contextMid-range pricing
Intelligence
17.4

Benchmark blend

Coding
14.5

Dev workflow signal

Context
N/A

N/A

Input Price
$2.75

Mid-range tier

Decision snapshot
34

Llama 3.1 Instruct 405B currently reads as a mid-range text-first option with partially published context and a balanced runtime profile.

Overall profile
Use-case specific
Best for
Agent workflows / Reasoning
Latency tier
Balanced
Price tier
Mid-range
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
17.4
17

General reasoning and benchmark headroom.

Limited
Speed
31 tok/s
59

TTFT 0.47s

Competitive
Context
N/A
N/A

How much prompt and task state can stay in view.

Unavailable
Price
$2.75
62

$6.50 output / 1M

Competitive

Editorial Profile

Llama 3.1 Instruct 405B 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 15Math score 3

The Llama 3.1 Instruct 405B AI model by Meta.

Identity

Meta text-first profile

Positioning

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

Cost posture

Balanced spend profile. Easier to justify in mixed production and exploration workloads.

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

Tradeoffs
  • Costs look manageable, but still deserve attention in always-on agents or batch jobs.

  • 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.

  • 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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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
17.4
MMLU Pro
73.2%
GPQA
51.5%
HLE
4.2%
Coding

Software implementation, debugging quality, and coding benchmark signal.

Coding Index
14.5
LiveCodeBench
0.305
SciCode
29.9%
Math

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

Math Index
3.0
AIME
21.3%
AIME 2025
3.0%
Math 500
70.3%
Agent / tool use

Long-horizon execution quality and interactive benchmark evidence.

IFBench
39.0%
TAU2
19.0%
TerminalBench Hard
6.8%
LCR
24.3%

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
$2.75
Output
per 1M output tokens
$6.50
Blended
AA 3:1 mix
$3.69

This model sits in a balanced spend range. It is easier to justify across both production and exploratory workflows.

Metadata

Raw source tables at the end

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