Llama 3.1 Instruct 405B

Llama 3.1 Instruct 405B

Metatext

API Available

Specifications

Context Window

Input Price/1M

$2.75

Output Price/1M

$6.50

Parameters

Speed

37 tok/s

Latency (TTFT)

580ms

Benchmarks

Llama 3.1 Instruct 405B results on the main AI model evaluation benchmarks. Higher scores indicate better performance.

Agentic

BenchmarkScoreMaximumMethodology
Terminal-Bench Hard7.0100.0

Coding

BenchmarkScoreMaximumMethodology
LiveCodeBench31.0100.0Artificial Analysis official API
SciCode30.0100.0
AA Coding Index14.5100.0Artificial Analysis official API

Knowledge

BenchmarkScoreMaximumMethodology
MMLU-Pro73.0100.0

Long Context

BenchmarkScoreMaximumMethodology
AA-LCR24.0100.0

Math

BenchmarkScoreMaximumMethodology
MATH-50070.3100.0Artificial Analysis official API
AA Math Index3.0100.0Artificial Analysis official API
AIME 20253.0100.0Artificial Analysis official API

overall

BenchmarkScoreMaximumMethodology
AA Intelligence Index17.4100.0Artificial Analysis official API

Reasoning

BenchmarkScoreMaximumMethodology
MMLU Pro73.2100.0Artificial Analysis official API
GPQA Diamond52.0100.0Artificial Analysis official API
IFBench39.0100.0
HLE4.0100.0

Tool Use

BenchmarkScoreMaximumMethodology
Tau²-Bench19.0100.0

Information

Release date
July 23, 2024
Tool Calling
❌ Not supported
Vision
❌ Not supported
Audio
❌ Not supported

Full Analysis: Llama 3.1 Instruct 405B

What is Llama 3.1 Instruct 405B?

Llama 3.1 Instruct 405B is an AI model developed by Meta, classified as a text model. It focuses on text processing and natural language generation. As a proprietary model, it is available via Meta's cloud API.

Pricing & Costs in 2026

Llama 3.1 Instruct 405B is usage-based, priced at $2.75/1M input tokens and $6.5/1M output tokens. For context: 1 million tokens is approximately 750,000 words, or about 10 average-length books. The mid-range pricing balances quality and cost for most professional applications.

Benchmarks & Performance

Llama 3.1 Instruct 405B was evaluated on 15 different benchmarks, covering categories like Agentic, Coding, Knowledge, Long Context, Math, overall, Reasoning, Tool Use. Results show moderate performance across available evaluations.

It's important to note that benchmarks measure specific aspects and don't capture the full user experience. Factors like instruction adherence, behavior in long conversations, and real-world task quality vary significantly between models and aren't always reflected in standard scores.

Recommended Use Cases

Llama 3.1 Instruct 405B specializes in text, offering advanced capabilities for creating and processing text content.

Comparison with Alternatives

In the 2026 AI model ecosystem, Llama 3.1 Instruct 405B competes directly with similarly capable models. As an open source model, it competes with Qwen (Alibaba), Mistral, and DeepSeek, as well as proprietary models like GPT, Claude, and Gemini. The choice between models depends on the specific use case, budget, latency requirements, and need for features like multimodality and tool calling.

For a detailed side-by-side comparison, use our comparison tool or check the overall model ranking.

Frequently Asked Questions

What is Llama 3.1 Instruct 405B?

Llama 3.1 Instruct 405B is an AI model developed by Meta. It is a text model.

How much does Llama 3.1 Instruct 405B cost?

Llama 3.1 Instruct 405B costs $2.75/1M input tokens and $6.5/1M output tokens. For heavy usage (e.g., a chatbot handling 100k messages/month), costs can range from $10 to $1,000 depending on volume.

How does Llama 3.1 Instruct 405B compare with other models?

In available benchmarks, Llama 3.1 Instruct 405B scored: Terminal-Bench Hard: 7/100, LiveCodeBench: 31/100, SciCode: 30/100. See the full table above for a detailed comparison.

Is Llama 3.1 Instruct 405B open source?

No, Llama 3.1 Instruct 405B is a proprietary model from Meta. It is available via cloud API. For open source alternatives, check our open source model ranking.

What is Llama 3.1 Instruct 405B best for?

Llama 3.1 Instruct 405B excels at general-purpose language tasks.

Last updated: June 01, 2026 View methodology →