# Llama 3.1 405B Instruct
##  Meta — Llama 3.1 405B Instruct
## Model Details
Llama 3.1 405B Instruct is Meta's largest open model with 405 billion parameters and a 128K context window, supporting tool use and multilingual tasks. For more information about model development and performance, see the [model/service card](https://www.llama.com/docs/model-cards-and-prompt-formats/llama3_1/).
+ **Model launch date:** Jul 23, 2024
+ **EOL no sooner than:** Jul 23, 2025
+ **Legacy period:** at least 6 months
+ **Model lifecycle policy:** [Model lifecycle (For Models Launched Prior to Sept 7 2026)](model-lifecycle-legacy.md)
+ **Model EOL date:** Legacy: July 7, 2026
+ **End User License Agreements and Terms of Use:** [View](https://github.com/meta-llama/llama-models/blob/main/models/llama3_1/LICENSE)
+ **Model lifecycle:** Legacy
+ **Context window:** 128K tokens
+ **Max output tokens:** 4K
+ **Knowledge cutoff:** Dec 2023
| **Input Modalities** | **Output Modalities** | **[APIs supported](apis.html)** | **[Endpoints supported](endpoints.html)** |
| --- | --- | --- | --- |
|  Audio |  Embedding |  Responses |  bedrock-runtime |
|  Image |  Image |  Chat Completions |  bedrock-mantle |
|  Speech |  Speech |  Invoke | |
|  Text |  Text |  Converse | |
|  Video |  Video | | |
**Tip**
Whenever possible, we recommend using the `bedrock-runtime` endpoint for new applications. See [Endpoints supported by Amazon Bedrock](endpoints.md) for details.
## Capabilities and Features
***Bedrock Features***
**Features supported using `bedrock-runtime` endpoint**
| **Supported** | **Not Supported** |
| --- | --- |
| +  [Response streaming](/bedrock/latest/APIReference/API_runtime_InvokeModelWithResponseStream.html)
+  [Guardrails](guardrails.html)
+  [Client-side tool calling](tool-use.html)
+  [Prompt management](prompt-management.html)
+  [Flows](flows.html)
+  [Agents](agents.html) | +  [Intelligent prompt routing](prompt-routing.html)
+  [Abuse detection](abuse-detection.html)
+  [Prompt optimization](prompt-management-optimize.html)
+  [Count tokens](count-tokens.html)
+  [Knowledge base](knowledge-base.html)
+  [Model evaluation](evaluation.html)
+  [Structured outputs](structured-outputs.html) |
## Pricing
For pricing information, see the [Amazon Bedrock Pricing](https://aws.amazon.com/bedrock/pricing/) page.
## Programmatic Access
Use the following model IDs and endpoint URLs to access this model programmatically. For more information about the available APIs and endpoints, see [APIs supported](apis.html) and [Endpoints supported](endpoints.html).
| **Endpoint** | **Model ID** | **In-Region endpoint URL** | **Geo inference ID** | **Global inference ID** |
| --- | --- | --- | --- | --- |
| bedrock-runtime | meta.llama3-1-405b-instruct-v1:0 | https://bedrock-runtime.{region}.amazonaws.com | us.meta.llama3-1-405b-instruct-v1:0 | Not supported |
*For example, if region is us-east-1 (N. Virginia), then the bedrock-runtime endpoint URL will be "https://bedrock-runtime.us-east-1.amazonaws.com" and for bedrock-mantle will be "https://bedrock-mantle.us-east-1.api.aws/v1".*
## Service Tiers
Amazon Bedrock offers multiple service tiers to match your workload requirements. **Standard** provides pay-per-token access with no commitment (set `"service_tier": "default"` or omit the field). **Priority** delivers the fastest response times for a price premium (set `"service_tier": "priority"`). **Flex** provides lower-cost access for flexible, non-time-sensitive workloads (set `"service_tier": "flex"`). **Reserved** provides dedicated throughput with a term commitment for predictable workloads; it is set at the account level rather than per request (contact your AWS account team to enable). For more information, see [service tiers](service-tiers-inference.html).
| **Standard** | **Priority** | **Flex** | **Reserved** |
| --- | --- | --- | --- |
|  |  |  |  |
## Regional Availability
***Regional availability at a glance***
Amazon Bedrock offers three inference options: **In-Region** keeps requests within a single Region for strict compliance, **Geo Cross-Region** routes across Regions within a geography (such as US, EU, and APAC) while respecting data residency, and **Global Cross-Region** routes anywhere worldwide when there are no residency constraints. Refer to the [Regional availability by models](models-region-compatibility.md) page for more details.
| **Region** | **In-Region** | **Geo** | **Global** |
| --- | --- | --- | --- |
| us-east-2 (Ohio) |  |  |  |
| us-west-2 (Oregon) |  |  |  |
***Geo inference details***
**Geo: US**
Geo Inference ID: `us.meta.llama3-1-405b-instruct-v1:0`
| **Source Region** | **Destination Regions** |
| --- | --- |
| us-east-2 (Ohio) | us-east-1 (N. Virginia), us-east-2 (Ohio), us-west-2 (Oregon) |
## Sample Code
**Step 1 - AWS Account:** If you have an AWS account already, skip this step. If you are new to AWS, sign up for an [AWS account](https://portal.aws.amazon.com/billing/signup).
**Step 2 - API key:** Go to the [Amazon Bedrock console](https://console.aws.amazon.com/bedrock/home#/api-keys/long-term/create) and generate a long-term API key.
**Step 3 - Get the SDK:** To use this getting started guide, you must have Python already installed. Then install the relevant software depending on the APIs you are using.
```
pip install boto3
```
**Step 4 - Set environment variables:** Configure your environment to use the API key for authentication.
```
AWS_BEARER_TOKEN_BEDROCK=""
```
**Step 5 - Run your first inference request:** Save the file as `bedrock-first-request.py`
------
#### [ Converse API ]
```
import boto3
client = boto3.client('bedrock-runtime', region_name='us-east-1')
response = client.converse(
modelId='meta.llama3-1-405b-instruct-v1:0',
messages=[
{
'role': 'user',
'content': [{'text': 'Can you explain the features of Amazon Bedrock?'}]
}
]
)
print(response)
```
------