Last refreshed 2026-06-15. Next refresh: weekly.
Why use Mixtral 8x7B on AWS Bedrock?
AWS Bedrock offers Mixtral 8x7B with pay-as-you-go pricing at $0.45/1M input tokens. AWS Bedrock is Amazon's fully managed foundation-model service, providing unified API access to top models from Anthropic, Meta, Mistral, and other leading AI labs with built-in tools for RAG, fine-tuning, and AI agent development.
Compare Mixtral 8x7B across 18 providers to find the best fit for your use caseSetup recipe
Python + curlpip install boto3export AWS_ACCESS_KEY_ID=...import boto3
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="mixtral-8x7b",mixtral-8x7bRequest example
import boto3
# Reads AWS_ACCESS_KEY_ID, AWS_SECRET_ACCESS_KEY, AWS_DEFAULT_REGION from env
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="mixtral-8x7b",
messages=[{
"role": "user",
"content": [{"text": "Hello"}]
}]
)
print(response["output"]["message"]["content"][0]["text"])Gotchas
- Use Amazon Bedrock model IDs, e.g. "anthropic.claude-3-opus-20240229-v1:0" for on-demand, or cross-region inference profile IDs like "us.anthropic.claude-opus-4-7-20251101-v1:0". These differ from the public model slug.
- The endpoint template includes a region segment; set the same region in your SDK/client configuration.
- The examples expect AWS_ACCESS_KEY_ID; rename it only if your application config maps the new variable.
Compare Mixtral 8x7B Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Databricks Foundation Model Serving | $0.50 | $1.00 |
| NVIDIA NIM | — | — |
| GCP Vertex AI | $0.40 | $1.20 |
| AWS Bedrock | $0.45 | $0.70 |
| OctoAI API (Deprecated) | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.45 |
| Output tokens | $0.70 |
Capabilities
No model capability flags are currently sourced.
About Mixtral 8x7B
Mixtral 8x7B, developed by Mistral AI, features a cutting-edge Mixture of Experts (MoE) architecture, utilizing eight experts with seven billion parameters each, yielding a total of 46.7 billion parameters. This architecture activates only two experts per token, allowing for efficient processing and a 6x faster inference rate compared to Llama 2 70B. The model excels in performance, surpassing Llama 2 70B and competing with GPT-3.5 on numerous benchmarks. It supports multiple languages and can handle context up to 32,000 tokens, enhancing understanding of lengthy text.