Last refreshed 2026-06-15. Next refresh: weekly.
Why use Jurassic-2 Mid on AWS Bedrock?
AWS Bedrock offers Jurassic-2 Mid with pay-as-you-go pricing at $12.50/1M 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 Jurassic-2 Mid across 2 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="jurassic-2-mid",jurassic-2-midRequest 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="jurassic-2-mid",
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 Jurassic-2 Mid Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| AWS Bedrock | $12.50 | $12.50 |
| AI21 Studio | $12.50 | $12.50 |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $12.50 |
| Output tokens | $12.50 |
Capabilities
About Jurassic-2 Mid
Jurassic-2 Mid, developed by AI21 Labs, is a large language model that balances quality, speed, and cost, making it well-suited for complex language tasks such as chatbots and conversational interfaces. It has a parameter size of 17 billion and supports multiple languages including Spanish, French, German, Portuguese, Italian, and Dutch. Optimized for generating precise text from instruction-only prompts, the model is capable of zero-shot text generation without requiring examples. Despite its powerful capabilities, it shares common limitations with other LLMs, such as potential inaccuracies, lack of coherence, and the presence of training data biases.