Last refreshed 2026-06-15. Next refresh: weekly.
Why use DeepSeek V3.2 on AWS Bedrock?
AWS Bedrock offers DeepSeek V3.2 with pay-as-you-go pricing at $0.62/1M 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 DeepSeek V3.2 across 7 providers to find the best fit for your use caseInput / 1M
$0.62
Output / 1M
$1.85
Cache
Not sourced
Batch
Not sourced
Setup recipe
Python + curlInstall
pip install boto3Auth
export AWS_ACCESS_KEY_ID=...Call
import boto3
client = boto3.client("bedrock-runtime", region_name="us-east-1")
response = client.converse(
modelId="deepseek-v3.2",Model ID
deepseek-v3.2Request 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="deepseek-v3.2",
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 DeepSeek V3.2 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Fireworks AI | $0.56 | $1.68 |
| NVIDIA NIM | — | — |
| AWS Bedrock | $0.62 | $1.85 |
| OpenRouter | $0.25 | $0.38 |
| Microsoft Foundry | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.62 |
| Output tokens | $1.85 |
Capabilities
Structured OutputsCode Execution
About DeepSeek V3.2
DeepSeek V3.2 is DeepSeek's DeepSeek V3 model. It offers a 160K-token context window with weights openly available for self-hosting and scores 70 on SWE-bench Verified.
Get Started
Model Specs
Released2025-12-01
Parameters671B
Context160k
ArchitectureDecoder Only