Last refreshed 2026-06-15. Next refresh: weekly.
Why use Kimi K2.5 on AWS Bedrock?
AWS Bedrock offers Kimi K2.5 with pay-as-you-go pricing at $0.60/1M 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 Kimi K2.5 across 10 providers to find the best fit for your use caseInput / 1M
$0.60
Output / 1M
$3.00
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="kimi-k2-5",Model ID
kimi-k2-5Request 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="kimi-k2-5",
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 Kimi K2.5 Across Providers
| Provider | Input (per 1M) | Output (per 1M) |
|---|---|---|
| Cloudflare Workers AI | — | — |
| Fireworks AI | $0.60 | $3.00 |
| OpenRouter | $0.44 | $2.00 |
| Together AI | $0.50 | $2.80 |
| NVIDIA NIM | — | — |
Pricing
| Type | Price (per 1M) |
|---|---|
| Input tokens | $0.60 |
| Output tokens | $3.00 |
Capabilities
VisionMultimodalJSON / Tool useStructured Outputs
About Kimi K2.5
Kimi K2.5 is Moonshot AI's Kimi model focused on code generation and software engineering. It offers a 256K-token context window and scores 87.9 on GPQA.
Get Started
Model Specs
Released2026-03-15
Parameters1T (MoE, 384 experts)
Context256k
ArchitectureMixture of Experts