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AWS Bedrock@eu-west-2

kimi-k2.5

Kimi K2.5 is an open-source, native multimodal agentic model built through continual pretraining on approximately 15 trillion mixed visual and text tokens atop Kimi-K2-Base. It seamlessly integrates vision and language understanding with advanced agentic capabilities, instant and thinking modes, as well as conversational and agentic paradigms.

👁Vision🧠Reasoning🔧Tool calling⚡Caching
Try kimi-k2.5CompareDocs

Specifications

Context window128K tokens
Max output16K tokens
API typechat
AddedMar 31, 2026
Model IDbedrock/kimi-k2.5@eu-west-2
Data retentionNo
Used for trainingNo
Provider location🇺🇸 US / 🇪🇺 EU
Privacy policyAWS Privacy Notice→

Benchmarks

Benchmarks haven't been published yet for this exact variant.

Some variants (region-specific deployments, highspeed tiers) share benchmarks with their base model — check the base model page or the AWS Bedrock models overview.

Pricing

Input / 1M
$0.72
Output / 1M
$3.60
Cache write / 1M
$0.72
Cache read / 1M
$0.72
Estimated cost
100K input + 10K output$0.11
1M input + 100K output$1.08
10M input + 1M output$10.80

Requesty charges exactly what the upstream provider charges — no markup, no per-request fees. Prompt caching and smart routing can reduce effective cost by 30-80%.

Quickstart

Drop-in compatible with the OpenAI SDK. Change the base URL, swap in your Requesty API key, and set the model to bedrock/kimi-k2.5@eu-west-2.

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from openai import OpenAI

client = OpenAI(
    api_key="YOUR_REQUESTY_API_KEY",
    base_url="https://router.requesty.ai/v1",
)

response = client.chat.completions.create(
    model="bedrock/kimi-k2.5@eu-west-2",
    messages=[
        {"role": "user", "content": "Explain quantum computing in one paragraph."},
    ],
)

print(response.choices[0].message.content)

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claude-opus-4-7
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claude-opus-4-7
@eu-central-1
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kimi-k2.5
@us-west-2
128K ctx$0.60/1M

Frequently asked questions

How much does kimi-k2.5 cost?
kimi-k2.5 is priced at $0.72 per million input tokens and $3.60 per million output tokens when accessed via Requesty. Prompt caching is supported, which can cut effective input cost by up to 90% on repeated context. Requesty charges exactly what the upstream provider charges — we don't add markup.
What is the context window of kimi-k2.5?
kimi-k2.5 has a context window of 128K tokens, with a maximum output of 16K tokens per response. That's roughly 171 words of input you can fit in a single prompt.
What can kimi-k2.5 do?
kimi-k2.5 supports vision input, tool calling, extended reasoning, prompt caching. You can call it through any OpenAI-compatible client by pointing base_url to Requesty.
How do I use kimi-k2.5 with the OpenAI SDK?
Install the OpenAI SDK, set base_url to "https://router.requesty.ai/v1", set your API key to your Requesty key, and set the model to "bedrock/kimi-k2.5@eu-west-2". The Quickstart above shows Python, JavaScript and cURL snippets.
What region is this deployment?
This variant of kimi-k2.5 is deployed in eu-west-2. Region-specific endpoints matter for data residency, latency to your users, and compliance requirements (GDPR, HIPAA). Other regions for the same model may be listed on the AWS Bedrock provider page.

Access kimi-k2.5 through Requesty

One API key, 400+ models, OpenAI-compatible. No markup on provider prices, automatic failover, and smart caching built-in.

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