# Kimi K3.1 Pro Highspeed on Amazon Bedrock: Model IDs, Caching, Flex Tier, and OpenCode

> Source: <https://explainx.ai/blog/kimi-k3-1-pro-amazon-bedrock-highspeed-opencode-guide-2026?utm_source=rss&utm_medium=feed&utm_campaign=blog>
> Published: 2026-11-11 00:00:00+00:00

**Amazon Web Services put [Kimi K3.1 Pro Highspeed](https://aws.amazon.com/blogs/machine-learning/introducing-kimi-k3-1-pro-on-amazon-bedrock/?ref=explainx) from Moonshot AI on Amazon Bedrock on November 10, 2026.** You reach it through cross-Region inference profiles, the same OpenAI-compatible Chat Completions path AWS already documents for Kimi K3, plus Converse, Responses, explicit prompt caching, a half-price Flex tier, and a first-party OpenCode wiring guide.

For teams that already run agents on Bedrock, the practical change is a model ID swap and a chance to test Moonshot’s post-trained K3.1 line without sending traffic to Moonshot’s own API. For teams that evaluated Kimi only on the [Kimi API](https://explainx.ai/blog/kimi-k3-1-pro-open-weights-benchmarks-price-terminal-bench-nov-4-2026), Bedrock is the procurement path: IAM, regional profiles, and AWS’s statement that inference data stays inside the AWS boundary and is not used to train the model.

## TL;DR

| Item | Detail | 
|---|---|
| Announced | November 10, 2026 ( [AWS ML blog](https://aws.amazon.com/blogs/machine-learning/introducing-kimi-k3-1-pro-on-amazon-bedrock/?ref=explainx) ) | 
| Variant on Bedrock | Kimi K3.1 Pro **Highspeed** (faster output token rate) | 
| Recommended model ID | `global.moonshotai.kimi-k3.1` | 
| Residency profiles | `us.moonshotai.kimi-k3.1` ,`in.moonshotai.kimi-k3.1` | 
| Context | 1M tokens (same class as K3 per AWS/Moonshot) | 
| APIs | OpenAI Chat Completions (recommended), Responses, Bedrock Converse and Invoke | 
| Reasoning | Always on; `reasoning_effort` or`reasoning.effort` :`low` ,`high` ,`max` (default`max` ) | 
| Cost levers | Flex tier (half Standard price), explicit prompt caching | 
| Agent IDE | Official OpenCode `opencode.json` example in the AWS post | 

## What Kimi K3.1 Pro Highspeed is

Moonshot released **Kimi K3.1 Pro** in October 2026 as the first model in its K3.1 series. explainx.ai covered the launch benchmarks, API price, and the November 4 open-weight drop in [the Kimi K3.1 Pro overview](https://explainx.ai/blog/kimi-k3-1-pro-open-weights-benchmarks-price-terminal-bench-nov-4-2026) and the earlier [Kimi K3 open-weights explainer](https://explainx.ai/blog/kimi-k3-open-weights-2-8-trillion-parameters-july-2026).

AWS’s November post positions **Highspeed** as the Bedrock serving SKU: same endpoint family as Kimi K3 on Bedrock, but Moonshot reports **two to three times** the output speed of Kimi K3.1 Pro on Moonshot’s side. That matters for coding agents, which chain many short generations. Each tool-call loop waits on output tokens; a faster stream shortens edit-test-fix cycles.

Moonshot’s own blog claims gains in long-horizon agentic work, fewer factual errors, financial modeling, office documents, and coding for game engines, 3D scenes, and CAD scripts. Those are vendor statements. Independent scores such as the [Artificial Analysis line in our launch post](https://explainx.ai/blog/kimi-k3-1-pro-open-weights-benchmarks-price-terminal-bench-nov-4-2026) are useful, but Bedrock adoption is really a hosting and governance decision.

## Why Bedrock instead of the Kimi API

Three reasons enterprises cite when a frontier open-weight model lands on a hyperscaler:

1. **Contract and IAM.** The model rides an existing AWS agreement. Security reviews that already approved Bedrock do not restart for Moonshot’s corporate entity.
2. **Data boundary language.** AWS states that your data is processed within the AWS data boundary, is**not shared with Moonshot AI** , and is**not used to train** the model. It also lists zero data retention for inference and zero operator access during inference. Read the[Bedrock service terms](https://aws.amazon.com/bedrock/security-compliance/?ref=explainx) for your account type; this post does not restate legal guarantees.
3. **Model mixing.** Bedrock already hosts other coding-oriented models. Our[GLM 5.3 on Bedrock guide](https://explainx.ai/blog/glm-5-3-amazon-bedrock-enterprise-coding-model-ids-2026) shows the same OpenAI-client pattern. You can A/B Kimi K3.1 against GLM or Claude family models behind one billing relationship.

Moonshot still operates the [Kimi API at roughly $2/$10 per million tokens](https://explainx.ai/blog/kimi-k3-1-pro-open-weights-benchmarks-price-terminal-bench-nov-4-2026) for direct access and open-weight self-hosting. Bedrock pricing is on [Amazon Bedrock pricing](https://aws.amazon.com/bedrock/pricing/?ref=explainx) and may differ by tier and cache hits. Compare **total cost per successful agent task**, not list price alone.

For agent security context on Kimi-class models, see [Vals AI’s cyber-offense bench](https://explainx.ai/blog/vals-ai-cyberoffense-gpt-6-sol-daybreak-deepseek-v4-1-flash-open-weight-cyber-range-2026), which scored Kimi K3.1 Pro in a controlled range, and Moonshot’s own cyber ablation in the launch post.

## Model IDs and regions

AWS exposes Kimi K3.1 Pro Highspeed only through **cross-Region inference profiles**:

| Profile ID | Routing | When to use | 
|---|---|---|
| `global.moonshotai.kimi-k3.1` | Any supported commercial AWS Region | Default; **lower per-token cost** per AWS | 
| `us.moonshotai.kimi-k3.1` | US Regions only | US data residency | 
| `in.moonshotai.kimi-k3.1` | India Regions only | India residency | 

If you already call Kimi K3 on Bedrock, AWS says you can **change the model ID** in existing code. Regional availability and card details are on AWS’s model documentation linked from the announcement.

## Calling Kimi with the OpenAI SDK

AWS recommends the **OpenAI-compatible Chat Completions** endpoint on `bedrock-runtime`. You authenticate with a short-lived bearer token from AWS credentials via `aws-bedrock-token-generator`, not a long-lived OpenAI key.

``` python
from openai import OpenAI
from aws_bedrock_token_generator import provide_token

REGION = "us-west-2"
MODEL_ID = "global.moonshotai.kimi-k3.1"

client = OpenAI(
    base_url=f"https://bedrock-runtime.{REGION}.amazonaws.com/openai/v1",
    api_key=provide_token(region=REGION),
)

completion = client.chat.completions.create(
    model=MODEL_ID,
    messages=[
        {"role": "system", "content": "You are a concise senior engineer."},
        {"role": "user", "content": "When should I prefer a queue over a stream?"},
    ],
    reasoning_effort="high",
)
print(completion.choices[0].message.content)
```

**Reasoning effort:** Kimi K3.1 Pro Highspeed always reasons before answering. `reasoning_effort` accepts `low`, `high`, or `max`. If you omit it, the model uses **` max`**, which increases latency and output tokens. For routing inside an agent, use `low` on classification steps and `high` or `max` on codegen.

The **Responses API** uses the same client with `reasoning={"effort": "low"}` instead. The **Converse API** uses boto3 with `modelId="global.moonshotai.kimi-k3.1"` and may return a reasoning block before text; pick the text block from the content array as AWS’s sample shows.

Prerequisites from AWS: Python 3.11+, `openai`, `aws-bedrock-token-generator`, `boto3`, and IAM permission for `bedrock:InvokeModel` and streaming, plus `bedrock:CallWithBearerToken` when using bearer tokens.

## Flex tier: half price, slower tail

Bedrock’s **Flex** tier serves Kimi at **half the Standard on-demand price** with potentially longer response times. AWS suggests batch evals, summarization, and background agent steps.

On Chat Completions, pass `service_tier="flex"`. On Converse, set `serviceTier` to `{"type": "flex"}`. Standard, Priority, and Flex draw from the **same on-demand quota**. AWS recommends starting with a small Flex share and watching CloudWatch dimensions `ServiceTier` and `ResolvedServiceTier` before shifting traffic.

This mirrors the tier story on [GLM 5.3 Bedrock](https://explainx.ai/blog/glm-5-3-amazon-bedrock-enterprise-coding-model-ids-2026) and the broader [Bedrock tokenomics patterns](https://explainx.ai/blog/amazon-bedrock-tokenomics-data-lake-finops-2026) we covered for finops teams.

## Explicit prompt caching for agents

Agents resend large stable prefixes: system prompts, tool schemas, repo guidelines, and reference docs. Kimi K3.1 Pro Highspeed supports **automatic** prefix caching and **explicit** breakpoints on Chat Completions, Responses, and Invoke.

Rules from AWS:

- Explicit caching **does not** work on**Converse** .
- A `prompt_cache_breakpoint` must follow**at least 256 tokens** of stable content.
- Cached content is retained **at least 30 minutes** .
- Cache **writes** bill higher than standard input;**reads** bill at a discount and**do not** count toward input TPM quota.

The AWS post shows a Responses example that marks breakpoints on a long `repo_guidelines.md` system prompt and on the user turn, then prints `cached_tokens` from `usage.input_tokens_details`. That pattern is the same class of savings we described for [prompt caching on Bedrock with GPT-6 Astra patterns](https://explainx.ai/blog/aws-bedrock-prompt-caching-openai-gpt-6-astra-agent-patterns-62-percent-input-cost-2026).

For multi-turn tool use on the open weights, Moonshot’s model card notes you must pass back the full assistant message including `reasoning_content`. Bedrock callers should confirm the same requirement in their harness when migrating from the Kimi API.

## OpenCode on Bedrock

AWS documents a terminal coding agent path with **OpenCode** and the Bedrock Chat Completions provider. You point `baseURL` at `https://bedrock-runtime.us-west-2.amazonaws.com/openai/v1` (swap Region), register `global.moonshotai.kimi-k3.1` with reasoning capabilities, and set it as the default model. Short-lived **Bedrock API keys** from the console are recommended for production; long-term keys are for exploration.

## OpenCode connect and reasoning variants

OpenCode connect dialog for Kimi K3.1 Pro Highspeed on Amazon Bedrock Chat Completions
*Image: [Amazon Web Services](https://aws.amazon.com/blogs/machine-learning/introducing-kimi-k3-1-pro-on-amazon-bedrock/?ref=explainx)*

After `/connect` and key paste, OpenCode stores credentials once per key. Use `/models` to switch between Kimi and other providers mid-session.

Reasoning effort variants for Kimi K3.1 Pro Highspeed in OpenCode on Bedrock
*Image: [Amazon Web Services](https://aws.amazon.com/blogs/machine-learning/introducing-kimi-k3-1-pro-on-amazon-bedrock/?ref=explainx)*

The `capabilities` block lists supported reasoning efforts so OpenCode exposes **low**, **high**, and **max** variants. That UI matters when you want a fast refactor pass on `low` and a careful migration on `max` without editing config between tasks.

OpenCode is one of several harnesses in our [agent harness comparisons](https://explainx.ai/blog/how-to-run-glm-5-2-coding-plan-agent-harnesses-2026). Bedrock is not the only backend: Moonshot’s own Kimi Code product and third-party hosts remain options.

## Comparison snapshot (where to look elsewhere)

| Need | Consider | 
|---|---|
| Lowest friction if you already pay Moonshot | Kimi API direct ( [pricing page](https://platform.kimi.ai/docs/pricing/chat?ref=explainx) ) | 
| AWS-only procurement and residency profiles | **Bedrock Kimi K3.1 Highspeed** (this announcement) | 
| Self-host with open weights | [Hugging Face moonshotai/Kimi-K3.1-Pro](https://explainx.ai/blog/kimi-k3-1-pro-open-weights-benchmarks-price-terminal-bench-nov-4-2026) plus vLLM/SGLang | 
| Another open-weight coder on the same bill | [GLM 5.3 on Bedrock](https://explainx.ai/blog/glm-5-3-amazon-bedrock-enterprise-coding-model-ids-2026) | 
| Cyber eval context | [Vals cyber-offense bench](https://explainx.ai/blog/vals-ai-cyberoffense-gpt-6-sol-daybreak-deepseek-v4-1-flash-open-weight-cyber-range-2026) | 

AWS does not publish a side-by-side Bedrock bench table in this post. Run your own tasks on your repositories before switching production traffic.

## What to verify in your account

1. **Entitlement.** New Bedrock models sometimes roll out in waves. Confirm Kimi K3.1 appears in the console Playground and in`ListFoundationModels` for your Region.
2. **Reasoning defaults.** Leaving effort at default`max` can balloon cost on simple steps. Set effort per call type in your router.
3. **Cache economics.** Measure cache hit rate before assuming savings; cache writes cost more upfront.
4. **License if you later self-host.** Moonshot’s open license includes revenue gates for large Model-as-a-Service operators, separate from Bedrock terms ([details in our weights update](https://explainx.ai/blog/kimi-k3-1-pro-open-weights-benchmarks-price-terminal-bench-nov-4-2026) ).
5. **Speed claims.** The 2–3× speed statement is Moonshot’s comparison to Kimi K3.1 Pro on Moonshot infrastructure, not a guarantee against other Bedrock models.

## Bedrock console quick start

If you are not ready to wire Python yet, AWS’s path is: open the **Amazon Bedrock** console, choose **Test**, then **Playground**, and select **Kimi K3.1** from the model picker. That is the fastest way to sanity-check entitlement and reasoning defaults before you paste IAM policies into CI. When the playground works, copy the same model ID into your agent router and enable streaming so tool-calling harnesses see tokens as they arrive. Teams that standardize on [Amazon Bedrock AgentCore evaluation patterns](https://explainx.ai/blog/amazon-bedrock-agentcore-evaluation-playbook-deepeval-autoeval-guide-2026) can add Kimi as another model arm in the same eval harness once the ID is approved.