Kimi K3.1 Pro Highspeed arrives on Amazon Bedrock with tokens 2–3x faster

If you build coding agents, you know the main pain is not token price but waiting. The agent reads files, plans an edit, writes code, runs tests and fixes what broke, and every step waits for the previous one to finish. That makes generation speed matter more here than a nice number on a benchmark. The new release on Amazon Bedrock, Kimi K3.1 Pro Highspeed from Moonshot AI, is aimed squarely at this problem.

What the model is

Kimi K3.1 Pro Highspeed belongs to the new K3.1 series. It is a post-trained version of Kimi K3: the same open-weights architecture at about 3 trillion parameters and the same 1 million-token context window, but with noticeably more post-training. According to Moonshot AI, this extra training targets long agentic tasks and fewer factual errors. The company also promises gains in professional scenarios such as financial modeling and office documents, as well as in code for game engines, 3D scenes and CAD scripts.

The key part is the Highspeed suffix: according to Moonshot AI, the model produces output tokens 2–3 times faster than K3.1 Pro. This is the vendor's claim, and the article offers no independent measurements, so run your own workloads before you rebuild production around it.

The model supports tool calling, structured output, configurable reasoning, image input and streaming. On security, AWS says data is processed within AWS boundaries, is not shared with Moonshot AI and is not used for training. Zero retention is always on for inference requests, and AWS operators cannot see your prompts and responses.

How to connect: change almost nothing

The model is available on the bedrock-runtime endpoint through the OpenAI-compatible Chat Completions and Responses APIs, as well as through Converse and Invoke. AWS recommends Chat Completions. If your application already runs on the OpenAI SDK, you only need to point it at the Bedrock endpoint and change the model name. If you are already on Kimi K3 in Bedrock, you don't even need that: the endpoint, API and features are the same, and only the model ID changes.

Access goes through cross-region inference profiles:

• global.moonshotai.kimi-k3.1 — routes to any supported commercial region, costs less per token, and suits most workloads;
• us.moonshotai.kimi-k3.1 — processes only in US regions;
• in.moonshotai.kimi-k3.1 — processes only in Indian regions, for data residency requirements.

If you have no strict data residency requirements, use global.

One more detail: the model always reasons before it answers. The reasoning_effort parameter (reasoning.effort in the Responses API) sets the depth, with values low, high or max. If you don't set it, max is used. For simple requests that means extra tokens and latency, so an explicit low is the most direct way to speed up responses and shrink the bill.

Where to save: Flex and explicit caching

The Flex tier serves the model at half the Standard price, but response times may be longer. It suits model evaluation, summarization and background tasks inside agent chains. You choose the tier per request: latency-critical requests can go through Priority, regular traffic through Standard, and everything else through Flex. The quota is shared across tiers. AWS advises starting with a small share of traffic on Flex, comparing latency and cost, and checking the per-tier breakdown in CloudWatch.

The second lever is explicit prompt caching. Agents and assistants resend the same long prefix on every turn: system instructions, tool descriptions, repository rules. The model caches repeated prefixes automatically, while explicit breakpoints let you mark where the stable part ends and raise the cache hit rate. The rules are:

• it works in Chat Completions, Responses and Invoke, but not in Converse;
• a breakpoint must come after at least 256 tokens of stable content;
• the cache is stored for at least 30 minutes;
• cache writes are billed higher than regular input, while reads are cheaper and do not count toward the input tokens per minute quota.

Checking for a hit is simple: the cached_tokens field in usage should be greater than zero.

Practical takeaways

The article shows how to connect the model to OpenCode, an open-source terminal coding agent that is not tied to a single model. You need to configure an OpenAI-compatible provider based on Chat Completions and a Bedrock API key. Short-term keys last up to 12 hours, work only in the region where they were created, and are recommended for production; long-term keys are for experiments. In OpenCode, the low, high and max reasoning levels appear as model variants.

What to do in practice. Developers with agents: change the ID in a test environment, measure the duration of the full "edit, test, fix" cycle and compare it with regular K3.1 Pro. Product managers: split traffic across tiers so that background tasks go through Flex. Everyone: don't leave reasoning_effort at its default where low is enough.

The limitations are clear. The 2–3x speedup and the reduction in errors are Moonshot AI's claims, not independent verification. The article gives no prices, so you will have to check the Bedrock pricing page. Caching does not work through Converse, so switching to explicit caching means changing APIs.