Kimi API Platforms: Moonshot AI Models Compared

Which Kimi models are available?

Moonshot’s current API documentation names kimi-k3.1-pro as a 3T-class model with a 1-million-token context window. That is the clearest documented option for analysing a large codebase or a substantial collection of documents in one request. A long context can reduce the need to split material into chunks, though you should still test whether your application retrieves and reasons over the relevant parts reliably.

The docs also list kimi-k3.1-pro-highspeed, described as having the same capabilities at roughly two to three times the speed. Treat that as Moonshot’s stated comparison, not a performance result you can assume for your workload. Benchmark both variants with your own prompts, expected output length and concurrency before selecting one for an interactive agent.

Moonshot’s start page says Kimi K3, Kimi K2.7 Code and Kimi K2.6 accept text, image and video input. It does not give model IDs or context lengths for those three there. Before wiring one into production, check the current model list and parameter reference for the exact ID, supported inputs and lifecycle status. Avoid copying an ID from an older tutorial without confirming it still works.

Context, reasoning and speed

For large-input analysis, kimi-k3.1-pro’s documented 1M-token context is the practical starting point. You might use it to review a repository, compare long contracts or ask questions across a set of technical documents. Context capacity is not a guarantee that every detail will be handled accurately: try representative inputs, measure answer quality and check how your request is billed before scaling usage.

The API lets you set reasoning effort through a top-level reasoning_effort field, with low, high and max as the documented choices. The docs give max as the default for kimi-k3.1-pro. For routine extraction or classification, test whether a lower setting meets your quality bar; reserve deeper reasoning for tasks where the extra work changes the result. Confirm field support for the precise model and endpoint you use.

Choose highspeed when latency matters more than maximising depth, such as a user-facing agent that needs to respond quickly. Run the same prompts against both versions and compare latency, output quality and failure rates. A vendor’s speed multiplier is useful guidance, but your network, request size and serving conditions affect the result.

Calling Moonshot directly

Moonshot exposes an OpenAI-compatible API at https://api.moonshot.ai/v1, including Chat Completions and Responses endpoints. The Responses API supports function tools, and tool calling is described as a general capability. If your application already uses an OpenAI-style client, this compatibility may let you adapt the base URL and model configuration rather than replace your request layer.

There is also an Anthropic-compatible Messages API endpoint. That can reduce integration work if your code already sends messages using the Anthropic protocol. Compatibility does not mean complete feature parity: test the specific request fields, streaming behaviour, tool calls, errors and response format your application depends on. Keep the current platform.kimi.ai documentation bookmarked; the older platform.moonshot.ai address redirects to it, so old tutorials may point to a retired domain.

Direct access is a straightforward fit when you want to configure Moonshot’s endpoint and model settings yourself. You manage your own credentials and integration, then handle any fallback logic in your application. Check the official model list before release and again when upgrading, especially if your deployment depends on a particular model ID.

Open-weight K2 models and self-hosting

The K2 line is a separate option from the hosted K3 API models. Secondary descriptions identify Kimi K2.6 as an open-weight model with about one trillion parameters, 32 billion active parameters, a 256K context and image and video input. The K2 family has also had earlier context limits: the original Kimi K2 was described with 128K, while the Instruct-0905 update doubled that to 256K. Check the specific model card rather than assuming every K2 release has the same capabilities.

Self-hosting can suit an internal tool when data must stay on your own servers, but open weights do not make deployment effortless. At this scale, hardware capacity and operating costs matter far more than downloading the files. Estimate memory, inference throughput and maintenance needs for your intended workload before choosing this route. Also read the exact model card and licence terms, including attribution requirements; do not treat “open weight” as a blanket statement that every use is unrestricted.

Check model availability before building around an older hosted K2 ID. Endpoints can be retired, and a model name found in a past example may no longer be supported. The official model list is the place to confirm current IDs and status.

What a multi-model platform adds

A multi-model platform can give you a single account or API layer for Kimi alongside other model families. That is useful if your product compares models, needs a second route when one endpoint is unavailable, or aims to keep provider-specific code out of several parts of an application. A platform may also offer routing or fallback controls, but check its actual feature set and whether the exact Kimi model is available before relying on either.

You trade some direct control for that shared layer. Your requests pass through an additional service, and billing, model naming, limits and supported API features can differ from Moonshot’s own endpoint. Do not assume that using a platform costs less: compare the applicable terms for your traffic and test any added latency or feature gaps. For production, verify how the platform handles errors and whether fallback preserves tools, streaming and the request format your app expects.

Our filtered comparison currently shows AI/ML API, CometAPI, Eden AI, Novita AI and WaveSpeedAI as platforms offering Kimi models. The available models and platform controls can vary, so confirm the exact Kimi ID before you choose a route. Filters such as free credits, routing and fallback, open source and GPU cloud describe ways to narrow the comparison; they are not guarantees that each listed platform provides those features.

How to choose from this filtered comparison

Start with the workload, then check the model and integration details. For a long document or repository, confirm access to kimi-k3.1-pro and test its 1M-token context with your own material. For a fast interactive agent, compare the highspeed variant against the standard model. For image or video input, verify the exact model ID and input handling rather than relying on a family name alone.

• Choose Moonshot direct if you want the official endpoint and are comfortable implementing credentials, model selection and fallback in your own code.
• Consider a multi-model platform if one integration or a platform-managed route to other models would simplify your application. Verify model availability and feature support first.
• Assess self-hosting if keeping inference on your infrastructure is a requirement and you can support the hardware, operations and licence checks.

Use our comparison as a way to narrow the platform options, not as a substitute for a model-level check. Across all 7 plans in our full AI comparison, prices start at $35.00; those figures cover the full category, not just this Kimi filter. The full comparison includes 7 providers, and our highest-scoring plan overall is AI/ML API from AI/ML API, scoring 5.1/10. Prices were last synced on 31 October 2026. Compare the relevant platform terms directly before committing to production traffic.