Last updated: 5 November 2026. Specs and benchmark scores are from Moonshot's Kimi K3.1 Pro guide and the Kimi K3.1 Pro tech blog.
What Kimi K3.1 Pro is
Kimi K3.1 Pro is the first model in Moonshot AI's K3.1 series. It is a post-trained version of Kimi K3, trained with much more reinforcement learning across coding, 3D creation, financial research, and other agent tasks. The architecture and the context window stay the same as K3; the gains come from post-training.
Key specs
- Context window: 1,048,576 tokens, shared by input and output.
- Reasoning: thinking is always on. Streaming responses carry the reasoning in
reasoning_content, separate from the final answer incontent. - Input: text and images. Images must be sent as base64 data URLs; public image URLs are not accepted by the model.
- Output length: up to 131,072 completion tokens per request in the playground.
- Tools: function calling with
tool_choice, and JSON Schema structured output withstrict: true. - Fixed sampling: temperature 1.0 and top_p 0.95 are fixed by Moonshot, so the playground does not show those sliders.
Where Kimi K3.1 Pro is strongest
Moonshot reports the scores below. They are the vendor's own numbers, not AIReiter measurements, so run your own prompts in the playground above before switching production traffic.
| Benchmark | What it tests | Kimi K3.1 Pro | Reference |
|---|---|---|---|
| Terminal-Bench 4.0 | Coding agents on real terminal tasks | 65.7% | Claude Opus 5.5 66.4%, GPT-6 Astra 58.2% |
| Kimi Code Bench 3.0 Long-horizon | Multi-step coding with tools (Moonshot internal) | 61.2% | Kimi K3 55.95% |
| BenchCAD, with tools | 3D reconstruction through CAD code | 92.7% | Claude Opus 5.5 96.2%, GPT-6 Astra 95.9% |
| OfficeQA Pro v2 | Fiscal data analysis across real financial documents | 64.4 | Reported as state of the art |
| Kimi Hallu Bench Hard, with tools | Claim-level hallucination rate, lower is better | 8.3% | Kimi K3 23.4% |
Good fits
- Long-horizon coding agents: repository-wide fixes and full project builds, including game work in Unity, Unreal Engine, and Godot.
- 3D and CAD scripting: writing and running Blender or CAD scripts to build parametric parts, furniture, and architectural models from a text brief or a reference image.
- Financial research and modeling: retrieval and multi-step numerical reasoning across filings, where the lower hallucination rate matters most.
Kimi K3.1 Pro vs Kimi K3
| Kimi K3.1 Pro | Kimi K3 | |
|---|---|---|
| Base | K3 with much larger post-training | K3 |
| Context window | 1,048,576 tokens | 1,048,576 tokens |
| Reasoning | Always on | Always on |
| Kimi Code Bench 3.0 Long-horizon | 61.2% | 55.95% |
| Kimi Hallu Bench Hard (with tools) | 8.3% | 23.4% |
| API model ID | kimi-k3.1-pro | kimi-k3 |
Both models take the same request shape, so moving from K3 to K3.1 Pro is a one-line change of the model ID. Keep K3 for workloads you have already tuned and evaluated; try K3.1 Pro first for new coding agents, 3D scripting, and finance tasks.
Calling Kimi K3.1 Pro through AIReiter
- Model ID:
kimi-k3.1-pro. - Endpoints: OpenAI-compatible
https://aireiter.com/api/v1/chat/completionsand Anthropic-compatiblehttps://aireiter.com/api/v1/messages. Existing OpenAI or Anthropic clients work after changing the base URL and the key. - Leave sampling at default: do not send temperature, top_p, n, presence_penalty, or frequency_penalty. Moonshot fixes them for this model.
- Multi-turn and tool loops: send back the complete assistant message from the previous turn, including its reasoning and tool calls, not only
content. - Images: put them in a
contentarray asimage_urlwith a base64 data URL. - Output tokens: reasoning is billed as output, so set
max_tokenshigh enough for the reasoning plus the answer.
Create a key on the API keys page. The request sample above this section already uses the right model ID.
Try Kimi K3.1 Pro in three steps
No install and no setup. The playground above calls the same model your code will call.
Paste a real task
Use the work you actually need done: a failing test with the code around it, a part you want modeled in Blender, or a set of figures to reconcile. Toy prompts do not show what a reasoning model adds.
Give it room to think
Reasoning is always on and counts toward max_tokens. Leave max_tokens at 8,192 or higher for multi-step work so the answer is not cut off after the reasoning.
Copy the API call
Move the same request into your code with model ID kimi-k3.1-pro on the OpenAI- or Anthropic-compatible endpoint. Token usage for every reply is shown under the response.
Kimi K3.1 Pro FAQ
Capability, reasoning, and integration questions.
/ 01What is Kimi K3.1 Pro?
Kimi K3.1 Pro is Moonshot AI's first K3.1 series model, a post-trained version of Kimi K3 with much more reinforcement learning on coding, 3D creation, and financial research. It keeps K3's 1,048,576-token context window, has thinking always on, and accepts text and image input.
/ 02How is Kimi K3.1 Pro different from Kimi K3?
K3.1 Pro uses the same base and context window as K3 but adds much larger post-training. Moonshot reports Kimi Code Bench 3.0 Long-horizon rising from 55.95% to 61.2%, and the claim-level hallucination rate on Kimi Hallu Bench Hard falling from 23.4% to 8.3%. Requests are the same apart from the model ID.
/ 03Can I turn off reasoning?
No. Kimi K3.1 Pro always runs with thinking enabled. The reasoning arrives in reasoning_content, separate from the answer in content, and it is billed as output tokens. For short questions the reasoning is usually brief, but leave enough max_tokens for multi-step tasks so the final answer is not truncated.
/ 04Why are temperature and top_p not adjustable?
Moonshot fixes temperature at 1.0 and top_p at 0.95 for Kimi K3.1 Pro, together with n, presence_penalty, and frequency_penalty. The playground hides those controls, and API requests should leave them out rather than set custom values.
/ 05Does Kimi K3.1 Pro support tool calling?
Yes. It supports function calling with tool_choice, including required on the first turn, and JSON Schema structured output with strict mode. In a tool loop, send back the full assistant message including tool_calls, then one tool message per call with the matching tool_call_id.
/ 06Can Kimi K3.1 Pro read images?
Yes, through the API. Send images inside a content array as image_url entries with a base64 data URL. The model does not fetch public image URLs, so download and encode the image first. Image upload is turned off in this playground for now, so test vision through the API.
/ 07How large is the context window?
The context window is 1,048,576 tokens, and it covers input and output together. With 900,000 tokens of input, under 150,000 tokens remain for reasoning and the answer. Repeated prefixes such as system prompts and codebases are cached automatically, which lowers the cost of long multi-turn sessions.