How to use Kimi K3 without wasting the first prompt

Open the K3 Kimi homepage and scroll to the Kimi K3 chat. Chat requires an account and a paid plan or credit pack; the welcome credit alone cannot start a response.

Image analysis is available after sign-in because image requests cost more upstream and need an account balance. The interface shows the fixed credit cost before a signed-in request: a text response uses 100 credits and a response with one image uses 150. Check the pricing page for the current plan catalog and live checkout status.

Chat answers stream into the page and render Markdown, code blocks, tables, and lists. Use Stop if the request is heading in the wrong direction, Retry to regenerate the latest request, or New chat to clear the working context. Completed messages are saved in local browser storage, not synchronized as a cloud conversation archive.

Begin with the objective: the decision or artifact you need. Add relevant context: code, evidence, constraints, or audience. State what must not change. Finish with a success test or requested format. This structure gives the model something concrete to optimize instead of rewarding a broad, confident essay.

Avoid instructions such as “be smart,” “think infinitely,” or “guarantee the answer.” Kimi K3 already uses the provider’s maximum reasoning effort in this interface. More useful instructions ask it to state assumptions, distinguish facts from inference, and flag any missing evidence that could change the recommendation.

Do not restart every time the first answer is imperfect. Ask a focused follow-up: “Which claim is least supported?”, “What would falsify this diagnosis?”, or “Reduce the patch to only the files required.” K3 Kimi sends the recent eight conversation messages so follow-ups can refer to the current objective without repeatedly pasting everything. It sends the visible user and assistant text; it does not expose or replay hidden reasoning. Moonshot recommends preserved thinking history for long-horizon K3 agents, so use a verified compatible API harness such as Kimi Code when that requirement matters.

When the conversation becomes crowded, start a new chat and carry over a short, verified summary. This prevents an early mistaken assumption from becoming invisible background. For a large task, work in phases: map the problem, choose an approach, produce the artifact, then audit the result against the original constraints.

If a partial stream stops because of a network error, read what was received but do not assume it is the intended final answer. Retry the request. The server charges authenticated credits only after a complete upstream response.

Signed-in users can attach one PNG, JPEG, or WebP file up to 4 MB. Crop the image to the relevant region and keep text readable. A screenshot of the entire desktop may hide the small error message you actually need analyzed. Add a text question that names the job: inspect accessibility problems, read a chart trend, explain an architecture flow, or locate visual inconsistencies.

Ask the model to separate direct observations from interpretation. For example: “List only what is visibly present first, then suggest likely causes.” This reduces the chance that it invents invisible UI state or assumes a person’s identity. Never rely on image analysis to infer sensitive traits, authenticate a document, or make a medical diagnosis.

Kimi K3 in this product understands images; it does not generate, retouch, or export image files. If your goal is to create graphics, choose a purpose-built image model instead.

For code, ask the model to name affected interfaces, failure modes, and tests before drafting a patch. Then run the actual formatter, linter, type checker, unit tests, and a targeted end-to-end check. Verify package APIs against official documentation. Do not execute destructive shell commands or database changes merely because they appear in a polished code block.

For research, give source excerpts or links and ask the model to identify which source supports each claim. Check dates, authorship, methodology, and whether a statement is a quote or inference. A model can synthesize evidence well while still citing a page that does not say what it claims.

Published model benchmarks are useful selection signals, but they are not a substitute for testing your own tasks. See the Kimi K3 benchmark guide for an evaluation method based on representative examples.

Read the Kimi K3 features guide to understand reasoning, code, and vision capabilities. If your input is larger than a normal chat, the context-window guide explains the difference between the upstream one-million-token capacity and this site’s eight-message product limit.

Developers who need custom payloads, larger context, or their own retention policy should start from the official Kimi K3 quickstart. Everyone else can start with a narrow real task, inspect the result, and continue only if the evidence holds up.