Long-context reading: hold whole manuals, contracts, and multi-file codebases in one conversation and answer questions that span them.

Visible reasoning: the thinking stream is returned as a separate channel from the answer, so the model's decomposition stays inspectable without polluting the reply.

Document and image input: attach files in the chat; text documents are read into context, images go in as multimodal blocks.

Structured output: summaries, extraction tables, comparison matrices, and citation-style pointers back into the supplied material.

kimi-k3 is the single model on this channel, billed per token at 741 credits per 1M input tokens and 3705 per 1M output tokens.

Deep thinking is a per-message switch: off asks for the light tier, on asks for the deep tier. Output tokens are the expensive side of the bill, so long reasoning runs cost materially more than short answers.

No web search: the vendor documents this series as not supporting search tools, so there is no search toggle and the field is not sent at all.

Thinking tiers are low / medium / high; a tier outside that list is rejected by the server before any billing happens.

Reasoning text is displayed but never merged into the answer, and it is not replayed as assistant output in later turns.

Document work: contract and policy review, technical documentation Q&A, literature triage, report summarization.

Codebase comprehension: read several files at once, explain an unfamiliar module, trace a bug across call sites.

Research: compare claims across sources, build an evidence table, surface contradictions.

Teaching: worked solutions with the reasoning shown, step-by-step concept explanations, exercise generation.

Attach the real document rather than a summary of it—the long context is the point of this channel.

Ask a scoped question per turn; broad "tell me everything" prompts burn output credits on structure you did not need.

Open the Reasoning panel when you need to audit an answer, and close it when you only want the result.

Turn deep thinking on for multi-hop questions and leave it off for lookups inside the supplied material.

Kimi on FuseAITools puts long-context reading and inspectable reasoning behind the same credits account as the rest of the toolbox. Upload the document, ask the question, and watch the reasoning arrive alongside the answer.