Moonshot AI — #4 Global Intelligence Ranking · Launched Oct 23, 2026
20 free Kimi K3.1 Pro prompts for coding, reasoning, long-context analysis, video understanding, and agentic workflows — built for Moonshot AI's latest frontier model. Copy directly into kimi.ai or the Kimi API. No signup.
Upgrade from Kimi K3? See the K3 prompt guide →
The Kimi K3.1 Pro prompt generator on this page gives you 20 copy-paste prompts designed for Moonshot AI's newest flagship model, released October 23, 2026. Kimi K3.1 Pro ranks #4 globally on the Artificial Analysis Intelligence Index with a score of 55 — an 11-point jump over Kimi K3. It sits just 0.7 points behind Claude Opus 5.5 on Terminal-Bench 4.0 (65.7 vs 66.4), making it one of the closest non-Anthropic challengers to the top of the reasoning leaderboard.
Compared to Kimi K3, the K3.1 Pro adds video input (text + image + video in the same query), adjustable reasoning effort (low, high, max), native tool calling, and strict JSON Schema structured output — plus a 33% price cut to $2/$10 per million tokens. The prompts below cover coding, long-context document work, multimodal vision, agentic workflows, and reasoning tasks. Paste any prompt into kimi.ai or your Kimi API call and fill in the bracketed placeholders.
AA Intelligence Index score jumped from 44 (K3) to 55 (K3.1 Pro). #4 globally, within 1 point of Claude Opus 5.5.
Accepts text, images, and video clips in the same prompt — K3 was text + image only. Analyse video frames alongside long documents.
Choose reasoning effort: low (fast + cheap), high (default), or max (deep chain-of-thought). Match compute cost to task complexity.
Down from K3's $3/$15 to $2/$10 per million input/output tokens. Better intelligence at a lower price — the clearest upgrade signal.
Optimised for K3.1 Pro's 1M-token window, video input, adjustable reasoning, and tool calling. Works with kimi.ai and the Kimi API.
Review this entire codebase for production readiness. I am pasting the full repository below (up to 1M tokens). For each file, identify: 1. BUGS — logic errors, off-by-one, null-pointer risks, race conditions 2. SECURITY — injection risks, exposed secrets, insecure auth patterns 3. PERFORMANCE — N+1 queries, unbounded loops, memory leaks 4. DEAD CODE — unused exports, unreachable branches, stale comments For every finding: file path, line number, one-sentence description, and the minimal fix. End with a priority-ordered action list (Critical → High → Medium → Low). [Paste repository here]
Analyse the attached video clip and extract the following: 1. SCENE STRUCTURE — shot types used (wide, medium, close-up), camera movements, transitions 2. KEY MOMENTS — timestamp + one-sentence description of the 5 most important moments 3. PRODUCTION QUALITY — lighting, audio clarity, colour grading, pacing assessment 4. CONTENT SUMMARY — what is happening, who is involved, what is the intended message 5. IMPROVEMENT NOTES — 3 specific suggestions to improve the video for its intended platform Target platform: [YouTube / TikTok / Instagram Reels / LinkedIn] [Attach video clip]
Diagnose this asynchronous bug systematically. Work through every plausible cause before committing to one. Error: ``` [Paste error message, stack trace, or unexpected behaviour description] ``` Relevant code (include the async function and its callers): ``` [Paste the code] ``` Runtime: [Node.js / Deno / browser] — Version: [X.X] Concurrency pattern in use: [Promise.all / async iterators / event emitters / workers] Deliver: 1. Ranked cause list (most → least likely) with one-line reasoning 2. The fix — exact code change 3. How to reproduce the bug reliably for regression testing 4. A test case that would have caught this
Read the full document below and produce a structured summary for a senior executive. Document: [contract / annual report / legal filing / research paper / board memo] Reader's role: [CEO / CFO / General Counsel / CTO / Board Member] Decision to make: [describe the specific decision this document informs] Deliver: 1. ONE-PARAGRAPH VERDICT — the single most important thing the executive needs to know 2. KEY NUMBERS — every figure that affects the decision, in a table 3. RISKS — top 3 risks with likelihood and magnitude estimates 4. RECOMMENDED NEXT ACTION — one concrete step, with the owner and deadline 5. OPEN QUESTIONS — what the document doesn't answer [Paste document]
Execute this multi-step agentic task. You have access to the following tools: [list your available tools — web_search, code_executor, file_reader, API_caller, etc.] Task: [describe the end goal precisely] Constraints: [budget, time limit, approved data sources, off-limits actions] Success criteria: [exactly what the finished output must contain or achieve] For each step: - State your plan before acting - Execute using the appropriate tool - Verify the output before proceeding - If a step fails, explain why and attempt an alternative path End with a structured summary: what you accomplished, what you couldn't, and what manual follow-up is needed.
Review the following contract for material risks. I am a [buyer / seller / licensor / licensee / employer / employee]. Contract: [Paste full contract text] For each clause, flag: - RED: unacceptable — must renegotiate before signing - AMBER: risky — negotiate if possible, acceptable if not - GREEN: standard — no action needed For every RED and AMBER clause: - Clause number and heading - What it says in plain English - Why it is risky for my position - The alternative wording I should propose End with a one-paragraph overall risk verdict.
Extract the following structured data from the text below and return it as valid JSON conforming exactly to this schema: Schema: ```json [Paste your JSON Schema here] ``` Input text: [Paste the unstructured text, document, or data] Rules: - Every required field must be present; use null for missing optional fields - Arrays must contain at least one item if the field is required - Dates must be ISO 8601 format - Return ONLY the JSON object — no explanation, no markdown code block If any required field cannot be extracted reliably, add an "_extraction_notes" key listing the missing fields and why.
Synthesise the following 10 research papers into a structured literature review. Papers: [paste abstracts, or full PDFs if within context] Research question: [the specific question you need answered] Audience: [describe their background and what they will do with this review] Structure: 1. CONSENSUS — findings that appear in 5+ papers 2. EMERGING EVIDENCE — findings in 2–4 papers, not yet replicated 3. CONTRADICTIONS — where papers directly disagree and the likely reason 4. METHODOLOGICAL GAPS — weaknesses in the evidence base 5. PRACTICAL IMPLICATIONS — 3 actionable takeaways 6. OPEN QUESTIONS — the most important unanswered research question Target: 800–1,000 words.
Answer this question concisely. Use low reasoning effort — give me a direct answer, not a chain-of-thought. Question: [your factual or analytical question] Format: [one sentence / a bulleted list / a table / a number with unit]
This requires your maximum reasoning effort. Think step by step before answering. Problem: [describe the hard problem — a complex proof, a multi-variable optimisation, a strategic decision with many interdependencies] Constraints: [list all constraints the solution must satisfy] Context: [relevant background the model needs to know] Deliver: 1. Your explicit reasoning chain — every inferential step numbered and justified 2. The answer 3. The strongest argument against your answer 4. Your confidence level (0–100%) and what would change your mind
I am attaching both a reference image and a short video clip. Analyse them together. Task: [describe what you need — e.g. 'check whether the product in the image matches the one shown in the video', or 'describe how the lighting style in the image could be replicated in the video'] For each medium: - Key visual characteristics (colours, composition, mood, quality) - How it relates to the other medium Then answer the specific task. [Attach image] [Attach video]
Design a complete RESTful API and deliver the full OpenAPI 3.1 YAML specification. System: [describe what the API controls] Entities: [list them — users, orders, products, etc.] Auth: [bearer token / API key / OAuth2 PKCE] Special needs: [pagination, webhooks, idempotency keys, rate limiting] For each endpoint include: - Method and path - Summary and description - Request body with full schema and validation rules - Response schemas for 200 and all error codes - One curl example Also write a 200-word README explaining the auth flow.
Review the following financial model for errors, assumptions risks, and presentation gaps. Context: [what the model is for — fundraising pitch / board approval / M&A valuation / budget planning] Model: [paste as a table, CSV, or structured text] For each issue found: - Location (row/column) - Type (formula error / circular reference / unrealistic assumption / missing sensitivity / presentation gap) - Impact ($ or % effect on the headline number) - Fix End with: 1. A one-paragraph verdict on the model's reliability 2. The three assumptions most likely to cause the model to be wrong by 20%+
Write a competitive strategy brief for our entry into the following market. Our product: [one sentence] Target customer: [describe] Launch window: [timeframe] Competitors to analyse: [list 5–6 by name] For each competitor: - Primary value proposition (one sentence) - Estimated customer profile - One thing they do better than us - One weakness we can exploit End with: 1. The single positioning claim that is genuinely unclaimed 2. The first-mover window we need to hit to win it 3. The metric that will tell us, 6 months in, whether the strategy is working
Plan a tool call chain to accomplish the following task. You have access to: [list your tools]. Goal: [describe what needs to be accomplished] Starting state: [describe what data / resources are available now] End state: [describe exactly what the output must be] Deliver: 1. A numbered list of tool calls in execution order 2. For each call: tool name, inputs, expected output, and what happens if it fails 3. Where a human checkpoint is needed (decision points that require approval) 4. The total estimated latency if all steps succeed Do not execute — plan only.
Write clear, developer-friendly API documentation for the following endpoint. Endpoint: [METHOD /path] Purpose: [one sentence] Request parameters: [list each with type, required/optional, and constraints] Request body schema: [describe or paste JSON Schema] Response schema: [describe or paste JSON Schema] Error codes: [list each with description] Structure the docs with: - Overview (2 sentences) - Request Reference (table) - Response Reference (table) - Code examples in: curl / Python / JavaScript - Common mistakes section (3 items)
Analyse the attached screenshot and extract actionable insights. Screenshot type: [dashboard / UI mockup / error screen / analytics chart / spreadsheet / presentation slide] Context: [what tool or system this is from, and what decision I'm making] Deliver: 1. What the screenshot shows in plain English (2 sentences) 2. The single most important number, trend, or anomaly 3. What it implies I should do next 4. What I might be misreading without more context [Attach screenshot]
I am pasting our current codebase below. Create a detailed migration plan to move from [source stack] to [target stack]. Current code: [Paste full codebase or relevant sections — up to 1M tokens] Migration goals: - Zero downtime during migration - Preserve all existing behaviour - Estimated timeline: [X weeks] - Team size: [N engineers] Deliver: 1. A phased migration plan (Discovery → Parallel Run → Cutover → Cleanup) 2. The 5 highest-risk files and what specifically will break 3. The first two files to migrate as a proof-of-concept 4. Test strategy to validate each phase 5. Rollback plan if phase 3 fails
Give me the strongest possible argument for BOTH sides of the following question. Do not express your own view — just make the best case for each position. Question: [state the question or proposition] Context: [any background needed to make the arguments specific] For each side: 1. The core logical argument (deductive or empirical) 2. The strongest piece of evidence 3. The most common counterargument it must defeat 4. Who this position serves best End with: the crux — the single empirical or value disagreement that, if resolved, would settle the debate.
Write a video generation prompt for the following cinematic scene. Optimise for text-to-video AI models. Scene concept: [describe the scene in plain English] Tone: [cinematic epic / lo-fi intimate / documentary / commercial / anime] Camera: [static / tracking / drone / handheld / slow push] Lighting: [golden hour / neon night / overcast day / studio / candlelight] Audio: [silent / ambient / score / dialogue hint] Deliver: - A 2–3 sentence video prompt using precise visual language (lens type, f-stop feel, colour grade reference) - Two alternate version prompts: one shorter for faster models, one more detailed for highest quality - One negative prompt listing what to exclude
How K3.1 Pro compares on intelligence, context, multimodal input, and pricing.
| Model | AA Intelligence | Context | Video Input | Price (in/out per MTok) |
|---|---|---|---|---|
| Kimi K3.1 Pro ✓ | 55 (#4) | 1M tokens | Yes | $2 / $10 |
| Claude Opus 5.5 | ~57 (#2–3) | 1M tokens | No | $15 / $75 |
| Claude Sonnet 5.5 | ~52 (#5–6) | 1M tokens | No | $3 / $15 |
| GPT-6.1 Sol | ~56 (#2–3) | 256K tokens | No | $5 / $20 |
| Gemini 4 Argon | ~54 (#5) | 1M tokens | Yes | $7 / $28 |
| Kimi K3 | 44 | 1M tokens | No | $3 / $15 |
Kimi K3.1 Pro is Moonshot AI's flagship reasoning model, released October 23, 2026 — the direct successor to Kimi K3 (launched July 2026). It ranks #4 on the Artificial Analysis Intelligence Index with a score of 55, an 11-point improvement over K3. The model accepts text, image, and video input, has a 1 million token context window, and runs always-on reasoning with adjustable effort levels (low, high, max). Kimi K3.1 Pro scored 65.7 on Terminal-Bench 4.0, just behind Claude Opus 5.5 (66.4), making it the most capable Moonshot model to date.
Kimi K3.1 Pro makes four significant changes over K3. First, it adds video input — K3 accepted text and images only; K3.1 Pro can process video clips alongside text and images. Second, its intelligence score jumped 11 points on the Artificial Analysis Index (55 vs 44), reflecting improved reasoning. Third, it adds selectable reasoning effort levels (low, high, max) rather than always-on default reasoning, giving developers cost control. Fourth, pricing dropped from $3/$15 per million tokens to $2/$10 per million tokens — a 33% price cut. It also adds native tool calling and strict JSON Schema structured output, making it significantly more agentic than K3.
On Terminal-Bench 4.0, Kimi K3.1 Pro scored 65.7 versus Claude Opus 5.5 at 66.4 — a gap of less than 1 point. K3.1 Pro is priced at $2/$10 per million tokens, which is significantly cheaper than Claude Opus 5.5 ($15/$75) but close to Claude Sonnet 5.5 ($3/$15). GPT-6.1 Sol is the closest competitor on agentic tasks. For coding and long-context document work, Kimi K3.1 Pro is a strong alternative at a lower price point than the top-tier US models. For the absolute hardest multi-domain frontier tasks, Claude Opus 5.5 and GPT-6 Pro still lead.
Kimi K3.1 Pro has a 1 million token context window — matching Claude Fable 5, Claude Sonnet 5.5, and Gemini 4 Argon. This is large enough to hold approximately 750,000 words, a large production codebase with hundreds of files, or hundreds of research papers in a single query. Kimi K3.1 Pro also adds video input to this large context window, meaning you can pass video clips alongside long documents in a single prompt — a capability not yet available on most competing models.
Kimi K3.1 Pro supports three reasoning effort levels: low, high, and max. Low effort is fastest and cheapest — best for simple factual questions, formatting tasks, and extraction. High effort is the default — suitable for coding, analysis, and most professional tasks. Max effort is the slowest and most expensive — use it for complex mathematical proofs, multi-step strategic decisions, and the hardest reasoning chains. This matches the effort-level system in Claude Sonnet 5.5 (low/medium/high/xhigh/max) and GPT-6.1 Sol, giving developers fine-grained cost control.
Yes — all 20 prompts on this page are free to copy and paste. Kimi K3.1 Pro is available through Kimi.com (kimi.ai), Kimi Work, Kimi Code, and the Kimi API. The Kimi app offers a free tier for personal use. API pricing is $2 per million input tokens and $10 per million output tokens — approximately 33% cheaper than Kimi K3's $3/$15 pricing. A faster variant, Kimi K3.1 Pro Highspeed, is also available at higher throughput for latency-sensitive applications. Copy any prompt above, paste it into kimi.ai or your API call, fill in the bracketed placeholders, and send.
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