Chat With Kimi K3 on Lorka AI

Take coding and research to another level. Try Kimi K3 on Lorka and quickly analyze large codebases, coordinate technical workflows, interpret visual inputs, and produce structured results.

Kimi
Ask anything...
Reasoning
💡💡💡💡💡
Frontier
Speed
Medium
Context Handling
🧠🧠🧠🧠🧠
Strong
Input
Text, Image, Video
Output
Text

Developed by Moonshot AI, Kimi K3 is a state-of-the-art multimodal large language model designed for advanced reasoning, coding, agentic workflows, and high-quality generation of HTML/React user interfaces, documents, spreadsheets, and other structured content.

Context window1M tokens
Max output tokens32K tokens
Knowledge cutoffJul 2025

Take Complex Technical Work Further With Kimi K3

Access Kimi K3 on Lorka and combine frontier-scale architecture with practical capabilities for sustained engineering, research, and multimodal work.

Long-horizon coding

Analyze large repositories, coordinate terminal tools, and carry complex engineering tasks forward with limited supervision.

Million-token context

Process up to 1,048,576 tokens with Kimi K3’s context window for project-scale prompts and long-running workflows.

Native visual understanding

Work with text, images, and video to connect screenshots, diagrams, visual feedback, and implementation decisions.

Structured outputs and tool use

Produce JSON-compatible responses and support specialized tool-calling workflows across technical environments.

Always-on deep reasoning

Use K3’s Max Thinking Effort approach for tasks that require extended analysis, planning, and multi-step execution.

Kimi K3 Benchmarks : Handle Production-Scale Coding, Visual, and Data Workflows

These examples show how the Kimi K3 AI model can turn demanding prompts into detailed, implementation-ready outputs.

User

Read this repository and identify the architecture, technical debt hotspots, and the safest refactor order.

Kimi
Kimi K3Codebase Analysis and Refactor Planning

Architecture summary API routes → application services → billing, identity, and reporting domains → shared database and event infrastructure Priority issues 1. Cross-domain coupling: Reporting imports billing repositories directly, making schema changes risky. 2. Unbounded exports: Large reports are loaded fully into memory instead of streamed. 3. Unsafe retries: Failed background jobs can generate duplicate exports. 4. Hidden side effects: Shared utilities initialize telemetry during import, complicating tests. Recommended refactor sequence - Add characterization and integration tests. - Introduce interfaces between reporting and billing. - Replace full-memory exports with streaming. - Add idempotency keys to background jobs. - Remove route-layer dependencies from workers. - Extract services only after internal boundaries are stable. export interface UsageReader { getSummary( organizationId: string, period: DateRange ): Promise<UsageSummary>; } Success criteria - No direct imports across domain internals. - Export memory remains below 300 MB. - Retried jobs produce no duplicate files. - Existing API contracts remain unchanged.

User

Look at this frontend screenshot and propose the HTML, CSS, and React changes needed to match the target design.

Kimi
Kimi K3Visual Frontend Reconstruction

Visual findings - Increase the headline width and tighten line height. - Apply a warm gradient only to the product name. - Use a large rounded specification panel with thin dividers. - Expand the label column and lighten the body copy. - Add numbered circular markers and an active left border.

HTML, CSS, and React
1<article className={`spec-row ${active ? "spec-row--active" : ""}`}>
2 <div className="spec-row__heading">
3 <span className="spec-row__number">01</span>
4 <span className="spec-row__label">MODEL TYPE</span>
5 </div>
6
7 <ul className="spec-row__details">
8 {details.map((detail) => <li key={detail}>{detail}</li>)}
9 </ul>
10</article>
11
12.spec-row {
13 display: grid;
14 grid-template-columns: 320px 1fr;
15 padding: 34px 38px;
16 border-bottom: 1px solid #ddd;
17}
18
19.spec-row--active {
20 border-left: 4px solid #111;
21}
22
23.spec-row__details {
24 color: #767676;
25 line-height: 1.5;
26}

Validation Test the implementation at desktop, tablet, and mobile widths, then compare typography, spacing, border radius, and row alignment against the source image.

User

Read these reports and return a strict JSON summary of risks, opportunities, and recommended next steps.

Kimi
Kimi K3Schema-Constrained Report Extraction
JSON
1{
2 "risks": [
3 {
4 "id": "R-001",
5 "title": "Single-region database dependency",
6 "severity": "critical",
7 "evidence": [
8 "Regional failover exceeded the recovery objective in three tests."
9 ],
10 "recommended_action": "Deploy and test a warm secondary region.",
11 "owner": "VP Infrastructure"
12 },
13 {
14 "id": "R-002",
15 "title": "Cloud costs growing faster than revenue",
16 "severity": "high",
17 "evidence": [
18 "Infrastructure spending increased 31% while revenue increased 18%."
19 ],
20 "recommended_action": "Introduce workload-level cost attribution.",
21 "owner": "FinOps Director"
22 }
23 ],
24
25 "opportunities": [
26 {
27 "title": "Move batch analytics to lower-cost compute",
28 "estimated_annual_value_usd": 840000
29 }
30 ],
31
32 "next_steps": [
33 {
34 "priority": 1,
35 "action": "Approve the secondary-region program.",
36 "deadline": "2026-07-25"
37 },
38 {
39 "priority": 2,
40 "action": "Launch cloud cost attribution.",
41 "deadline": "2026-08-01"
42 }
43 ]
44}
45
46Validation rule
47
48const RiskSchema = z.object({
49 id: z.string(),
50 title: z.string(),
51 severity: z.enum(["low", "medium", "high", "critical"]),
52 evidence: z.array(z.string()).min(1),
53 recommended_action: z.string(),
54 owner: z.string()
55});
User

Plan the task, call the right tools, and return a final answer only after all tool results are processed.

Kimi
Kimi K3Tool-Orchestrated Research Assistant

Plan 1. Review architecture and workload documents. 2. Retrieve current contract terms. 3. Query 12 months of platform costs. 4. Estimate migration effort. 5. Compare three-year total cost of ownership. Processed tool results Current annual platform cost: $2.95M Estimated migration cost: $1.86M Parallel-run cost: $0.98M Target annual platform cost: $2.11M Estimated break-even point: Month 46 Final recommendation Do not approve a full migration within the current three-year planning horizon. The lower steady-state platform cost does not offset migration, parallel operation, and contract expenses soon enough. Proceed with a smaller pilot: - Test five representative workloads. - Renegotiate the current contract. - Improve workload portability. - Recalculate the business case using measured migration effort. - Reconsider migration if the break-even point falls below 36 months. The supplied briefing supports these workflow categories through its examples of repository-scale engineering, screenshot-guided development, structured outputs, specialized tool use, and long-running research processes.

Why Access Kimi K3 on Lorka AI

Use the Kimi K3 chat within an all-in-one workspace built for practical AI model access and comparison.

Chat without a custom integration

Access Kimi K3 through a familiar conversational interface.

Work across large inputs

Upload substantial code, documents, screenshots, and research materials in one organized workspace.

Compare frontier models

Test the same prompt across Kimi K3 and other leading AI models, such as GPT-5.6, Claude Sonnet 5, and more.

Keep complex projects organized

Maintain prompts, outputs, and supporting files within persistent workspaces.

Move from analysis to execution

Use detailed outputs for engineering, research, product, and operational decisions.

Kimi K3 Capabilities: Context Window, Pricing, Inputs, and More

01

Model Type

  • 2.8-trillion-parameter released-weights model
  • Positioned as a frontier-scale generalist for coding, research, and autonomous workflows
  • Designed to move beyond reactive chat toward long-horizon agentic execution
02

Context Window

  • Supports a 1,048,576-token context window
  • Built for extensive repositories, long documents, and multi-stage project context
  • KDA and AttnRes are intended to preserve information quality across long sequences
03

Modalities

  • Accepts text, image, and video inputs
  • Produces high-fidelity text and JSON-compatible structured responses
  • Supports visual engineering, diagram interpretation, and multimodal research workflows
04

Architecture

  • Uses Kimi Delta Attention and Attention Residuals
  • Built on a sparse Stable LatentMoE framework
  • Activates 16 of 896 experts for each token
  • The briefing reports a 2.5× scaling-efficiency improvement over Kimi K2
05

Agentic Reasoning and Tool Use

  • Designed for terminal coordination and long-running technical tasks
  • Supports specialized tool calling and multi-agent workflows
  • Operates with Max Thinking Effort by default according to the source material
  • Built to sustain recursive research and engineering processes with limited oversight
06

Access, Release, and Pricing

  • The briefing lists Kimi Work, Kimi Code, and the Kimi API as access points
  • It states that full model weights are scheduled for July 27, 2026
  • Exact Kimi K3 pricing is not provided in the supplied briefing
  • Future lower-effort and balanced-effort API modes are described, but no release dates or rates are specified

Try Kimi K3 to Simplify Engineering, Research, and Product Work

Repository modernization for software engineers

Understand large codebases and organize complex refactors into safer implementation phases.

KimiTry this prompt
"

Audit this repository, map its dependencies, identify technical debt, and propose a staged modernization plan with tests and rollback steps.

"

Visual implementation for frontend developers

Translate screenshots and visual references into structured React, CSS, and component-level implementation guidance.

KimiTry this prompt
"

Analyze this interface screenshot and produce an accessible React and Tailwind implementation that closely matches its layout, typography, and spacing.

"

Structured risk analysis for technical analysts

Convert extensive reports into schema-constrained findings that can move directly into databases or operational workflows.

KimiTry this prompt
"

Review these reports and return valid JSON containing prioritized risks, supporting evidence, responsible owners, deadlines, and recommended mitigation actions.

"

Tool orchestration for AI builders and agent developers

Plan multi-step tasks, select appropriate tools, process intermediate results, and produce a verified final response.

KimiTry this prompt
"

Plan this task, call only the necessary tools, validate every result, and return the final recommendation after resolving conflicting evidence.

"

Decision briefs for product managers

Turn meeting notes, specifications, research, and stakeholder feedback into priorities and clear product decisions.

KimiTry this prompt
"

Convert these product notes into a decision brief with user problems, priorities, dependencies, unresolved questions, and recommended next-quarter actions.

"

Multidocument synthesis for researchers

Review large collections of papers and technical materials while tracking agreements, conflicts, methods, and evidence quality.

KimiTry this prompt
"

Synthesize these papers into one evidence map, highlighting shared findings, methodological conflicts, unresolved questions, and practical follow-up research directions.

"

Visual research analysis for knowledge workers

Combine documents, screenshots, charts, and video material into one coherent overview with traceable conclusions.

KimiTry this prompt
"

Analyze this document set, presentation, chart collection, and recorded briefing, then produce a sourced executive overview with key decisions.

"

Compiler and performance work for systems engineers

Investigate kernels, intermediate representations, profiling results, and low-level optimization opportunities across complex technical stacks.

KimiTry this prompt
"

Profile this GPU kernel, explain the bottlenecks, propose optimized code, and provide correctness tests plus before-and-after benchmark commands.

"

Kimi K3 vs. Fable 5 and Other Top AI Models on Lorka

See how the Kimi K3 model compares to other large language models, such as Grok 4.5, Opus 4.8, and more found on Lorka’s all-in-one AI platform.

Legend:
💡Reasoning
Speed
🤖Multimodality
🧠Context
(1: Poor – 5: Very good)
Kimi

Kimi K3

Reasoning
💡💡💡💡💡
Speed
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Repository-scale engineering, visual coding, structured research, and extended autonomous workflows

OpenAI

GPT-5.6 Sol

Reasoning
💡💡💡💡💡
Speed
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Sustained deep-focus reasoning, strict criteria adherence, sophisticated platform orchestration, and governing autonomous virtual entities.

OpenAI

GPT-5.6 Terra

Reasoning
💡💡💡💡💡
Speed
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Balanced professional tasks, enterprise processes, dependable automation, and economical high-volume work

OpenAI

GPT-5.6 Luna

Reasoning
💡💡💡💡💡
Speed
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Rapid everyday operations, lightweight assistants, routine execution, and throughput-focused production workloads

Claude

Claude Fable 5

Reasoning
💡💡💡💡💡
Speed
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

High-depth reasoning, sustained software projects, difficult research, and nuanced analytical assignments

Claude

Claude Opus 4.8

Reasoning
💡💡💡💡💡
Speed
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Autonomous engineering, demanding problem-solving, enterprise analysis, and high-stakes professional workflows

Claude

Claude Sonnet 5

Reasoning
💡💡💡💡💡
Speed
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Efficient agentic development, project planning, code review, and broad professional knowledge work

Z.ai

GLM-5.2

Reasoning
💡💡💡💡💡
Speed
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Open-weight long-context coding, project-wide refactoring, structured automation, and sustained engineering execution

Grok

Grok 4.5

Reasoning
💡💡💡💡💡
Speed
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Fast agentic coding, technical problem-solving, office documents, and tool-supported engineering tasks

Gemini

Gemini 3.6 Flash

Reasoning
💡💡💡💡💡
Speed
Multimodality
🤖🤖🤖🤖🤖
Context
🧠🧠🧠🧠🧠
Ideal Use Cases

Low-latency multimodal work, coding assistance, document review, and scalable knowledge-processing workflows

Strengths and Limitations of Kimi K3 and Other LLMs

Kimi

Kimi K3

Strengths

2.8T model with 1M+ context, native vision, and strong coding, tool, and JSON support.

Limitations

Some features are still evolving, and the ecosystem is less mature than OpenAI or Anthropic.

Kimi

Kimi K2.6

Strengths

Open-weight multimodal model with strong long-horizon coding, agent workflows, and benchmark performance.

Limitations

Smaller context and lower capability than newer flagships, with heavier integration and compute demands.

Claude

Claude Fable 5

Strengths

Mythos-class model with 1M context and top-tier long-horizon reasoning and coding.

Limitations

Strict safety controls may reroute tasks, while pricing and access remain premium and proprietary.

Claude

Claude Opus 4.8

Strengths

Mature high-end model for reliable advanced reasoning, coding, and enterprise work.

Limitations

Below Fable 5 on the hardest tasks and remains a higher-cost proprietary option.

Claude

Claude Sonnet 5

Strengths

Cost-efficient model with 1M context and strong coding, planning, and agentic capabilities.

Limitations

Less capable than frontier models on the most demanding tasks and remains closed-source.

OpenAI

GPT-5.6 Sol

Strengths

Frontier model for agentic coding, defensive cybersecurity, and deep reasoning.

Limitations

Preview access may be limited, with safeguards that can restrict some legitimate tasks.

Z.ai

GLM-5.2

Strengths

Open-weight model with 1M context and strong long-horizon coding performance.

Limitations

Text-only, with a smaller ecosystem and greater integration requirements.

How to Access Kimi K3 on Lorka AI

Try Kimi K3 in Lorka’s AI chat along with models like Sonnet 5, Qwen 3.8, and more for an effective workflow.

Select Kimi K3

Find Kimi K3 in the model dropdown in the AI chat.

Write your prompt

Type in a prompt and attach a file as well for more context.

Receive your output

Begin your workflow with Kimi and use other AI models to help you complete tasks.

Chat With Kimi K3 Today

Create your Lorka account now and start using Kimi K3 for coding, multimodal analysis, long-context reasoning, structured workflows, and more.

Kimi K3 FAQs

Access Kimi K3 online through the Kimi API or Playground. On Lorka AI, select Kimi K3 from the model menu and begin chatting without building a separate integration.