Moonshot AI Models on AtlasCloud | Kimi
Atlas Cloud hosts the full Kimi lineup via the MoonshotAI API, from K2-Thinking for deep reasoning to K2.6 for agentic coding. All pay-as-you-go, 262K context.
Large Language Models by Moonshot AI
Power chat, reasoning, and agents at scale with leading large language models, served fast and affordably on Atlas Cloud.
Moonshot AI Models API Pricing Details
Compare standard vs. our pricing across every Moonshot AI model.
| Model | Standard Price (USD) | Our Price (USD) | Discount | |
|---|---|---|---|---|
| Kimi K3 | $3/$15per 1M tokens1048.6K context | $3/$15M in/outper 1M tokens1048.6K context | — | View |
| Kimi K2.7 Code | $0.95/$4per 1M tokens262.1K context | $0.95/$4M in/outper 1M tokens262.1K context | — | View |
| Kimi K2.6 | $0.95/$4per 1M tokens262.1K context | $0.95/$4M in/outper 1M tokens262.1K context | — | View |
| Kimi K2.5 | $0.6/$3per 1M tokens262.1K context | $0.49/$2.5M in/outper 1M tokens262.1K context | — | View |
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Moonshot AI API Use Cases You Can Build on Atlas Cloud
Kimi's agent swarm and long-horizon execution capabilities let teams run tasks that would take days of human effort in a single automated session. Teams use the M-series alongside K2-Thinking to cover everything from autonomous code changes to multi-document research at scale.
Legacy Codebase Modernization
Engineering teams use Kimi K2.6 to run long-horizon coding agents that autonomously overhaul production codebases over extended multi-hour sessions. In a documented example, K2.6 rewrote an 8-year-old financial matching engine over 13 hours and delivered a 185% throughput improvement without human intervention between commits. Atlas Cloud's pay-as-you-go pricing makes it practical to run these extended agentic sessions without capacity commitments.
Parallel Document Batch Processing
Operations teams use Kimi K2.6's 300-agent swarm to process large document batches in parallel. A single orchestration run matched one CV against 100 job roles and produced 100 fully customized resumes as output. The same pattern applies to contract review, compliance checks, and any workflow where a fixed input needs to be evaluated against a large, variable set of targets.
Deep Reasoning for Complex Analysis
Research and legal teams use Kimi K2-Thinking for multi-step analysis problems that require extended internal reasoning. The model supports up to 200 to 300 sequential tool calls per session, looping through reason-call-reason cycles without human prompting between steps. On Atlas Cloud it is priced at $0.6 per million input tokens and shares the 262K context window with the rest of the Kimi lineup.
Automated Research Paper Production
Academic and content teams use Kimi K2.6 to turn source documents into full research outputs. In a demonstrated run, K2.6 converted an astrophysics paper into a 40-page research paper, a structured dataset with over 20,000 entries, and 14 astronomy-grade charts in a single session. This reduces the turnaround on literature-to-output workflows from weeks to hours.
Business Prospecting at Scale
Growth and sales teams use Kimi K2.6 swarms to identify prospects and generate outreach assets in parallel. One example run identified 30 retail stores in a target city without websites and generated a landing page for each. The same pattern works for lead enrichment, competitive landscape mapping, and any task that combines discovery and content generation at list scale.
Visual Document and Code Analysis
Product and data teams use Kimi K2.5 and K2.6's native vision capabilities to process image and video inputs alongside text in the same API call. The MoonViT encoder handles diagrams, screenshots, UI mockups, and document scans without external preprocessing. This is useful for pipelines that convert visual specifications directly into code, or extract structured data from image-heavy documents.
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Contact SalesFrequently Asked Questions about Moonshot AI Models
Kimi K2.6 is MoonshotAI's latest open-source multimodal LLM, released in April 2026 under a Modified MIT license. It runs a Mixture-of-Experts architecture with 1 trillion total parameters and 32 billion active during inference. It is designed for agentic coding, long-horizon task execution, and multi-agent swarm orchestration.
Kimi K2.6 scales to 300 sub-agents executing up to 4,000 coordinated steps in a single run. Kimi K2.5 on Atlas Cloud supports swarm execution with up to 100 sub-agents. Tasks are dynamically decomposed into parallel, domain-specialized subtasks for fully autonomous output.
Kimi K2-Thinking uses deep chain-of-thought reasoning with up to 200 to 300 sequential tool calls per session. The model reasons, calls a tool, interprets the result, calls another tool, and continues this loop without human input. It is suited for multi-step logical inference, complex math, and problems where extended internal reasoning improves accuracy.
Yes. Kimi K2.5 and K2.6 include MoonViT, a 400-million-parameter vision encoder that processes images and video natively. You pass image or video inputs directly in the API call alongside text without external preprocessing. This supports visual analysis, document understanding, and image-to-code generation workflows.
Yes. Kimi K2.6 is released under a Modified MIT license, which permits commercial use. Open weights are available on HuggingFace for self-hosted deployments. Atlas Cloud also provides K2.6 via API for teams that prefer managed access without infrastructure overhead.
Kimi K2.6 scores 80.2% on SWE-Bench Verified and 54.0% on Humanity's Last Exam with tools, outperforming GPT-5.5 on both benchmarks. It also leads on BrowseComp at 83.2%, above GPT-5.4. These results come at roughly 80% lower cost per million tokens than GPT-5.5.
Kimi K2.5 is priced at $0.49 per million input tokens and $2.5 per million output tokens on Atlas Cloud. Kimi K2-Thinking and K2-Instruct-0905 run at $0.6 per million input tokens with the same output rate. Check the Atlas Cloud Kimi K2.6 model page for its current specific pricing.
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