Kimi

Overview

Kimi is Moonshot AI’s product stack: the consumer app at kimi.com, developer API on platform.kimi.ai, desktop Kimi Work, and the open terminal agent line (Kimi Code CLI / Kimi CLI). In mid-July 2026 Moonshot shipped the headline model of the year for open-scale labs: Kimi K3 — a ~2.8-trillion-parameter Mixture-of-Experts system with a 1-million-token context window and native vision, positioned for long-horizon coding, tool loops, and knowledge-work agents.

K3 is not a lone chat model. It sits on top of an already-shipping agent surface (Goal, Swarm, Deep Research, Code CLI with MCP/ACP) and an OpenAI-compatible API that integrators already use via OpenRouter, Cline, Cursor-class gateways, and custom SDKs. Sibling models remain important for cost control: K2.6 (open generalist, 256k) and K2.7 Code (coding-focused, optional high-speed tier).

Key features

• Kimi K3 (flagship, ~16 Jul 2026) — 2.8T MoE with Stable LatentMoE routing (16/896 experts), KDA + Attention Residuals, ~1,048,576-token context, multimodal (image/video understanding). Hosted model id typically kimi-k3 / OpenRouter moonshotai/kimi-k3. Reasoning effort at launch is effectively max-only.
• Long-horizon coding agents — Built for multi-hour terminal sessions, large-repo navigation, thousands of tool calls, and “vision in the loop” (screenshots for UI/frontend/game/CAD iteration). First-party harness: Kimi Code.
• K2.6 / K2.7 Code — Cheaper 256k-class models for volume coding; K2.6 established Moonshot’s open-weight agent reputation earlier in 2026 before K3’s scale jump.
• OpenAI-compatible API — Chat Completions-style endpoints, streaming with separate reasoning/thinking channels, tools, structured output, automatic context caching (cache-hit pricing is first-class, not a side feature).
• Official server tools (“Formula”) — Web search, code runner, fetch-to-markdown, spreadsheet helpers, memory, etc. Note: Moonshot has flagged web-search tooling as mid-update around the K3 window — treat as non-stable for production.
• Product surfaces — Consumer agents (Goal / Swarm / Deep Research), Kimi Work desktop, Kimi Code CLI (shell, MCP, ACP for Zed/JetBrains, VS Code), Agent SDK for embedding the runtime.
• Open weights trajectory — K2.6 weights are part of the open story; K3 launched hosted first with open weights promised shortly after (Moonshot target around 27 Jul 2026). Self-host planning should assume heavy infra (Moonshot talks supernode-scale serving).

Pricing

Two surfaces: consumer memberships (app credits) and API tokens (platform / OpenRouter). Figures below are list prices circulating at K3 launch week (mid-July 2026) — always re-check the live pricing pages.

Consumer memberships (kimi.com)

Plan	Monthly	Notes
Adagio (Free)	$0	Limited free usage; try chat/agents
Moderato	~$19/mo	Entry paid; chat + research/agent quotas
Allegretto	~$39/mo	Higher agent/code credits; common power-user tier
Allegro	~$99/mo	Premium agent mode
Vivace	~$199/mo	Top consumer tier; heavy multi-agent use

Annual billing discounts apply on higher tiers. Heavy Swarm / Deep Research / Code usage burns credits faster than chat-only use — many builders eventually move agent load to pure API.

API token pricing (per 1M tokens, list)

Model	Input (cache hit)	Input (cache miss)	Output	Context
kimi-k3	~$0.30	~$3.00	~$15.00	~1,048,576
kimi-k2.7-code	~$0.19	~$0.95	~$4.00	~262,144
kimi-k2.7-code-highspeed	~$0.38	~$1.90	~$8.00	~262,144
kimi-k2.6	~$0.16	~$0.95	~$4.00	~262,144

OpenRouter lists K3 at $3 / $15 per 1M in/out with 1M context (launch week). Cache-hit input pricing is the real lever for agent loops that resend large repos/history. K3 bills thinking/reasoning tokens as output — max-effort multi-hour runs can get expensive even when input is mostly cached.

Limits & gotchas

• Harness sensitivity — Self-reported coding/agent benches depend on KimiCode / Claude Code / Codex harnesses. Raw Chat Completions often underperform the first-party CLI on multi-file work.
• Thinking history — Dropping reasoning traces mid-session can destabilize K3; keep the full loop.
• reasoning_effort = max only — At launch, finer effort knobs were not fully shipping; expect higher latency and output token burn.
• Web search Formula tool — Flagged as under update near K3 launch; don’t hard-depend for production search agents yet.
• Vision input format — Prefer base64 / file ids over random public URLs; follow platform docs for multimodal parts arrays.
• Self-host cost — Even after weights land, 2.8T MoE is not a laptop model. Moonshot’s own materials push large accelerator domains for efficient serving.
• Benchmark theater — Moonshot publishes competitive tables vs other frontier systems. Treat lab leaderboards as marketing until you re-run your own harness. Community threads on r/LocalLLaMA mixed celebration with “wait for third-party verification.”
• Vendor / compliance — Chinese lab; data residency, export controls, and enterprise DPAs matter for regulated buyers.

Community sentiment

r/LocalLLaMA lit up around K3 benchmark posts (threads with 1,000+ upvotes): excitement about open 3T-class scale and long context, plus skepticism until independent harness numbers settle and weights actually land for local/infra experiments.

r/kimi and agent communities (Cline, coding-agent Discords) focused on practical access: OpenRouter / platform model ids, membership credit burn on Allegretto, and “does K3 beat K2.6 enough to justify 4× output price.” Cline publicly wired moonshotai/kimi-k3 within a day of launch.

Press (Reuters, CNBC, Fortune, AP) framed K3 as another China open-weight shock after DeepSeek-era pricing pressure — “largest open model” headlines, competitive claims vs US closed labs, market chatter about inference cost. Independent reviewers (e.g. Simon Willison’s July 2026 notes) treated it as a serious frontier open release with the usual caveats about harness and UX polish.

Who should use it

• Agentic coding teams running long terminal loops who want OpenAI-compatible APIs plus a first-party CLI.
• Cost-sensitive builders who need 256k–1M context and heavy tool-call volume — use K2.x for bulk, K3 for the hardest jobs.
• Open-weight / multi-vendor shops planning self-host or failover after weights ship (infra heavy for K3).
• Bilingual CN/EN knowledge work using Swarm / Deep Research / Work surfaces in the consumer app.
• Integrators already on OpenRouter, Cline, or custom OpenAI SDKs who can swap model ids quickly.

Skip (or pilot carefully) if you need the most polished closed-model UX today, strict US-only residency without enterprise terms, or production web-search tooling that is currently in flux.

Alternatives

• Claude / Claude Code — Stronger polished agent UX and safety defaults; usually higher $ for comparable long sessions.
• ChatGPT — Broadest product ecosystem; weaker open-weight path.
• DeepSeek — Rival Chinese open-frontier lab; different product surface than Kimi Work/Code.
• Gemini — Google multimodal + Workspace/Cloud procurement path.
• Cursor — IDE product that can route to Kimi or rivals when you want editor UX first.
• Mistral — European open/commercial option for some compliance buyers.
• See also: Kimi vs DeepSeek, Kimi vs Claude, Kimi vs ChatGPT.

Verdict

As of mid-July 2026, Kimi K3 is a real shipping release, not a vapor roadmap. The headline numbers are clear: ~2.8T MoE, 1M context, multimodal, hosted API at roughly $3 / $15 per MTok (with much cheaper cache hits), and a surprisingly complete agent product around the model. K2.6/K2.7 still matter for unit economics; K3 is the sledgehammer for the hardest long-horizon jobs.

Honest caveats: capacity and 429s at launch, harness-dependent quality, always-on max reasoning token burn, web-search tooling in flux, and self-host reality that is infra-heavy even after weights drop. For teams already comfortable with Chinese open labs and OpenAI-compatible agents — and who measure success in multi-hour coding sessions rather than single-turn chat polish — Kimi belongs on the shortlist next to Claude Code, Codex-class stacks, and DeepSeek. Start on kimi-k2.7-code or kimi-k2.6 for volume; reserve kimi-k3 where the 1M context and frontier agent loop actually pay for themselves.