Kimi K3

Overview

Kimi K3 is Moonshot AI’s July 2026 flagship large language model: a ~2.8-trillion-parameter Mixture-of-Experts system with a 1-million-token context window, native vision (and video) understanding, and always-on reasoning. Moonshot positions it as the first open 3T-class model—hosted first on Kimi products and the developer API, with full weights promised for community release by 27 July 2026.

K3 is not a generic chat wrapper. It is aimed at long-horizon coding, multi-hour agent sessions, large-repo navigation, tool orchestration, and end-to-end knowledge work (research reports, spreadsheets, interactive dashboards). Product surfaces include the consumer app at kimi.com, Kimi Work desktop, Kimi Code terminal agents, and the OpenAI-compatible API on platform.kimi.ai (model id kimi-k3; OpenRouter moonshotai/kimi-k3).

Moonshot’s own materials are explicit that overall quality still trails the strongest proprietary systems of the same window (Claude Fable 5 and GPT-5.6 Sol), while matching or beating many peers on coding and agent-style tasks—especially when paired with a strong harness such as Kimi Code. Independent trackers (Artificial Analysis, Arena frontend coding, Vals-style real-world indices) broadly place K3 in the frontier tier rather than as a cheap mid-tier open model.

Key features

• 2.8T sparse MoE at “3T-class” scale — Largest open-scale model Moonshot has shipped. Sparse expert routing (16/896 active) keeps activation cost far below dense 2.8T; still not a laptop model for full weights.
• 1,048,576-token context — Flat per-token pricing (no context-length tiers). Built for whole-repo, multi-document, and multi-day agent trajectories in one window.
• Always-on thinking — K3 always reasons. Configure via top-level reasoning_effort (at launch: max only; lower efforts promised later). Do not use K2.x’s older thinking parameter.
• Native multimodal — Image and video inputs as content parts (base64 or ms://file-id). Strong “vision in the loop” story for frontend, game, CAD, and screenshot-driven debugging.
• Agent-first tool stack — Function calling, tool_choice (including required), dynamic tool loading mid-conversation, structured JSON Schema output, partial mode, streaming with separate reasoning_content and final content deltas.
• Automatic context caching — No manual cache IDs. Stable long prefixes (system prompts, repos, tool defs) get cache-hit input pricing; Moonshot claims high hit rates on coding workloads with their Mooncake disaggregated inference stack.
• First-party harnesses — Kimi Code CLI (MCP/ACP, multi-file engineering), Kimi Work (widgets, dashboards, deep research-style jobs), plus documented integrations for Claude Code, Codex CLI, OpenCode, OpenClaw, Hermes Agent, and Cline via OpenRouter.
• Open-weights trajectory — Hosted launch mid-July 2026; weights targeted by 27 July 2026 with a fuller technical report. Prior K2.x models already live on Hugging Face under the Moonshot org.

Pricing

Two bills matter: API tokens (primary for builders) and consumer memberships (app credits on kimi.com). Figures below are official or widely listed list prices around the mid-July 2026 launch window—re-check live pages before budgeting production.

API token pricing (kimi-k3)

Component	Price (per 1M tokens)	Notes
Input — cache hit	$0.30	Automatic; keep long prefix stable
Input — cache miss	$3.00	OpenRouter list matches $3 input
Output	$15.00	Includes reasoning/thinking tokens
Context window	1,048,576	No length-based tier uplift

OpenRouter lists moonshotai/kimi-k3 at about $3 / $15 in/out with cache-aware blended input near ~$0.50 when hits are strong. Compared with earlier open Chinese models (often well under $1/$4), K3 is expensive—closer to frontier closed pricing—reflecting its scale and agent workload.

Sibling models for cost control

Model	Typical use	Ballpark API (list)	Context
kimi-k3	Hardest coding / knowledge agents	$0.30 hit / $3 miss / $15 out	~1M
kimi-k2.7-code (+ highspeed)	Volume coding, faster loops	roughly ~$0.95–$1.90 in / ~$4–$8 out class	~256k
kimi-k2.6	General agent, open weights available	lower than K3; free routes on some gateways	~256k

Consumer memberships

Kimi’s consumer plans (music-tempo names such as Adagio free through higher paid tiers roughly in the ~$19–$199/mo band depending on region and promotions) gate agent credits for Goal, Swarm, Deep Research, and Code in the app. Heavy multi-agent use usually migrates to pure API once credit burn becomes the bottleneck. Enterprise packaging (org accounts, member management, data separation) is sold separately via the membership/enterprise path on kimi.com.

Limits & gotchas

• Thinking history sensitivity — Trained with preserved thinking history. Stripping CoT mid-session, or switching to K3 mid-thread from another model, can make outputs unstable. Prefer harnesses Moonshot lists as verified (e.g. Kimi Code).
• reasoning_effort = max only at launch — Expect higher latency and token burn until low/high effort knobs ship.
• Fixed sampling knobs — Docs fix temperature 1.0, top_p 0.95, n=1, presence/frequency penalty 0; omit them rather than override.
• Vision URL rules — Public image URLs are not supported; use base64 or uploaded ms:// file ids. Content must be an array of parts, not a stringified blob.
• Web search Formula tool — Explicitly under update at K3 launch; Moonshot warns against production reliance near term.
• Harness dependence — Lab tables mix KimiCode, Claude Code, and Codex harnesses. Bare Chat Completions often underperforms first-party agent loops on multi-file SWE.
• Excessive proactiveness — Moonshot admits K3 may over-improvise on ambiguous tasks; constrain with system prompts / AGENTS.md.
• UX gap vs top closed models — Even where benches look strong, Moonshot notes a noticeable user-experience gap versus Fable 5 and GPT-5.6 Sol.
• Self-host reality — 2.8T MoE with recommended large high-bandwidth accelerator domains (Moonshot talks 64+ accelerator supernodes for efficient serving). Open weights ≠ easy local run.
• Capacity & rate limits — Launch week saw tight capacity on some gateways; RPM/TPM scale with spend. Plan retries/backoff for production agents.
• Vendor / compliance — Beijing-based lab; data residency, export controls, and enterprise DPA review still apply for regulated buyers even with open weights later.

Community sentiment

Hacker News treated the official “Open Frontier Intelligence” post as a major story (thousands of points and a large comment tree within days). Themes: genuine scale milestone for open models; skepticism that self-reported agent benches transfer outside Kimi’s harness; practical questions about $3/$15 pricing for a Chinese open-weight line; and “almost no one will run this locally” jokes that double as real infra analysis.

r/LocalLLaMA flooded with benchmark screenshots (one early benchmarks post cleared ~1,000+ upvotes), MoE sparsity math (16 of 896 experts), Arena frontend wins, Artificial Analysis placements, and counterposts warning that a few hours of first-party API access is not a settled Fable replacement. Practitioners repeatedly separate model IQ from agent harness.

r/kimi, r/CLine, coding-agent communities focused on access: OpenRouter model id, membership credit burn, Cline wiring moonshotai/kimi-k3 within a day, and “when do weights land so we can quantize / distill.”

Independent reviewers (e.g. Simon Willison’s July 2026 notes) framed K3 as a serious frontier open release: competitive tables vs Opus 4.8 / GPT-5.5 class, still behind Fable 5 / Sol on overall lab claims, with frontend Arena leadership as a concrete public signal. Press (AP, BBC, Reuters, CNBC, Fortune, VentureBeat) emphasized geopolitical and market impact—another open-weight shock after DeepSeek-era cost pressure—more than day-two product polish.

Who should use it

• Agentic coding teams that already run long terminal loops and want OpenAI-compatible APIs plus a first-party CLI (Kimi Code) with MCP/ACP.
• Frontend / product engineers who care about screenshot-driven UI iteration; public Arena-style frontend leadership is the standout signal.
• Research / knowledge agents that need 1M context, multi-tool research, and interactive report generation in Kimi Work–style workflows.
• Cost-aware multi-model stacks that route easy jobs to K2.6 / K2.7 Code (or DeepSeek / Qwen peers) and reserve K3 for the hardest trajectories.
• Labs and infra teams preparing for open weights (quantization, distillation, vLLM-style serving) who need a top-of-frontier open base when weights drop.

Who should pause: teams that need cheapest possible tokens, strict low-latency chat, locked-down Western enterprise procurement today, or consumer laptop offline inference. Also teams that cannot preserve thinking history in their harness.

Alternatives

• Claude (Anthropic) — Strongest closed coding/agent UX for many teams; choose when compliance, polish, and Claude Code ecosystem outweigh open weights.
• ChatGPT / GPT models (OpenAI) — Default closed frontier; Sol-class models still lead overall Moonshot comparisons in Moonshot’s own tables.
• DeepSeek — Open-weight / cheap API peer for bulk coding and reasoning; smaller total scale than K3 but often better $/token for routine work.
• Qwen — Strong open Chinese alternative family for local/regionally hosted stacks and aggressive price/performance.
• Gemini — Google’s multimodal long-context alternative with deep Google Cloud integration.
• Grok — xAI frontier chat/API option when you want US-hosted frontier without Anthropic/OpenAI lock-in.
• Kimi (platform) — Broader Kimi product stack (app, memberships, K2.x line) if you care about the ecosystem rather than only the K3 model id.
• Cursor / Cline / Claude Code — Harnesses, not models: often decide real productivity more than raw leaderboard scores.

Verdict

Kimi K3 is a real frontier open-scale release—not marketing vapor. The combination of 2.8T MoE scale, 1M context, native multimodal, and agent-oriented API features puts Moonshot in the same conversation as top closed labs for coding and long-horizon work, at list prices that no longer look like “China discount” tokens.

Treat lab leaderboards as directional, not destiny: preserve thinking history, use a strong harness, watch cache hit rates, and expect high output burn on max reasoning. Until weights are public and third-party serving matures, most teams will experience K3 as a hosted API product with tight launch capacity—not as free local intelligence. For hardest agent jobs in mid-2026, K3 belongs on the shortlist; for everyday coding volume, keep K2.x or cheaper open peers in the router.

Bottom line: Use Kimi K3 when you need open-frontier capability on long agent trajectories and can pay ~frontier rates; use cheaper Kimi/DeepSeek/Qwen models for bulk; verify your harness before trusting any single leaderboard screenshot.