Kimi K2.6: プロダクション級エージェンティックコーディング
Kimi K2.6は本番環境向けのエージェンティックコーディングモデルです。12時間の自律実行、300エージェントスウォーム協調、フルスタック生成に対応。SWE-Bench Pro 58.6%、Terminal-Bench 2.0 66.7%を達成。
兆パラメータのK2 MoEバックボーンに262Kトークンコンテキストと自動圧縮を搭載。Anthropic API互換で、Kimi.com、API、Kimi Code CLIから利用可能。Vercel、Factory.ai、CodeBuddyによるパートナー検証済み。
Kimi K2.6 体験
強力なAIアシスタントをすぐに試してみてください
Kimi K2.6が正式リリースされました！🎉 最大12時間の連続実行、300のサブエージェント協調、フルスタックコードベースのエンドツーエンド処理が可能です。何を作りたいですか？
ベンチマークでの優秀な性能
Kimi K2.6はコーディング、推論、数学のベンチマークで本番レベルの結果を達成
エージェンティック機能
ツール連携による自律的問題解決
高性能
最先端の推論とコーディング
Mixture-of-Experts
384エキスパート、32Bアクティベートパラメータ
Version paths
Compare K2.6 with the latest Kimi Code pages
Use these pages to move from K2.6 context into K2.7, Kimi Code, and status updates.
Kimi K3
Flagship hub: 2.8T specs, 1M context, API id kimi-k3, pricing, and when to switch.
ContinueKimi K2.7 Code
Current release overview, capabilities, and practical context for K2.7 Code.
ContinueKimi Code
Setup paths, API notes, and developer workflow guidance for Kimi Code.
ContinueKimi K3 Status
K3 availability tracker with official Kimi Code model docs and plan notes.
ContinueKimi K2.6 の主な機能
12時間自律実行、300エージェントスウォーム、フルスタック生成を実現するプロダクション級エージェンティックコーディング能力。
Kimi K2.6 とは？
Kimi K2.6 is MoonshotAI's production agentic coding model — the first in the K2 series designed for 12-hour autonomous runs and 300-agent swarm coordination. It keeps the trillion-parameter MoE backbone while adding a new execution layer purpose-built for long-horizon engineering tasks.
Kimi K2.6 について
Kimi K2.6 is the general-availability release of MoonshotAI's agentic coding model, shipped April 21 2026 after an eight-day preview. It is built on the same trillion-parameter Mixture-of-Experts backbone as the original K2 (1T total / 32B active / 384 experts, MLA attention, SwiGLU, MuonClip training) but adds a production execution layer optimized for sustained autonomous operation.
The headline capability is duration and coordination: K2.6 can hold a coding task together for twelve hours and 4,000 coordinated steps across up to 300 sub-agents in a single swarm. Its 262K token context window — paired with automatic compression that summarizes and elides history as sessions grow — means a mid-sized monorepo plus its test output fits in context without truncation-induced drift at hour nine.
Three reference deployments shipped with the GA release: a Zig-based inference runtime reaching 193 tokens/sec, a 185% throughput improvement on the exchange-core financial matching engine, and full-stack Next.js generation validated by Vercel at >50% improvement on their internal benchmark. K2.6 is available on Kimi.com, the official API, and the Kimi Code CLI.
K2.6 Technical Specs
- • 262K token context with auto-compression
- • 300 sub-agents per swarm, 4,000+ step coordination
- • SWE-Bench Pro 58.6% / Terminal-Bench 2.0 66.7%
- • MathVision 93.2% (with Python tool use)
- • Anthropic API compatible, Apache 2.0 base
K2.6 Use Cases
- • Long-horizon autonomous coding (12h+ runs)
- • Full-stack generation: UI → auth → database
- • Performance engineering on unfamiliar codebases
- • Multi-agent swarm orchestration (up to 300 agents)
- • Systems programming (Zig, Rust, low-level runtimes)
開発者によるK2.6の評価
エンジニアリングチームが長期間のエージェンティックコーディングタスクにK2.6を本番環境で使用した体験を共有。
"We ran K2.6 against our internal Next.js benchmark and saw over 50% improvement versus K2.5. It handles App Router, Server Components, and the surrounding ecosystem without hallucinating APIs — that gap has been open for a long time."
"K2.6 improved 15% on both our evaluated benchmarks. The swarm orchestration is the real unlock — decomposing a large refactor across 50 workers and reconciling the outputs coherently is something we haven't seen from any other model at this scale."
"12% better code generation accuracy and 18% better long-context stability versus K2.5. For our users doing multi-file refactors, the stability improvement is the one that actually matters — fewer sessions that drift off-track at step 200."
"Deployed Qwen3.5-0.8B locally in Zig using K2.6. It picked Zig without prompting — a language with a tiny training corpus — and still produced a working low-level runtime at 193 tokens/sec. That's the frontier I care about."
"Handed K2.6 the exchange-core matching engine and asked for throughput improvements. It read the Java codebase, identified hot paths, and rewrote them correctly — 185% median throughput, no broken invariants. I reviewed the plan, not the diffs."
"The design-to-code capability is genuinely new. I gave it a Figma export and a database schema; it generated the animated UI, wired up auth, and connected the database. What used to be a three-day sprint is now a three-hour K2.6 run."
"K2.6 is the first model where "give it to the agent overnight" stopped being aspirational. We handed it a 60k-line Java codebase, asked it to find and fix throughput bottlenecks, and woke up to a 185% improvement with no regressions. That's not a demo — that's production."
Kimi K2.6 よくある質問
Kimi K2.6の機能、ベンチマーク、始め方に関する一般的な質問への回答。
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K2 base model (Apache 2.0): HuggingFace • GitHub • API Documentation