Guide

How to run Kimi K3 as a coding agent, today

Moonshot shipped Kimi K3 on July 16, 2026 — a 2.8-trillion-parameter mixture-of-experts model with a one-million-token context window, priced at $3 per million input tokens and $15 per million output. If you want to know whether it's actually good at your kind of work (and not just at benchmark theater), the fastest way is to run it as a coding agent on your own repo. Here's the whole path, in about ten minutes.

Step 1: pick your door — Moonshot or OpenRouter

K3 is served two ways, and both speak the OpenAI-compatible dialect every modern CLI understands:

• OpenRouter — the pragmatic default. One key covers K3 and every other frontier model, which matters later when you want to compare. If you already keep an OpenRouter key around (we've argued it's the most leveraged key you own), you're done with step 1.
• Moonshot direct — the first-party API. Marginally shorter path to new releases and the native cache-hit pricing ($0.30 per million input on cache hits, 10x below the $3 list rate — that discount is what makes long agent loops cheap).

Step 2: wire it into a terminal agent

Claude Code won't host K3 — it's Anthropic-only. The cleanest host is OpenCode, which is provider-agnostic by design: install it, add your Moonshot or OpenRouter key as a provider, select the K3 model, and you have the same describe-edit-run loop you know from Claude Code, powered by a different brain. The whole install is a one-line script; our OpenCode guide walks through it.

Start it the way you'd start any agent you don't fully trust yet: in a repo with a clean working tree, approvals on, git diff after every run. If you keep secrets in the tree, this is also the moment to make sure they're excluded from what the agent can read.

Step 3: don't demo it — test it

A new model always looks brilliant on a toy prompt. The useful question is whether it beats your current model on your work, and there's a cheap protocol for that: five real tasks from your git history, identical context, blind scoring. Two caveats specific to K3 while independent numbers settle: Moonshot's benchmark figures (81.2 FrontierSWE, 88.3 Terminal-Bench 2.0) are self-reported so far, and its sibling K2.6 — not K3 — currently holds the reputation for the longest stable agent runs. Long-horizon behavior is exactly the thing to probe on your own tasks.

About that 1M context window: it's real, and it's a meter. Filling the window once costs about $3 of input, and an agent loop re-sends context every turn. In practice you want the cache-hit rate doing the heavy lifting, and you still want the agent working from focused context rather than "here's the whole monorepo, good luck."

Frequently asked questions

Where Backgrind fits

The moment you have two agents worth running — your Claude Code and a K3-powered OpenCode — the bottleneck stops being the models and becomes you watching terminals. Backgrind runs both in tabs in one always-on-top overlay, pings the tab that needs a decision, and stays out of the way otherwise. And when you'd rather get one answer than run two sessions, seat K3 next to Claude in the Fusion council via your OpenRouter key: every model reads the repo read-only, a chairman judges, you get pinged only on a split. New model launches stop being a migration and start being one more chair at the table.

Sources

K3 release, specs, and pricing: The AI Rankings — Kimi K3 review, BenchLM — Kimi API pricing (July 2026), and MorphLLM's K3 spec sheet. Benchmark numbers are Moonshot's self-reported figures; independent evals were still landing as we published.