Testing Moonshot AI's Kimi K3 Inside Claude Code - philippdubach.comSkip to main contentphilippdubach<br>Quantitative finance, AI, and the economics underneath.

Inside Claude Code, Kimi K3 was good enough that I soon stopped thinking about which model was behind the interface.<br>The model felt slower than its measured output speed suggests, but it produced a close frontend reconstruction from a detailed prompt and asset pack.<br>My exported run cost $7.18. The same recorded token mix would have cost about $11.96 on Claude Opus 4.8 and $23.92 on Claude Fable 5 at published list prices.<br>The larger opportunity may be sovereign AI: open weights hosted on local infrastructure and sold on residency, control and auditability rather than on tokens alone.

×Kimi K3 is Moonshot&rsquo;s new 2.8-trillion-parameter model, with native vision and a one-million-token context window. Moonshot calls it the first open model at this scale and says the weights will be released by July 27. Its own launch post is also unusually candid: it says K3 still trails Fable 5 and GPT-5.6 Sol overall, and that its user experience is not yet at the same level.<br>While I was writing this, Alibaba announced Qwen 3.8, another very large model headed for an open-weight release. This is also shortly after Thinking Machines Lab released Inkling, a 975-billion-parameter US model with 41 billion active parameters and full weights available. Kimi is not a one-off anymore. There is now a queue.<br>I have burned millions of tokens through Claude Code by now. My config works, the tools are where I expect them to be, and I know rhythm of the harness.<br>So I kept Claude Code and using OpenRouter, I routed the requests to Kimi K3.<br>First impression: slow<br>It felt slow. Very slow, at first. Which is odd, because the measured numbers say otherwise. Artificial Analysis reports about 62 output tokens per second for Kimi K3 and 57 for Claude Opus 4.8. It also measures a much shorter time to first token for K3.<br>Maybe the difference was time spent reasoning before useful text appeared. Maybe it was the cadence of the stream. Maybe I have simply used Claude Code for long enough that anything with a different rhythm feels wrong. (Or maybe it was just because I was going through openrouter.)<br>Then, somewhere during the first hour, I stopped noticing. Not because K3 suddenly became faster. It handled files, edits and tool calls well enough that I forgot where the traffic was going.<br>A quick frontend test<br>K3 has been getting attention for frontend work (ranked #1 on Code Arena right now), so I wanted something visual rather than another coding benchmark. I reused the prompt and assets from this Lafys build. I assume the reference was made with Fable 5, although I cannot verify that.

×I know his is one run, with very specific instructions and the original assets. Give the model a vaguer prompt and the result may fall apart.<br>Cost<br>The OpenRouter export for this run contains 115 requests, 13.64 million prompt tokens and 82,307 output tokens. Of the prompt tokens, 12.95 million were cache hits. Total cost: $7.18.<br>Applying Anthropic&rsquo;s published list prices to that exact traffic mix gives about $11.96 for Claude Opus 4.8 and $23.92 for Claude Fable 5. In this run, then, Kimi came out roughly 40% cheaper than Opus and 70% cheaper than Fable.<br>Is it an Opus replacement?<br>No. Not for me.<br>Opus still feels more dependable when the work gets difficult. It is better at knowing when to stop, better at handling ambiguity, and less likely to make an energetic decision I did not ask for. Moonshot itself lists excessive proactiveness and harness sensitivity among K3&rsquo;s limitations, which matches parts of my experience.<br>I would not call K3 a Fable 5-class model either.<br>But below Fable and Opus the air gets thin very quickly. K3 is close enough that many users will not care about the remaining difference, or will care less than they care about the price.<br>So where do we go from here?<br>The takes over the past few days have gone in every direction.<br>Kimi K3 is the end of the American model moat. Kimi K3 is benchmark theatre. Open weights make closed models obsolete. Open weights are irrelevant if running the thing costs a small data centre. China is deliberately commoditising the layer on which a large part of the US market is now betting. Or there is no master plan and Chinese labs are releasing good models because good models bring developers, usage and prestige.<br>A less dramatic list (from my notes) would look like this:<br>$/token is a bad measure when models use very different numbers of tokens.<br>Public benchmarks are easier to optimise for and worse at separating the top models.<br>Open weights put a ceiling on API prices even if most users never self-host.<br>The model is becoming replaceable; the harness, data and workflow may not be.<br>Value moves up into applications and down into chips, power and inference infrastructure.<br>Open weights help with control and privacy, but &ldquo;open&rdquo; does not mean...