Kimi K2.8 Preview puts near-K3 coding in every membership tier
Moonshot spent the week arguing about revenue and the weekend shipping a model that undercuts its own flagship. Meanwhile the person who built Redis quietly made DeepSeek's newest MoE run on a desktop Mac.
Moonshot AI put Kimi K2.8 Preview live across Kimi Code and Kimi Work on September 11, claiming overall performance close to flagship K3 with further gains in coding and agentic work, and a 1M-token context window open to every membership tier. The pricing gap is the point: K3 needs the ¥99-per-month plan just to be selected, and its million-token window sits behind the ¥199 Allegretto tier, while K2.8 Preview is available on all five plans with the full 1M. The swap is invisible — the existing kimi-for-coding model ID now resolves to K2.8 Preview, and K3 requests with thinking disabled get routed to the preview's non-thinking variant. It carries K3's three reasoning-effort levels (low, high, max) and takes image and video input.
Why now: Moonshot's own K3 documentation concedes the flagship is fragile outside its sweet spot — sensitive to missing prior thinking context in an agent loop, and prone to over-reaching on vague tasks. A cheaper, more predictable workhorse is what high-volume code completion and everyday editing actually need. It also fits the commercial pressure: Moonshot reportedly crossed $1 billion in annualized revenue in August, up from roughly $300 million in June, and is targeting $2 billion by year-end while pursuing a Hong Kong listing at a $50 billion pre-IPO valuation — about 167 times revenue, against roughly 20 for Anthropic and 40 for OpenAI. We covered the filing last week — Moonshot AI files for Hong Kong IPO at $50B — days after Anthropic calls it out. Volume is the lever it needs: K3's series already generates around 300 billion tokens a day.
A 366 GB GGUF of DeepSeek V4.1 Flash is now on Hugging Face from Salvatore Sanfilippo, the Redis author, and it is built to run off solid-state storage rather than fit in memory. The quants back his ds4 inference engine, a from-scratch Metal, CUDA and ROCm runtime for DeepSeek's Flash and Pro models that ships an SSD-streaming mode — his own benchmark sweep runs a Q2 build on an M5 Max with 128 GB of RAM, and the docs point first-time users at a 96 or 128 GB machine. The Q2 file is up; the Q4 was still uploading as r/LocalLLaMA noticed. The relevant part for anyone running models locally is not the download size but what it implies: a frontier-class MoE whose active-parameter sparsity lets a single desktop stream expert weights from disk and still hold useful token rates. The model itself landed last week — DeepSeek V4.1 Flash ships — and retires V4 Pro on Sunday.
What to watch: whether K2.8 Preview graduates out of "Preview" with published weights, the way K3 did — Moonshot's flagship story has always leaned on open weights for distribution.
If your agent stack ran on one 128 GB desktop instead of a cloud API, what would you still pay for per token? Tell us in the comments.
Sources: antirez/ds4 (GitHub)
