--- title: "Two Chinese large models (大模型) just reset what \"good enough\" costs" date: 2026-09-23 category: Foundation Models site: NeuroAI canonical: https://neuroai.site/a/na-model-deepseek-kimi-efficient language: en --- # Two Chinese large models (大模型) just reset what "good enough" costs > In September 2026, DeepSeek released V4.1 Flash — a 552B open-weight model with a 1M-token context — and Kimi shipped K2.8 Preview, a near-flagship closed model. Two opposite strategies, one signal: China's large model (大模型) race is now about efficiency, not just scale. Ten days felt like a year. One lab gave away a 552-billion-parameter model for free. Another quietly shipped a model almost as good as its flagship and hid the price inside a subscription. Same country, same week of September, opposite playbooks. The Chinese large model (大模型) field is no longer just chasing the frontier. It is arguing about what most users actually need — and pricing the answer aggressively. ## DeepSeek V4.1 Flash: open weights, agent-first architecture On **September 10, 2026**, DeepSeek made **V4.1 Flash** generally available. The headline number — **552 billion total parameters** — is almost a distraction. The point is how few of them run per token. DeepSeek describes the model as a **causal encoder–decoder mixture-of-experts**: roughly **8 billion parameters activate to read your input**, and about **16 billion to write the output**. That asymmetry is deliberate. Agent workloads spend most of their tokens re-reading context, not generating, so DeepSeek made the "reading" half cheap and the "writing" half the only part that costs much. The model also carries a **1-million-token context** and reads **images as well as text** natively — the separate vision variant is gone. Weights are published on Hugging Face under an **MIT license**, so anyone can self-host, fine-tune, or ship it commercially. ## The memory trick that makes 1M tokens affordable Long context is useless if it bankrupts you in cache. DeepSeek says V4.1 Flash holds its key-value cache at about **890 bytes per token** — roughly **one-quarter the HBM and one-eighth the SSD** of the previous Flash generation. The model card frames it plainly: a 1M-token window now fits in under a gigabyte of cache, which is what makes serving it at scale plausible. ## DeepSeek is retiring its own flagship to push this one The most telling move is what DeepSeek did to **V4-Pro**. From **September 14, 2026**, API requests to deepseek-v4-pro are routed to V4.1 Flash at Flash prices until a V4.1-Pro arrives. In other words, DeepSeek is using a cheaper, smaller-activation model to replace the product it was charging a premium for — a pricing decision dressed as a research drop. The new rate card (per 1 million tokens, off-peak) runs **$0.15 input / $0.003 cache-hit / $0.60 output**; peak hours roughly double the input and output rates. Independent benchmarking from Artificial Analysis puts V4.1 Flash at an **Intelligence Index of about 39–40** under maximum-effort reasoning — strong for an open-weight model of its size, if notably verbose. ## Kimi K2.8 Preview: flagship performance, closed door One day later, on **September 11, 2026**, Moonshot AI fully rolled out **Kimi K2.8 Preview** inside Kimi Code. The model ID did not change (kimi-for-coding), so existing tools and clients needed no reconfiguration. Moonshot positions K2.8 as **close to its flagship K3** on overall quality, with **more efficient thinking than K2.7 Code**. It adds **adjustable thinking effort** — low, high, or max, defaulting to max — matching the K3 series, and opens a **1-million-token context to every membership tier**. It also takes **image and video input**, not just text. Here is the strategic twist: unlike K2.7 Code and K3, which shipped as open weights, **K2.8 Preview is closed** — available only through Moonshot's own API and apps. Moonshot has not fully explained the reversal, but the timing fits a model the company describes as "flagship-adjacent at a lower tier." For a lab that built its reputation on open releases, closing the mid-tier is a signal that the valuable model is now the one you rent, not the one you download. ## Two strategies, one conclusion Put the two side by side and a pattern emerges: - **DeepSeek** bets that the winning move is **open, efficient, and cheap to serve** — give the weights away, win on inference cost and the agent use case. - **Kimi** bets that the winning move is **capturing the best experience behind a paywall** — keep the strong model closed, monetize through subscriptions and API. Both agree the old contest — "who has the biggest model" — is over. The new one is "who gives users enough model for the lowest total cost," where cost includes latency, context length, and whether you can host it yourself. ## Why this matters outside China Open weights mean V4.1 Flash can be downloaded and run anywhere, so its efficiency gains are not locked to one cloud. A clinic in São Paulo, a startup in Lagos, or a research group in Berlin can fine-tune it without sending data to a foreign server. That is a different distribution model from closed US frontier models, and it changes who gets to build on top of strong AI. Kimi's move matters for the opposite reason: it shows that even open-weight champions are experimenting with closing the tiers that matter most commercially. The "open" label in Chinese AI is becoming conditional, not absolute. ## Honest limitations - **Parameter and architecture details** for DeepSeek V4.1 Flash come from DeepSeek's official release notes and model card, plus independent coverage; the "196B Engram memory" figure appears in secondary write-ups but not in the primary blog we reviewed, so we omitted it. - **Kimi K2.8 Preview's parameter count is undisclosed** by Moonshot; we describe only what the company published (context, modalities, thinking tiers, positioning). - The **Intelligence Index of ~39–40** is Artificial Analysis's own metric (v4.3), not a vendor claim, and reflects only the reasoning variant under max effort. - **"Close to K3"** is Moonshot's qualitative positioning; we found no head-to-head public benchmark comparing K2.8 Preview to K3 on identical tasks. - Both releases are **very recent (September 10–11, 2026)**; real-world latency, cost, and quality at scale are still being measured by users, not yet by long-run studies. ## What readers can do now - **If you run agentic or long-context workloads**, test DeepSeek V4.1 Flash's open weights on your own hardware — the 890-byte/token cache and MIT license make a self-hosted 1M-context assistant realistic for the first time at this price. - **If you are a Kimi Code user**, switch daily coding to K2.8 Preview and reserve K3 for the hardest engineering tasks; the thinking-effort dial lets you trade latency for quality per call. - **If you compare models for procurement**, stop ranking by parameter count — rank by cost-per-task at your real context lengths, where both of these releases change the math. --- Published by NeuroAI (https://neuroai.site/) — https://neuroai.site/a/na-model-deepseek-kimi-efficient Free to quote with attribution and a link to the original.