[AINews] Xiaomi MiMo-V2.6-Pro 1T-A42B: the new top Open Weights model, trained for $3M

來源原標題

[AINews] Xiaomi MiMo-V2.6-Pro 1T-A42B: the new top Open Weights model, trained for $3M

作者

Latent Space

原始標籤

blog

原始連結

https://www.latent.space/p/ainews-xiaomi-mimo-v26-pro-1t-a42b

摘要原文

Meet Xiaomi and other top Chinese frontier labs at AIE Shanghai ! This is a first for the “Apple of China” phone maker-turned-frontier lab: “ The MiMo-V2.6 series includes two natively omnimodal models: MiMo-V2.6-Pro is our most capable model to date, while MiMo-V2.6-Flash strikes the best balance between intelligence, efficiency, and cost. We are also rolling-out MiMo-V2.6-Pro-UltraSpeed, delivering up to 20x faster output speed at the same quality, for users who require extreme generation speed.” Xiaomi is not traditionally considered one of the six Chinese AI Tigers , so it is very surprising to the established order of names you have come to know and love. And… it is natively omnimodal! Xiaomi made news a few days ago when Fuli Luo, a former DeepSeek star engineer now at Xiaomi , started publishing their final RL training runs live, which showed an abnormal amount of transparency in their internal metrics . As they note in their technical report , they scaled RL compute along three axes: Larger batches and higher throughput : large batches on a fully asynchronous architecture, with 1,568 samples per update, training at up to 1M context length, and 3.5 to 3.7B tokens per step. More tasks and richer environments : a multi-task training suite spanning coding, general agents, visual and cyber, mixed across several harnesses so that gains in one capability reinforce the others. More grader compute : relative comparison within each group gives long-horizon RL tasks more precise and more diverse reward signals, closes a self-improvement loop, and steers the model toward shorter paths and fewer tokens per task. ALL of this tooling, including the environments, will be open sourced.- the environment code and training recipes , but the complete 7k+ task datasets have not yet been released. Coding / software engineering: Code recipes, dataset loader and rewards Cyber / vulnerability reproduction: ARVO environment and training recipe General / knowledge work: General environment, tools and training recipe Visual / web development: Web-development environment and grading Music generation: Data preparation and music scorer Composable mini-harnesses: Agent configurations Shared environment adapters: mimoagent environments AI News for 9/19/2026-9/21/2026. We checked 12 subreddits, 544 Twitters and no further Discords. AINews’ website lets you search all past issues. As a reminder, AINews is now a section of Latent Space . You can opt in/out of email frequencies! Open Models, Competition, and the China Gap Open models remain the central policy and market story : Nathan Lambert shared a congressional briefing on open-model performance, adoption, and U.S.-China competition, followed by a public summary . The broader argument resurfaced elsewhere: @Yuchenj_UW claims frontier coding capability has plateaued since Opus 4.8 , while open-source models keep closing the gap at 10–50x lower cost ; @ClementDelangue similarly argues APIs are overkill for many real-world use cases and that specialized models will take share. Counterpoint: @teortaxesTex argues frontier has actually split into new higher tiers, with internal models and top closed models still well ahead. The release cadence from Chinese labs is now difficult to dismiss : @Thom_Wolf compiled an unusually dense ~10-week run of open releases including Kimi K3, Qwen3.8-Max, DeepSeek V4-Pro, GLM-5.3, Hy4 Preview, Atria Dawn , and more. This is reinforced by a Bloomberg-sourced note via @Polymarket that startups are increasingly building custom models on open weights to cut cost and reduce dependence on OpenAI/Anthropic. The subtext across several tweets: open-weight capability is no longer confined to midsized models; multiple teams are shipping frontier-scale MoEs with credible cost-performance stories. Xiaomi MiMo-V2.6 and RL as the New Scaling Lever MiMo-V2.6 is the biggest open-model release in the set : @XiaomiMiMo launched MiMo-V2.6 Pro and Flash , described as open omnimodal models with weights, technical report, RL environments, and training code. Artificial Analysis says MiMo-V2.6-Pro debuts as the top open-weights model on its Intelligence Index (46) , with 1.02T total / 42B active parameters and strong cost efficiency at $0.435/M input and $0.87/M output tokens. @victormustar notes the models are under MIT license . What stood out technically was not just the model, but the RL stack : @eliebakouch highlighted Xiaomi’s environment/data-factory paper for generating RL tasks from open repositories with “agents in the loop” for robustness and anti-cheating. Later commentary points to a second paper and unusually high transparency: @xeophon notes Xiaomi wants to release ~7K RL environments , and @eliebakouch emphasizes the team shipped model + tech report less than a week after the final RL run . A recurring interpretation, from @bertgodel and @Thom_Wolf , is that high-quality open RL environments may now be as strategically important as pretraining corpora were in the last cycle. RL cost/throughput details drew attention because they compress timelines : @zephyr_z9 cites 130 hours , 75B tokens , and $2.6M for the RL run behind the result; @tianjun_zhang says the MiMo family scales RL on JAX + TPU , where scaling is “mostly a config change, not a code rewrite.” If these numbers hold up, the implication is that post-training/RL is becoming a far cheaper route to frontier-adjacent gains than many assumed. Decision Models, Jev, and the Return of Specialized Inference Jev was the dominant product/theme discussion : Multiple posts converged on the same framing: this is “just” classification/routing, but with modern model intelligence and much better latency/cost. @karpathy calls it a point on the Pareto frontier for “no thinking, single token, low latency acceptable intelligence” . @willdepue describes it as a zero-shot classifier with frontier-ish intelligence , while @ClementDelangue argues the excitement shows there is large latent demand for specialized models rather than ever-larger generalists. The ecosystem around Jev expanded quickly : @sarah_edo built a Chrome extension that uses Jev to select and fill relevant WebMCP tools per keystroke. LangChain added Jev-as-a-judge to LangSmith; @hwchase17 and @Hacubu pushed SemIf , an open-source decision model, through the LangSmith Gateway. @omarsar0 reports using Jev to retag ~2.3K papers in 83 seconds for $0.14 , with 579 high-confidence changes and manual validation of disagreements. The more durable takeaway is architectural : DSPyOSS argues that asking frontier agents is like managing people, while hand-writing decision-model programs is analogous to writing assembly; both extremes are useful, but brittle if overused. Several posts emphasized where these models fit best: routing, approval gates, trace scoring, tool selection, discrete document decisions, and low-cost supervision inside larger agent loops rather than as standalone “smart agents.” Inference, Tooling, and Systems Optimizations Tokenizer and post-training infra both got substantive upgrades : Hugging Face’s tokenizers v1 RC claims up to 30x faster tokenization , improved multithread scaling, lower memory use, and much smaller package size; @art_zucker framed it as a new SOTA tokenization library. Separately, Halo launched as a post-training framework claiming up to 2.8x throughput over stock TRL while keeping models in native Hugging Face format. Inference-side engineering remains a major lever : @RisingSayak showed how KV caching is incorporated into QwenImage 2.1 , separating fixed context from changing image positions and yielding a 2.55x speedup ; the thread cites 50.57s → 19.86s DiT time on a warmed A100 with moderate memory overhead. vLLM published tuned serving configs for Qwen3.8-2.4T on GB300 NVL72 , showing a Pareto frontier from 5K total tok/s/GPU at high throughput to 180 output tok/s/user at low latency.