Xiaomi MiMo

AI at Xiaomi: MiMo model releases and open weights, on-device and in-car AI.

34 picksRelated topicsQwen On-device AI Open source

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May 29Friday

AI HOT (Curated Pool)May 29, 17:13

Xiaomi Open-Sources Controllable Video Foley Model ControlFoley

Xiaomi’s large model application team open-sourced ControlFoley, a controllable video Foley model supporting three tasks: text-guided video dubbing, text-controlled video dubbing, and reference-audio-controlled video dubbing, with code, model weights, and an online demo released.

Why it matters: ControlFoley clears HKR-H/K/R with controllable video Foley plus code, weights, and demo. It is a useful multimodal-audio release from Xiaomi, but not a flagship foundation-model launch, so it sits near the featured threshold.

May 27Wednesday

AI HOT (Curated Pool)May 27, 03:21

MiMo 2.5 Pro Gets Major Price Cut, Matching DeepSeek V4 Pro

Xiaomi permanently cut MiMo-V2.5 API prices by up to 99%, matched DeepSeek V4 Pro pricing, increased same-price token allowances by 5–8x, reset existing user quotas in full, and set the new pricing to take effect on May 26.

Why it matters: HKR-H/K/R all pass: the 99% cut creates a price-war hook, the post gives 5-8x token economics, and API cost pressure resonates. It remains a pricing update, not a model or capability release, so it stays below the 78+ band.

Take

Xiaomi’s 99% MiMo-V2.5 API price cut is a survival move in the inference price war, not a capability flex. The hard facts are brutal: MiMo-V2.5 now matches DeepSeek V4 Pro pricing, same-price token allowances rise 5–8x, and existing user quotas get fully reset. That is Xiaomi buying back developer attention before usage habits harden elsewhere.

I don’t fully buy the “full-stack inference optimization” explanation yet. The snippet says a technical blog will come later, but gives no throughput, latency, GPU utilization, or per-token cost data. DeepSeek has already trained the China API market to treat cheap inference as table stakes. Xiaomi can match price, but MiMo still has to prove a reason to be called inside phones, cars, and audio workflows instead of just being another discounted endpoint.

May 24Sunday

Synced · WeChatMay 24, 15:00

ICML 2026: First Parallel Thinking Framework for Vision-Language Models

Visual Para-Thinker introduces a parallel thinking framework for vision-language models, using Pa-Attention and LPRoPE to isolate four visual reasoning paths and training on 163,000 question-answer pairs.

Why it matters: HKR-H/K/R pass: the ICML 2026 paper offers a concrete parallel-thinking mechanism, four isolated paths, and 163K training pairs. It remains a single research release without broad replication or product impact, so it fits 78–84.

Take

Visual Para-Thinker’s useful part is the mechanism, not the “parallel thinking” branding. It isolates four visual reasoning paths with Pa-Attention, keeps shared position ranges unbiased, then adds LPRoPE so paths stay distinguishable. The training set is also concrete: 163,000 QA pairs distilled mainly from Qwen3-VL-235B-A22B-Instruct.

That targets a real VLM failure mode. Long CoT often dilutes attention over visual tokens, which shows up as hallucination rather than better reasoning. The reported gains are nontrivial: +12.6 / +6.3 on V* for 3B / 7B, and +6.1 / +5.0 on HallusionBench. I don’t buy the “first framework” framing, since K2.5, Step3-VL, and LongCat-Flash-Thinking already explored reasoning width. This reads more like a clean VLM-specific patch; the open question is whether it holds outside curated perception benchmarks.

May 14Thursday

AI HOT (Curated Pool)May 14, 13:33

MiMo V2.5 Pro Places Third on DesignArena

MiMo V2.5 Pro placed third on the DesignArena overall leaderboard; its Thinking version rose 8 spots over MiMo-V2.5 and matched Claude Sonnet 4.6 performance on frontend coding tasks.

Why it matters: HKR-H/K/R all pass, but the facts come from one official X post with no methodology, access, or pricing. This fits a mid-weight benchmark/product update, not a same-day must-write.

Take

Xiaomi’s strongest claim here is not the No. 3 DesignArena rank; it is MiMo V2.5 Pro matching Claude Sonnet 4.6 on frontend coding. DesignArena is closer to UI generation and shippable frontend work than math-heavy leaderboards, so this tests taste, structure, and executable code. The Thinking version also rose 8 places over MiMo-V2.5, which says Xiaomi is iterating beyond a one-off checkpoint.

The gap is big: the snippet gives no sample size, task mix, voting method, or multi-turn repair setup. Frontend benchmarks can reward component templates, Tailwind habits, and benchmark-specific styling. Sonnet’s value in coding is boring reliability across the whole work loop, not one leaderboard slice. If Xiaomi wants practitioners to care, the next proof is API pricing, IDE integration, and long-task consistency.

May 13Wednesday

r/LocalLLaMAMay 13, 16:31

The Trillion-Parameter Dilemma: MiMo-V2.5-Pro Open-Sourced at 1.02T Parameters

Xiaomi open-sourced MiMo-V2.5-Pro with 1.02T parameters, 42B active parameters, a 1M context window, and an MIT license; the author ran 125 Claude Code sessions through the API, spending $70.12 for 387,380,436 tokens with a 96.3% cache hit rate.

Why it matters: HKR-H/K/R all pass: a Xiaomi 1.02T open model plus a concrete Claude Code API cost experiment. Reddit sourcing keeps it at the low end of the 85+ band, but the domestic flagship-model signal clears p1.

May 8Friday

AI HOT (Curated Pool)May 8, 11:06

China releases L1-L4 AI terminal intelligence standards covering 7 device categories

MIIT and other agencies released AI terminal intelligence standards covering 7 device categories. The framework uses a “2+N” structure with L1 response, L2 tool, L3 assistance, and L4 collaboration; L4 details come later. The post does not disclose concrete test metrics.

Why it matters: HKR-H/K/R pass: the story has a clear L1-L4 standards hook, concrete “2+N” and 7-category details, and compliance impact for device AI teams. Missing L4 rules and test metrics keep it near the featured floor.

Take

This standard wins control over the label before it proves control over capability. GB/Z 177—2026 covers seven device classes: phones, PCs, TVs, glasses, car cockpits, speakers, and earbuds. It defines L1 response, L2 tool, L3 assistance, and L4 collaboration. But the full text is not yet public on the national standards platform, and L4 rules are explicitly left for later revision.

That gap matters because “AI phone” and “AI PC” already became launch-event wallpaper for Xiaomi, Honor, Lenovo, Huawei, and others. If the tests do not specify reproducible tasks, offline behavior, tool-call success rates, latency, privacy boundaries, and failure handling, L3 becomes a sales badge. The listed drafters are mostly device makers, which is practical but also tells you where the pressure sits: make the ladder strict enough to sound official, loose enough for products to climb it.

May 5Tuesday

r/LocalLLaMAMay 5, 14:51

DeepSeek V4 Pro matches GPT-5.2 on FoodTruck Bench, 10 weeks later and about 17x cheaper

DeepSeek V4 Pro ranked No. 4 on FoodTruck Bench. The 30-day agentic benchmark uses 34 tools, persistent memory, and daily reflection; its median is within 3% of GPT-5.2 at about 17x lower workload cost. Xiaomi MiMo v2.5 Pro also ranked No. 6, with 5/5 survival, 1,019% median ROI, and $2.41 per run.

Why it matters: HKR-H/K/R all pass: the cost gap is clickable, and the post gives a 30-day, 34-tool setup plus a 17× cost delta. Single-source Reddit benchmark with no cross-validation keeps it in the 78–84 band.

May 1Friday

r/LocalLLaMAMay 1, 20:34

MiMo-V2.5-Pro: the actual best open-weights model

Reddit user cjami benchmarked Xiaomi MiMo-V2.5-Pro in autonomous Blood on the Clocktower games. It scored 88% as Good and 48% as Evil, with 183,639 output tokens per game, $0.99 cost, and a 0.4% tool-call error rate. The key comparison is Kimi K2.6: 580,000 tokens, $2.65, and 10–15 hours per game.

Why it matters: Single Reddit benchmark limits authority, so this is not a model-release story. HKR-H/K/R all pass via a named test with win rates, token counts, cost, and tool-error data, placing it in the 78–84 featured band.

Apr 28Tuesday

QbitAI · WeChatApr 28, 20:56

Xiaomi open-sources MiMo-V2.5 series; Pro builds a macOS-like desktop in 4 hours

Xiaomi open-sourced MiMo-V2.5 weights, covering Pro Agent, multimodal base, TTS, and ASR models. MiMo-V2.5-Pro built a 54-app macOS-like desktop in 4 hours without human takeover; it scored 233/233 on SysY with 672 tool calls in 4.3 hours. Key details for practitioners are the 1M context, 100T-token program, and free Agent-framework access.

Why it matters: HKR-H/K/R all pass: Xiaomi open-sourced MiMo-V2.5 weights with concrete agent and coding-task numbers. Domestic flagship model release bump puts it in the must-write same-day band.

Take

Xiaomi open-sourced MiMo-V2.5 weights with 1M context, a 100T-token program, and a 4.3-hour SysY run. My read is split: the engineering demos are stronger than a routine Chinese model launch, but the article jumps from demos to “top global model table” far too quickly.

The concrete claims are not small. MiMo-V2.5 includes MiMo-V2.5-Pro, a multimodal base model, TTS, and ASR. MiMo-V2.5-Pro reportedly built a macOS-like desktop in 4 hours without human takeover. The demo had 54 apps, 68 components, React 18, TypeScript, Zustand, Tailwind CSS, and Vite. It also completed the Peking University SysY compiler task in 4.3 hours, using 672 tool calls, and scored 233/233. The article says ClawEval used about 70K tokens per trajectory for a 64% Pass³ rate, while Claude Opus 4.6, Gemini 3.1 Pro, and GPT-5.4 used 120K to 180K tokens. If that holds under outside replication, it matters. Agent cost is not just model pricing; it is tool loops, retries, context growth, and failed trajectories.

The gaps are equally large. The article does not disclose parameter count, MoE layout, training-token count, RL recipe, tool sandbox, sampling settings, failed runs, average pass@k, or repository history for the macOS demo. A 54-app desktop sounds flashy, but static app shells and maintainable software are different artifacts. The claim that the XcodeApp includes a “real browsable web engine” needs inspection. Is that an iframe wrapper, a browser API simulation, or an actual browser implementation? The body does not say. For practitioners, that difference moves the demo from a weekend build to a serious systems result.

The SysY result is the strongest number in the piece. Compiler tasks test long-horizon consistency better than front-end demos. Lexing, parsing, IR generation, RISC-V backend work, and optimization all interfere with each other. A model surviving 672 tool calls without derailing suggests Xiaomi trained or engineered around state management and error recovery. I would compare this to SWE-bench Verified-style agent runs: plenty of models write a good patch for one issue, then start overwriting themselves after 20 to 50 steps when tools, files, and context stack up. If MiMo-V2.5-Pro is reliably stable past 600 turns, that is not copywriting. That is model policy plus agent runtime doing real work.

I care even more about the 70K-token efficiency claim. Agent token reduction usually comes from one of three places: better compressed planning, more aggressive context trimming, or a tool-result summarizer. The first is genuinely valuable. The second hides failure modes. The third depends heavily on the surrounding framework. The article only says “token efficiency.” It does not say whether this comes from MiMo-V2.5-Pro itself or from Xiaomi’s agent framework compressing trajectories. Xiaomi also announced free access for emerging agent frameworks, which makes benchmarking messier. Are we measuring the open model, or a product stack with policy, memory, and tool routing layered around it?

Externally, Xiaomi’s move feels closer to Qwen and DeepSeek distribution playbooks than a pure Claude/GPT benchmark chase. Qwen gained developer mindshare through open weights, many model sizes, and tool coverage. DeepSeek won trust with cheap API economics and reproducible inference cost. Moonshot, Zhipu, and MiniMax have leaned more on products and APIs. Xiaomi has a different asset base: phones, cars, IoT, voice, and OS-level distribution. Shipping TTS, ASR, a multimodal base, and an agent model together makes sense for Xiaomi. The company wants models that can sit inside phones, vehicles, home devices, and developer workflows, not just collect GitHub stars.

The audio claims need caution. The article says MiMo-V2.5-TTS supports text-described voices and zero-shot cloning without reference audio. It says ASR reaches Chinese-English SOTA and handles Cantonese, Sichuanese, Wu, and Minnan. But the “99.999%” recognition number comes from one Cantonese test, with no disclosed duration, noise condition, accent spread, labeling standard, or dataset. ASR is already crowded: Whisper, SenseVoice, Paraformer, and FunASR have made clean speech less impressive. The hard cases are overlapping speakers, far-field audio, music beds, mixed dialects, and low-bitrate telephone speech. The article gives experience notes, not a proper evaluation.

The 100T-token giveaway is the sharpest commercial move. It gives agent builders a reason to test a new base. OpenAI and Anthropic have strong developer habits. Qwen already owns a lot of open-source inertia. A later entrant needs three things at once: good enough quality, low enough cost, and low migration friction. Xiaomi cut Pro credits from 4x to 2x, standard from 2x to 1x, priced 1M and 256K context at the same credit multiplier, and added a 20% night discount from 00:00 to 08:00 Beijing time. Those details matter more than the “global top-tier” language. Agent developers follow invoices faster than slogans.

My biggest problem is the source layer. This is a media experience piece, not a system card and not an independent benchmark report. It mentions Claude Opus 4.6, GPT-5.4, and Gemini 3.1 Pro, but gives no table, benchmark version, evaluator, confidence interval, or reproduction package. The title says the weights are open-sourced, but the body does not disclose the license. Commercial use, acceptable-use limits, redistribution terms, and training-data posture matter more for enterprise adoption than a demo video. Open weights with a restrictive license are downloadable, not freely deployable.

So my stance is simple. MiMo-V2.5 is Xiaomi’s first AI release that looks like a complete platform bet: agent, long context, voice, open weights, and developer subsidy in one package. The article pushes every claim to maximum volume, which makes me wait for third-party replication. I need three things before upgrading my view: public SysY and ClawEval scripts, the full macOS-demo repository trajectory, and clear license terms. If those hold up, Xiaomi becomes a serious player in the Chinese open agent stack. For now, I accept the release has force. I do not yet seat it beside Claude and GPT.

Hacker News front pageApr 28, 20:16

Xiaomi releases MiMo-v2.5 weights with strong coding and agent benchmarks

Xiaomi released MiMo-v2.5 family weights; the title cites strong coding and agent benchmarks. The RSS body only lists URLs, 13 HN points and 2 comments; the post does not disclose size, license, or scores.

Why it matters: HKR-H/K/R pass because a Xiaomi coding/agent weights release is concrete and practitioner-relevant. Sparse sourcing holds it near the featured floor: no parameters, license, or benchmark numbers are disclosed.

Take

MiMo-V2.5-Pro has the right headline and a thin evidence trail. The article confirms an April 23, 2026 release, open weights, and a coding focus; the feed shows only 13 Hacker News points and 2 comments. It gives no parameter count, license, context length, SWE-bench score, agent benchmark setup, or evaluation harness. Using “right next to Claude Opus 4.6” as the hook borrows frontier-model credibility without reproducible conditions.

Xiaomi does have a plausible reason to ship this: phones, IoT, and cars all need local coding-ish agents and tool use. But the open-model bar is now set by DeepSeek, Qwen, and Llama releases with weights plus usable evals and commercial terms. A weight drop alone is a teaser; the missing license and benchmark recipe decide whether practitioners can use it.

X · @op7418Apr 28, 10:42

Xiaomi open-sources the MiMo-V2.5 model series

Xiaomi open-sourced the MiMo-V2.5 model series under the MIT license for commercial use, retraining, and fine-tuning. It also launched Orbit 100T Token, offering approved AI builders up to 1.6B credits worth 659 yuan. Agent framework teams can apply for free MiMo token access; the post does not disclose model size or benchmark results.

Why it matters: HKR-H/K/R all pass: Xiaomi MiMo-V2.5 open source, MIT terms, and Orbit 100T credits matter to builders. Missing params and benchmarks keep it in the 78–84 band, below P1.

Take

Xiaomi open-sourced the full MiMo-V2.5 series under the MIT license. That is the clearest fact here. MIT allows commercial use, retraining, and fine-tuning without extra permission. For a phone, EV, and IoT company, this reads less like a pure model launch and more like a developer funnel. The post does not disclose parameter counts, context length, API pricing, SWE-bench, Aider, LiveCodeBench, or Chinese evals. The title says the full series is open-sourced; the body does not show the technical receipts.

My read is blunt: Xiaomi is not proving that MiMo-V2.5 beats DeepSeek, Qwen, or Kimi on capability. It is lowering adoption friction first. The Orbit 100T Token plan offers approved AI builders up to 1.6B credits, listed at 659 yuan. That number is catchy, but the body does not define the credit-to-token conversion, input-output split, rate limits, eligible models, or expiry rules. Without those conditions, 1.6B credits only tells us Xiaomi is willing to subsidize usage. It does not tell us whether the serving economics work.

Qwen is the obvious comparison. Alibaba’s open model strategy worked because it paired permissive licensing with parameter tiers, quantized variants, inference integrations, public benchmarks, and a steady community loop. DeepSeek-R1 hit because reasoning behavior, low API pricing, open weights, and distilled models arrived together. Xiaomi’s disclosed package is thinner. It gives the ecosystem an opening, but not enough evidence for a serious team to route production traffic. Agent framework teams care about latency stability, function-calling reliability, long-context cost, concurrency limits, and failure modes. A large token pool does not answer those questions.

Honestly, the agent ecosystem angle is the part I take more seriously. Xiaomi’s advantage is not model-lab prestige. It is distribution across phones, cars, home devices, and HyperOS. If MiMo becomes the model layer for device control, in-car assistants, household automation, and cross-device workflows, Xiaomi has a deployment surface that many standalone model companies lack. But the post leaves the key facts blank. It does not say whether MiMo-V2.5 supports tool use, structured output, multimodal inputs, on-device variants, or multiple model sizes. Without that, the agent story is a recruitment pitch, not an architecture claim.

I do not buy “fully open-source” as a sufficient signal in 2026. The bar has moved. A permissive license without a model card, reproducible evals, inference recipes, and clear API pricing will not move careful builders off their current stack. Xiaomi made a useful move, but the useful part is channel plus subsidy, not demonstrated model superiority. Once it publishes weights, evals, serving terms, and integration details, MiMo-V2.5 can be compared on the same board as Qwen and DeepSeek. Right now, Xiaomi opened the door. It has not shown what is inside.

Apr 8Wednesday

QbitAI · WeChatApr 8, 12:00

Xiaomi unveils two AI audio frameworks: Any2Speech and Midasheng-audio-generate

Xiaomi's large-model application team introduced Xiaomi Any2Speech and Midasheng-audio-generate. Any2Speech generates up to about 10 minutes per inference, while the other model turns one text prompt into mixed audio with speech, music, and ambient sound. The post names GST labeling, dual-path planning with dimension dropout, Flow Matching, and five-field structured labels; benchmark scores, training scale, and commercial terms are not disclosed.

Why it matters: Xiaomi released two audio-generation frameworks with a clear hook and concrete mechanisms, so HKR-H and HKR-K pass. HKR-R is weaker because benchmark results, training data scale, open-source status, and commercial terms are not disclosed, so this sits at the low end of featured.

Take

Xiaomi’s strongest move here is treating audio as a directed scene, not a prettier TTS clip. Any2Speech claims up to about 10 minutes per inference, while Midasheng-audio-generate turns one prompt into mixed speech, music, and ambient sound. The named mechanics—GST labeling, dual-path splitting with dimension dropout, Flow Matching, and five-field structured labels—sound like control plumbing, not just another voice-style demo.

I’m holding back on the platform narrative. The captured article body is only a WeChat CAPTCHA page, and the useful numbers are absent: benchmarks, training scale, pricing, licensing, API terms. Against ElevenLabs or Suno, Xiaomi’s credible edge is distribution across phones, cars, and IoT speakers. Without latency, rights boundaries, and deployment terms, this reads like a serious research release that has not yet crossed into product pressure.

Apr 6Monday

X · @doteyApr 6, 02:08

Xiaomi MiMo lead Luo Fuli on token costs in the Agent era

Luo Fuli said Agent workloads can resend 100k+ tokens across repeated tool calls, and global compute cannot keep up with that burn. She said OpenClaw makes several times more requests than Claude Code and can push real API cost to tens of times the subscription price; the post does not disclose a pricing formula.

Why it matters: A named Xiaomi MiMo lead makes a concrete, testable critique of agent cost: 100k+ token context replay, multi-tool-call overhead, and several-times request inflation vs Claude Code. HKR-H/K/R all pass, but missing public benchmark setup and pricing keeps it at the low end of the

Feb 26Thursday

New York Times ChineseFeb 26, 16:07

Where Is the U.S. Losing to China in AI?

The piece argues China has embedded AI into manufacturing, with 30,000+ smart factories, and over half of all industrial robots installed globally in 2024 going to Chinese plants. It cites shop-floor data: Zeekr's Ningbo plant uses 800+ robots, Xiaomi says its Beijing factory produces one car every 76 seconds, while only 18% of U.S. manufacturers report a formal AI strategy and two-thirds struggle to scale pilots. The real point is not frontier models but AI deployment in factory automation, scheduling, and inspection.

Why it matters: Data-backed commentary with all three HKR axes: a strong US-vs-China hook, concrete factory metrics, and direct resonance on AI deployment and competitiveness. Not a new product, model, or research release, so it stays in the low featured band.

Take

U.S. AI weakness here is shop-floor organization, not model research. The numbers bite: China has 30,000-plus smart factories, and more than half of new industrial robots installed globally in 2024 went into Chinese plants. Zeekr’s Ningbo factory runs 800-plus robots; Xiaomi says its Beijing plant ships a car every 76 seconds. On the U.S. side, only 18% of manufacturers report a formal AI strategy, and two-thirds struggle to move pilots into production. Honestly, the LLM world keeps pitching enterprise agents, while factories already run the less glamorous version: vision inspection, scheduling, and predictive maintenance. America has OpenAI, Anthropic, and Nvidia. China has sensors, robots, MES systems, and local-government money tied into production lines. The NYT piece has an opinion frame, but these deployment numbers are hard to wave away.