Xiaomi MiMo

Artificial Analysis reports that four new models this week added 11 points on the Intelligence Index vs. cost-per-task Pareto frontier. GPT-6 Luna contributed 5 points, Claude Opus 5.5 contributed 4, and the remaining two came from MiMo-V2.6-Pro and GPT-6 Sol. The post doesn't disclose specific scores, pricing, or latency—hold off on conclusions until full benchmarks drop.

Xiaomi open-sourced two multimodal models: MiMo-V2.6 Pro and Flash. Pro scored 46 on the Artificial Analysis Intelligence Index—the highest among open-source models—and matches Claude Opus 5 and GPT-5.6 Sol on most agent benchmarks. The post doesn't disclose parameter counts, training cost, inference latency, or the exact open-source license, so I'd hold off on production assumptions for now.

Xiaomi released the MiMo-V2.6 series. The Pro version ranks #1 among open weights models on the Artificial Analysis Intelligence Index with a score of 46, at a training cost of $3M. A Flash variant targets efficiency, and an UltraSpeed variant offers 20x faster output. The technical report details RL scaling across three axes: larger batches and throughput, richer multi-task environments, and more grader compute. Code and training recipes are open-sourced, but the 7k+ task datasets are not yet released. Former DeepSeek engineer Fuli Luo, now at Xiaomi, previously live-streamed the training runs.

Artificial Analysis ranks Xiaomi's MiMo-V2.6-Pro #1 out of 114 models with a score of 46. It's a 1T total / 42B active parameter open-weight model with text, image, speech, and video input. Output speed is 125 tokens/sec, but it's verbose—generating 140M tokens during evaluation. Pricing: $0.43/M input, $0.87/M output; the full eval cost $206.66.

Xiaomi released two omni-modal models: MiMo-V2.6-Pro and Flash. The Pro version scored 1628 on Code Arena WebDev, up 153 points from MiMo-V2.5-Pro's 1475, landing around 10th overall and ~3rd among open-weight models under MIT license. The post doesn't disclose Flash's benchmark numbers or parameter counts.

Xiaomi released MiMo-V2.6-Pro, scoring 46 on the Artificial Analysis Intelligence Index—up from 26 for the previous V2.5-Pro. It's now the highest among open-weight models. The post doesn't disclose parameter count, architecture details, or a release timeline.

Xiaomi MiMo dropped two fully multimodal open-source models. The Pro version matches Claude Opus 5 and GPT-5.6 Sol on most agent benchmarks and scores 46 on the Artificial Analysis Intelligence Index—the highest among open-source models so far. Capabilities span coding, computer use, 3D reasoning, and creative tasks. The post doesn't disclose parameter counts, training details, or where Flash sits in the lineup, so I'd hold off on direct comparisons for now.

Xiaomi MiMo-V2.6-Pro scored 46 on the Artificial Analysis Intelligence Index, the highest among open-weight models. The previous MiMo-V2.5-Pro scored 26. The post doesn't disclose model size, training data, or release license, so hold for details.

Anthropic's September threat-intel report calls out DeepSeek, Xiaomi, and Moonshot for piping user-model conversations into Claude and using Claude's replies as training data for distillation. The exchanges reportedly contained sensitive info from individual users, multinationals, and state-affiliated actors. Anthropic says this violates PRC law and suggests sharing detailed findings with China's Ministry of Public Security via the FBI. The post doesn't disclose the volume of conversations or the time range involved.

SimbaStack open-sourced Sentinel under MIT, a QA agent that reads the codebase first, derives business flows on its own, then tests them end-to-end across frontend and backend. They pointed it at their own hotel PMS with only the repo and admin credentials, no test plan. Sentinel read the code, concluded it was a boutique hotel system, and auto-derived nine critical flows including the full reservation lifecycle, group bookings, and night audit. It ran the top two flows twice each and caught three bugs invisible to UI-only checks: a backend NO_AVAILABILITY error on a reservation that already held the room, a calendar showing a room as available when the API said it was booked, and a check-in returning 200 but leaving the guest registration status unchanged. The pipeline: a deterministic grep/find recon pass extracts code structure, Xiaomi's Mimo model derives business flows, Playwright drives the browser, and an api_request tool checks server state. Each flow runs twice by default, findings are unioned, and a 90-call cap bounds each attempt. A final vision pass scores visual hierarchy, spacing, and contrast on visited screens. It currently supports common JS stacks like Next.js, Express, Fastify, and Prisma; other stacks need a recon patch.

Xiaomi's MiMo Claw is a cloud-hosted agent product that runs without local setup. The official release ships with MiMo-V2.5-Pro, natively adapted to the OpenClaw framework and MCP protocol. Xiaomi claims roughly 3× inference throughput improvement in agent workflow tests. It integrates with WPS Office for online document generation, preview, and editing. Free tier gets 4 hours per day; paid plans start at ¥14.9/month. On ClawEval, task pass rate reaches 63.8%, with 40–60% lower token consumption than comparable products.

Xiaomi released MiMo Claw, a lightweight cloud Claw product powered by the MiMo-V2.5-Pro flagship model. It natively supports the MCP tool-calling protocol, handles over a thousand consecutive tool calls per session, and has a million-token context window. The MTP three-layer decoding architecture roughly triples throughput in standard OpenClaw agent workflows. On ClawEval it hit a 63.8% task pass rate while cutting token consumption by 40–60% versus peers. It integrates with Kingsoft Office for online creation and editing of Word, Excel, PPT, and PDF files. Free daily session time jumps from 1 to 4 hours, and a new TokenPlan tiered subscription starts at ¥14.9/month.

Xiaomi released MiMo Code on GitHub, an open-source terminal coding agent built on OpenCode. It tackles long-horizon context limits by using a separate writer subagent that periodically writes structured checkpoints early, well before the context window fills up. When the window nears its limit, the system rebuilds working context from those checkpoints instead of relying on increasingly unreliable summarization. Background processes also extract reusable patterns from past sessions. The post does not disclose which model powers it, nor latency or cost figures.

Xiaomi's MiMo V2.5 is live, claiming 1000–3000 tps via DFlash and Persistent Kernel. The DFlash model weights are out, and an open-source release is promised soon. The post body is blocked by Reddit security, so only the headline is available—no details on measured latency, concurrency, or hardware. I'd discount that tps figure: headline peaks usually assume optimal batching, and real single-user throughput is likely lower.

Xiaomi released MiMo Code V0.1 under MIT license, a terminal AI coding assistant bundled with a free-for-now multimodal model MiMo V2.5 that supports a 1M-token context window. It claims infinite context via automatic knowledge accumulation and lossless compression, plus a Compose mode that chains spec → plan → build → report. The agent and model collaborate in a test-review-verify loop. Voice input runs on MiMo-V2.5-ASR. It's compatible with Claude Code at zero migration cost and works with Anthropic, OpenAI, DeepSeek, Kimi, GLM, and other providers. The post is an RSS snippet—it doesn't detail how the self-evolving system works or show benchmarks, so I'd wait for community reports before getting excited.

Xiaomi open-sourced MiMo Code V0.1.0 under MIT license. The built-in MiMo-V2.5 multimodal model is free for a limited time and claims performance on par with Claude Sonnet 4.6; it also supports DeepSeek, Kimi, and GLM. Two standout features: a persistent memory system (project memory, session checkpoints, task progress) to avoid forgetting in long sessions, and a Compose mode for model-agent collaboration that hits 62% on SWE-Bench Pro (Claude Code scored 57%) and 73% on Terminal Bench 2. The post doesn't disclose how long the free period lasts or MiMo-V2.5's parameter count. Type `mimo` in the terminal to start; the UI is fully localized in Chinese.

Xiaomi MiMo and TileRT released MiMo-V2.5-Pro-UltraSpeed, a 1T-parameter model mode exceeding 1,000 tokens/s, with API access open from June 9 to June 23, 2026, at 3× the MiMo-V2.5-Pro price and about 10× the speed.

Xiaomi MiMo claims MiMo-V2.5-Pro UltraSpeed runs a 1T-parameter MoE model above 1,000 output tokens per second on one standard 8-GPU node; the post does not disclose the GPU model, batch settings, or reproducible configuration.

The title says Xiaomi MiMo-v2.5-Pro-UltraSpeed is a 1T model running at 1,000 tokens per second; the RSS body only provides the URL, Hacker News comments link, 66 points, and 14 comments, and the post does not disclose hardware, precision, context window, benchmark setup, or availability.

Xiaomi MiMo and TileRT_AI released MiMo-V2.5-Pro-UltraSpeed, running a 1T MoE model above 1,000 tokens/s on a single standard 8-GPGPU node, with UltraSpeed API priced at 3x and applications open from June 8 to 23 PDT.