Xiaomi's new open source, agentic AI coding harness MiMo Code beats Claude Code at ultra-long, 200+ step tasks

Xiaomi’s MiMo AI team released MiMo Code V0.1.0, an MIT-licensed, terminal-native AI coding assistant based on the OpenCode agent. The company says it outperformed Claude Code on long-horizon agentic coding benchmarks and is offering limited free access to its MiMo-V2.5 model with a million-token context window.

The launch reflects growing competition around coding agents that can manage complex, multi-session development work rather than just autocomplete or short tasks. Xiaomi’s focus on persistent memory architecture highlights a key frontier for AI developer tools: retaining project context reliably across long workflows.

Xiaomi's MiMo AI team has open-sourced MiMo Code V0.1.0, a terminal-native AI coding assistant that the Chinese electronics giant says outperforms Anthropic's Claude Code on key agentic coding benchmarks, especially on long-horizon, multi-step tasks (200+ steps) — at least, according to its own internal beta release and survey of 576 developers. It's also bundling limited-time free access to MiMo-V2.5, its multimodal flagship model with a million-token context window, requiring no registration to get started.The release was announced June 10, 2026 in a post on the social network X from the official @XiaomiMiMo account, which described the tool as "more than an AI coding assistant in your terminal — it's the smartest coding partner you'll ever work with."MiMo Code is available now on GitHub under an MIT license, and installs with a single terminal command (curl -fsSL https://mimo.xiaomi.com/install | bash) on macOS and Linux or via npm (npm install -g @mimo-ai/cli) on Windows. The project is a fork of the open-source OpenCode agent, which Xiaomi has extended with its own memory architecture, workflow modes, and model harness.The end of AI coding agents' amnesia?As any avid vibe coder would surely attest, AI coding agents degrade over long working sessions: as the context window fills, earlier decisions, conventions, and task state get compacted away or lost entirely, forcing developers to re-explain their projects.Xiaomi argues this approach is doomed at scale. "What we need is not better compression, but an explicit storage-and-retrieval mechanism that decides what information should be written into persistent structures, and when it should be recalled," the MiMo team noted in their launch blog.MiMo Code attacks this with a cross-session memory system, powered under the hood by SQLite FTS5 full-text search, that spans four layers: project memory (a persistent MEMORY.md file), session checkpoints, scratch notes, and per-task progress logs. The note-taking is key, here: Rather than forcing the primary coding agent to pause its work to take notes, the system deploys an independent "checkpoint-writer" subagent. Think of it the primary coding agent as a construction contractor working to build a massive mansion alongside a dedicated architect, the checkpoint-writer subagent. While the main agent focuses on building out the physical structure, the subagent updates the blueprints in real time, noting decisions, issues, and the actual lay of the land as the construction project progresses. When the context window approaches its limits — the contractor gets lost in the half-built mansion — it can consult the subagent and find its place again. In the case of MiMo Code, the system simply rebuilds the environment from structured checkpoints with the relevant context, ensuring no loss of operational momentum.Two self-improvement mechanisms round out the system: a /dream command that periodically (roughly every seven days) reviews historical sessions, deduplicates them, and compresses them into long-term memory, and a "distill" function that mines past sessions for repeated workflows that can be automated, following a similar approach taken recently by OpenAI and Anthropic with their various models. Impressive performance on software engineering (SWE) benchmarksAccording to benchmark figures published in Xiaomi's technical blog post, MiMo Code paired with MiMo-V2.5-Pro outperformed Claude Code paired with Claude Sonnet 4.6 on all three evaluations tested:SWE-bench Verified: 82% vs. 79%SWE-bench Pro: 62% vs. 55%Terminal Bench 2: 73% vs. 69%The harness itself accounts for a measurable share of the gain. Running the same MiMo-V2.5-Pro model in both harnesses, MiMo Code scored 62% on SWE-bench Pro versus 57% for Claude Code, and 73% on Terminal Bench 2 versus 68% — roughly five points each, attributable purely to the agent system rather than the model.Xiaomi notably did not publish comparisons against OpenAI's Codex or Google's Gemini CLI — Claude Code is the sole named competitor throughout its materials, a telling choice of benchmark target.Independent reference points suggest why. On the official Terminal-Bench 2.0 leaderboard maintained at tbench.ai, OpenAI's Codex CLI running GPT-5.5 scores 82.2% — roughly nine points above MiMo Code's self-reported 73% — and OpenAI's own GPT-5.5 announcement claims 82.7% on the same benchmark. On SWE-Bench Pro, however, the picture flips: OpenAI reports GPT-5.5 at 58.6%, below MiMo Code + MiMo-V2.5-Pro's claimed 62%. (MiMo Code does not yet appear on either official leaderboard, and cross-comparing self-run numbers against leaderboard submissions carries the usual configuration caveats.)Perhaps more interesting than the offline benchmarks: Xiaomi says it ran a human double-blind A/B evaluation during its internal beta, covering 576 developers working in 474 real private repositories, producing 1,213 judged head-to-head p